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70: Discrete Math and Probability. Programming Computers
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70: Discrete Math and Probability.

Feb 12, 2022

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Page 1: 70: Discrete Math and Probability.

70: Discrete Math and Probability.

Programming Computers

≡ Superpower!

What are your super powerful programs doing?Logic and Proofs!Induction ≡ Recursion.

What can computers do?Work with discrete objects.Discrete Math =⇒ immense application.

Computers learn and interact with the world?E.g. machine learning, data analysis.Probability!

See note 1, for more discussion.

Page 2: 70: Discrete Math and Probability.

70: Discrete Math and Probability.

Programming Computers ≡ Superpower!

What are your super powerful programs doing?Logic and Proofs!Induction ≡ Recursion.

What can computers do?Work with discrete objects.Discrete Math =⇒ immense application.

Computers learn and interact with the world?E.g. machine learning, data analysis.Probability!

See note 1, for more discussion.

Page 3: 70: Discrete Math and Probability.

70: Discrete Math and Probability.

Programming Computers ≡ Superpower!

What are your super powerful programs doing?

Logic and Proofs!Induction ≡ Recursion.

What can computers do?Work with discrete objects.Discrete Math =⇒ immense application.

Computers learn and interact with the world?E.g. machine learning, data analysis.Probability!

See note 1, for more discussion.

Page 4: 70: Discrete Math and Probability.

70: Discrete Math and Probability.

Programming Computers ≡ Superpower!

What are your super powerful programs doing?Logic and Proofs!

Induction ≡ Recursion.

What can computers do?Work with discrete objects.Discrete Math =⇒ immense application.

Computers learn and interact with the world?E.g. machine learning, data analysis.Probability!

See note 1, for more discussion.

Page 5: 70: Discrete Math and Probability.

70: Discrete Math and Probability.

Programming Computers ≡ Superpower!

What are your super powerful programs doing?Logic and Proofs!Induction ≡ Recursion.

What can computers do?Work with discrete objects.Discrete Math =⇒ immense application.

Computers learn and interact with the world?E.g. machine learning, data analysis.Probability!

See note 1, for more discussion.

Page 6: 70: Discrete Math and Probability.

70: Discrete Math and Probability.

Programming Computers ≡ Superpower!

What are your super powerful programs doing?Logic and Proofs!Induction ≡ Recursion.

What can computers do?

Work with discrete objects.Discrete Math =⇒ immense application.

Computers learn and interact with the world?E.g. machine learning, data analysis.Probability!

See note 1, for more discussion.

Page 7: 70: Discrete Math and Probability.

70: Discrete Math and Probability.

Programming Computers ≡ Superpower!

What are your super powerful programs doing?Logic and Proofs!Induction ≡ Recursion.

What can computers do?Work with discrete objects.

Discrete Math =⇒ immense application.

Computers learn and interact with the world?E.g. machine learning, data analysis.Probability!

See note 1, for more discussion.

Page 8: 70: Discrete Math and Probability.

70: Discrete Math and Probability.

Programming Computers ≡ Superpower!

What are your super powerful programs doing?Logic and Proofs!Induction ≡ Recursion.

What can computers do?Work with discrete objects.Discrete Math

=⇒ immense application.

Computers learn and interact with the world?E.g. machine learning, data analysis.Probability!

See note 1, for more discussion.

Page 9: 70: Discrete Math and Probability.

70: Discrete Math and Probability.

Programming Computers ≡ Superpower!

What are your super powerful programs doing?Logic and Proofs!Induction ≡ Recursion.

What can computers do?Work with discrete objects.Discrete Math =⇒ immense application.

Computers learn and interact with the world?E.g. machine learning, data analysis.Probability!

See note 1, for more discussion.

Page 10: 70: Discrete Math and Probability.

70: Discrete Math and Probability.

Programming Computers ≡ Superpower!

What are your super powerful programs doing?Logic and Proofs!Induction ≡ Recursion.

What can computers do?Work with discrete objects.Discrete Math =⇒ immense application.

Computers learn and interact with the world?

E.g. machine learning, data analysis.Probability!

See note 1, for more discussion.

Page 11: 70: Discrete Math and Probability.

70: Discrete Math and Probability.

Programming Computers ≡ Superpower!

What are your super powerful programs doing?Logic and Proofs!Induction ≡ Recursion.

What can computers do?Work with discrete objects.Discrete Math =⇒ immense application.

Computers learn and interact with the world?E.g. machine learning, data analysis.

Probability!

See note 1, for more discussion.

Page 12: 70: Discrete Math and Probability.

70: Discrete Math and Probability.

Programming Computers ≡ Superpower!

What are your super powerful programs doing?Logic and Proofs!Induction ≡ Recursion.

What can computers do?Work with discrete objects.Discrete Math =⇒ immense application.

Computers learn and interact with the world?E.g. machine learning, data analysis.Probability!

See note 1, for more discussion.

Page 13: 70: Discrete Math and Probability.

70: Discrete Math and Probability.

Programming Computers ≡ Superpower!

What are your super powerful programs doing?Logic and Proofs!Induction ≡ Recursion.

What can computers do?Work with discrete objects.Discrete Math =⇒ immense application.

Computers learn and interact with the world?E.g. machine learning, data analysis.Probability!

See note 1, for more discussion.

Page 14: 70: Discrete Math and Probability.

Admin.Course Webpage: inst.cs.berkeley.edu/~cs70/sp16

Explains policies, has homework, midterm dates, etc.

Two midterms, final.midterm 1 before drop date. (2/16)midterm 2 before grade option change. (3/29)

Questions =⇒ piazza:piazza.com/berkeley/spring2016/cs70

Also: Available after class.Assessment: Two options:

Test Only.Midterm 1: 25%Midterm 2: 25%Final: 49%Sundry: 1%

Test plus Homework.Test Only Score: 85%Homework Score: 15%

Page 15: 70: Discrete Math and Probability.

Admin.Course Webpage: inst.cs.berkeley.edu/~cs70/sp16

Explains policies, has homework, midterm dates, etc.

Two midterms, final.midterm 1 before drop date. (2/16)midterm 2 before grade option change. (3/29)

Questions =⇒ piazza:piazza.com/berkeley/spring2016/cs70

Also: Available after class.Assessment: Two options:

Test Only.Midterm 1: 25%Midterm 2: 25%Final: 49%Sundry: 1%

Test plus Homework.Test Only Score: 85%Homework Score: 15%

Page 16: 70: Discrete Math and Probability.

Admin.Course Webpage: inst.cs.berkeley.edu/~cs70/sp16

Explains policies, has homework, midterm dates, etc.

Two midterms, final.

midterm 1 before drop date. (2/16)midterm 2 before grade option change. (3/29)

Questions =⇒ piazza:piazza.com/berkeley/spring2016/cs70

Also: Available after class.Assessment: Two options:

Test Only.Midterm 1: 25%Midterm 2: 25%Final: 49%Sundry: 1%

Test plus Homework.Test Only Score: 85%Homework Score: 15%

Page 17: 70: Discrete Math and Probability.

Admin.Course Webpage: inst.cs.berkeley.edu/~cs70/sp16

Explains policies, has homework, midterm dates, etc.

Two midterms, final.midterm 1 before drop date. (2/16)

midterm 2 before grade option change. (3/29)

Questions =⇒ piazza:piazza.com/berkeley/spring2016/cs70

Also: Available after class.Assessment: Two options:

Test Only.Midterm 1: 25%Midterm 2: 25%Final: 49%Sundry: 1%

Test plus Homework.Test Only Score: 85%Homework Score: 15%

Page 18: 70: Discrete Math and Probability.

Admin.Course Webpage: inst.cs.berkeley.edu/~cs70/sp16

Explains policies, has homework, midterm dates, etc.

Two midterms, final.midterm 1 before drop date. (2/16)midterm 2 before grade option change. (3/29)

Questions =⇒ piazza:piazza.com/berkeley/spring2016/cs70

Also: Available after class.Assessment: Two options:

Test Only.Midterm 1: 25%Midterm 2: 25%Final: 49%Sundry: 1%

Test plus Homework.Test Only Score: 85%Homework Score: 15%

Page 19: 70: Discrete Math and Probability.

Admin.Course Webpage: inst.cs.berkeley.edu/~cs70/sp16

Explains policies, has homework, midterm dates, etc.

Two midterms, final.midterm 1 before drop date. (2/16)midterm 2 before grade option change. (3/29)

Questions

=⇒ piazza:piazza.com/berkeley/spring2016/cs70

Also: Available after class.Assessment: Two options:

Test Only.Midterm 1: 25%Midterm 2: 25%Final: 49%Sundry: 1%

Test plus Homework.Test Only Score: 85%Homework Score: 15%

Page 20: 70: Discrete Math and Probability.

Admin.Course Webpage: inst.cs.berkeley.edu/~cs70/sp16

Explains policies, has homework, midterm dates, etc.

Two midterms, final.midterm 1 before drop date. (2/16)midterm 2 before grade option change. (3/29)

Questions =⇒ piazza:

piazza.com/berkeley/spring2016/cs70Also: Available after class.Assessment: Two options:

Test Only.Midterm 1: 25%Midterm 2: 25%Final: 49%Sundry: 1%

Test plus Homework.Test Only Score: 85%Homework Score: 15%

Page 21: 70: Discrete Math and Probability.

Admin.Course Webpage: inst.cs.berkeley.edu/~cs70/sp16

Explains policies, has homework, midterm dates, etc.

Two midterms, final.midterm 1 before drop date. (2/16)midterm 2 before grade option change. (3/29)

Questions =⇒ piazza:piazza.com/berkeley/spring2016/cs70

Also: Available after class.Assessment: Two options:

Test Only.Midterm 1: 25%Midterm 2: 25%Final: 49%Sundry: 1%

Test plus Homework.Test Only Score: 85%Homework Score: 15%

Page 22: 70: Discrete Math and Probability.

Admin.Course Webpage: inst.cs.berkeley.edu/~cs70/sp16

Explains policies, has homework, midterm dates, etc.

Two midterms, final.midterm 1 before drop date. (2/16)midterm 2 before grade option change. (3/29)

Questions =⇒ piazza:piazza.com/berkeley/spring2016/cs70

Also: Available after class.

Assessment: Two options:

Test Only.Midterm 1: 25%Midterm 2: 25%Final: 49%Sundry: 1%

Test plus Homework.Test Only Score: 85%Homework Score: 15%

Page 23: 70: Discrete Math and Probability.

Admin.Course Webpage: inst.cs.berkeley.edu/~cs70/sp16

Explains policies, has homework, midterm dates, etc.

Two midterms, final.midterm 1 before drop date. (2/16)midterm 2 before grade option change. (3/29)

Questions =⇒ piazza:piazza.com/berkeley/spring2016/cs70

Also: Available after class.Assessment:

Two options:

Test Only.Midterm 1: 25%Midterm 2: 25%Final: 49%Sundry: 1%

Test plus Homework.Test Only Score: 85%Homework Score: 15%

Page 24: 70: Discrete Math and Probability.

Admin.Course Webpage: inst.cs.berkeley.edu/~cs70/sp16

Explains policies, has homework, midterm dates, etc.

Two midterms, final.midterm 1 before drop date. (2/16)midterm 2 before grade option change. (3/29)

Questions =⇒ piazza:piazza.com/berkeley/spring2016/cs70

Also: Available after class.Assessment: Two options:

Test Only.Midterm 1: 25%Midterm 2: 25%Final: 49%Sundry: 1%

Test plus Homework.Test Only Score: 85%Homework Score: 15%

Page 25: 70: Discrete Math and Probability.

Admin.Course Webpage: inst.cs.berkeley.edu/~cs70/sp16

Explains policies, has homework, midterm dates, etc.

Two midterms, final.midterm 1 before drop date. (2/16)midterm 2 before grade option change. (3/29)

Questions =⇒ piazza:piazza.com/berkeley/spring2016/cs70

Also: Available after class.Assessment: Two options:

Test Only.Midterm 1: 25%Midterm 2: 25%Final: 49%Sundry: 1%

Test plus Homework.Test Only Score: 85%Homework Score: 15%

Page 26: 70: Discrete Math and Probability.

Admin.Course Webpage: inst.cs.berkeley.edu/~cs70/sp16

Explains policies, has homework, midterm dates, etc.

Two midterms, final.midterm 1 before drop date. (2/16)midterm 2 before grade option change. (3/29)

Questions =⇒ piazza:piazza.com/berkeley/spring2016/cs70

Also: Available after class.Assessment: Two options:

Test Only.Midterm 1: 25%Midterm 2: 25%Final: 49%Sundry: 1%

Test plus Homework.Test Only Score: 85%Homework Score: 15%

Page 27: 70: Discrete Math and Probability.

Admin.Course Webpage: inst.cs.berkeley.edu/~cs70/sp16

Explains policies, has homework, midterm dates, etc.

Two midterms, final.midterm 1 before drop date. (2/16)midterm 2 before grade option change. (3/29)

Questions =⇒ piazza:piazza.com/berkeley/spring2016/cs70

Also: Available after class.Assessment: Two options:

Test Only.Midterm 1: 25%Midterm 2: 25%Final: 49%Sundry: 1%

Test plus Homework.Test Only Score: 85%Homework Score: 15%

Page 28: 70: Discrete Math and Probability.

Instructor/Admin

Instructors: Satish Rao and Jean Walrand.

Both are available throughout the course.

Office hours or by email, technical and administrative.

Satish Rao: mostly discrete math.

Jean Walrand: mostly probability.

Page 29: 70: Discrete Math and Probability.

Instructor/Admin

Instructors: Satish Rao and Jean Walrand.

Both are available throughout the course.

Office hours or by email, technical and administrative.

Satish Rao: mostly discrete math.

Jean Walrand: mostly probability.

Page 30: 70: Discrete Math and Probability.

Instructor/Admin

Instructors: Satish Rao and Jean Walrand.

Both are available throughout the course.

Office hours or by email, technical and administrative.

Satish Rao: mostly discrete math.

Jean Walrand: mostly probability.

Page 31: 70: Discrete Math and Probability.

Instructor/Admin

Instructors: Satish Rao and Jean Walrand.

Both are available throughout the course.

Office hours or by email, technical and administrative.

Satish Rao: mostly discrete math.

Jean Walrand: mostly probability.

Page 32: 70: Discrete Math and Probability.

Instructor/Admin

Instructors: Satish Rao and Jean Walrand.

Both are available throughout the course.

Office hours or by email, technical and administrative.

Satish Rao: mostly discrete math.

Jean Walrand: mostly probability.

Page 33: 70: Discrete Math and Probability.

I  was  born  in  Belgium(1)  and  came  to  Berkeley  for  my  PhD.    I  have  been  teaching  at  UCB  since  1982.      My  wife  and  I  live  in  Berkeley.    We  have  two  daughters  (UC  alumni  –  Go  Bears!).  We  like  to  ski  and  play  tennis  (both  poorly).    We  enjoy  classical  music  and  jazz.        My  research  interests  include  stochasLc  systems,  networks  and  game  theory.  

Jean  Walrand  –  Prof.  of  EECS  –  UCB  257  Cory  Hall  –  [email protected]  

(1)  

Page 34: 70: Discrete Math and Probability.

Satish Rao

17th year at Berkeley.

PhD: Long time ago, far far away.Research: Theory (Algorithms)Taught: 170, 174, 70, 270, 273, 294, 375, ...

Recovering Helicopter(ish) parent of 3 College(ish) kids.

Page 35: 70: Discrete Math and Probability.

Satish Rao

17th year at Berkeley.PhD: Long time ago,

far far away.Research: Theory (Algorithms)Taught: 170, 174, 70, 270, 273, 294, 375, ...

Recovering Helicopter(ish) parent of 3 College(ish) kids.

Page 36: 70: Discrete Math and Probability.

Satish Rao

17th year at Berkeley.PhD: Long time ago, far

far away.Research: Theory (Algorithms)Taught: 170, 174, 70, 270, 273, 294, 375, ...

Recovering Helicopter(ish) parent of 3 College(ish) kids.

Page 37: 70: Discrete Math and Probability.

Satish Rao

17th year at Berkeley.PhD: Long time ago, far far away.

Research: Theory (Algorithms)Taught: 170, 174, 70, 270, 273, 294, 375, ...

Recovering Helicopter(ish) parent of 3 College(ish) kids.

Page 38: 70: Discrete Math and Probability.

Satish Rao

17th year at Berkeley.PhD: Long time ago, far far away.Research: Theory

(Algorithms)Taught: 170, 174, 70, 270, 273, 294, 375, ...

Recovering Helicopter(ish) parent of 3 College(ish) kids.

Page 39: 70: Discrete Math and Probability.

Satish Rao

17th year at Berkeley.PhD: Long time ago, far far away.Research: Theory (Algorithms)

Taught: 170, 174, 70, 270, 273, 294, 375, ...

Recovering Helicopter(ish) parent of 3 College(ish) kids.

Page 40: 70: Discrete Math and Probability.

Satish Rao

17th year at Berkeley.PhD: Long time ago, far far away.Research: Theory (Algorithms)Taught: 170, 174, 70, 270, 273, 294,

375, ...

Recovering Helicopter(ish) parent of 3 College(ish) kids.

Page 41: 70: Discrete Math and Probability.

Satish Rao

17th year at Berkeley.PhD: Long time ago, far far away.Research: Theory (Algorithms)Taught: 170, 174, 70, 270, 273, 294, 375, ...

Recovering Helicopter(ish) parent of 3 College(ish) kids.

Page 42: 70: Discrete Math and Probability.

Satish Rao

17th year at Berkeley.PhD: Long time ago, far far away.Research: Theory (Algorithms)Taught: 170, 174, 70, 270, 273, 294, 375, ...

Recovering Helicopter(ish) parent of 3 College(ish) kids.

Page 43: 70: Discrete Math and Probability.

Satish Rao

17th year at Berkeley.PhD: Long time ago, far far away.Research: Theory (Algorithms)Taught: 170, 174, 70, 270, 273, 294, 375, ...

Recovering Helicopter(ish) parent of 3 College(ish) kids.

Page 44: 70: Discrete Math and Probability.

Wason’s experiment:1

Suppose we have four cards on a table:

I 1st about Alice, 2nd about Bob, 3rd Charlie, 4th Donna.

I Card contains person’s destination on one side,and mode of travel.

I Consider the theory:“If a person travels to Chicago, he/she flies.”

I Suppose you see that Alice went to Baltimore, Bob drove,Charlie went to Chicago, and Donna flew.

Alice

Baltimore

Bob

drove

Charlie

Chicago

Donna

flew

I Which cards do you need to flip to test the theory?

Answer: Later.

Page 45: 70: Discrete Math and Probability.

Wason’s experiment:1

Suppose we have four cards on a table:

I 1st about Alice, 2nd about Bob, 3rd Charlie, 4th Donna.

I Card contains person’s destination on one side,and mode of travel.

I Consider the theory:“If a person travels to Chicago, he/she flies.”

I Suppose you see that Alice went to Baltimore, Bob drove,Charlie went to Chicago, and Donna flew.

Alice

Baltimore

Bob

drove

Charlie

Chicago

Donna

flew

I Which cards do you need to flip to test the theory?

Answer: Later.

Page 46: 70: Discrete Math and Probability.

Wason’s experiment:1

Suppose we have four cards on a table:

I 1st about Alice, 2nd about Bob, 3rd Charlie, 4th Donna.

I Card contains person’s destination on one side,and mode of travel.

I Consider the theory:

“If a person travels to Chicago, he/she flies.”

I Suppose you see that Alice went to Baltimore, Bob drove,Charlie went to Chicago, and Donna flew.

Alice

Baltimore

Bob

drove

Charlie

Chicago

Donna

flew

I Which cards do you need to flip to test the theory?

Answer: Later.

Page 47: 70: Discrete Math and Probability.

Wason’s experiment:1

Suppose we have four cards on a table:

I 1st about Alice, 2nd about Bob, 3rd Charlie, 4th Donna.

I Card contains person’s destination on one side,and mode of travel.

I Consider the theory:“If a person travels to Chicago, he/she flies.”

I Suppose you see that Alice went to Baltimore, Bob drove,Charlie went to Chicago, and Donna flew.

Alice

Baltimore

Bob

drove

Charlie

Chicago

Donna

flew

I Which cards do you need to flip to test the theory?

Answer: Later.

Page 48: 70: Discrete Math and Probability.

Wason’s experiment:1

Suppose we have four cards on a table:

I 1st about Alice, 2nd about Bob, 3rd Charlie, 4th Donna.

I Card contains person’s destination on one side,and mode of travel.

I Consider the theory:“If a person travels to Chicago, he/she flies.”

I Suppose you see that Alice went to Baltimore, Bob drove,Charlie went to Chicago, and Donna flew.

Alice

Baltimore

Bob

drove

Charlie

Chicago

Donna

flew

I Which cards do you need to flip to test the theory?

Answer: Later.

Page 49: 70: Discrete Math and Probability.

Wason’s experiment:1

Suppose we have four cards on a table:

I 1st about Alice, 2nd about Bob, 3rd Charlie, 4th Donna.

I Card contains person’s destination on one side,and mode of travel.

I Consider the theory:“If a person travels to Chicago, he/she flies.”

I Suppose you see that Alice went to Baltimore, Bob drove,Charlie went to Chicago, and Donna flew.

Alice

Baltimore

Bob

drove

Charlie

Chicago

Donna

flew

I Which cards do you need to flip to test the theory?

Answer: Later.

Page 50: 70: Discrete Math and Probability.

Wason’s experiment:1

Suppose we have four cards on a table:

I 1st about Alice, 2nd about Bob, 3rd Charlie, 4th Donna.

I Card contains person’s destination on one side,and mode of travel.

I Consider the theory:“If a person travels to Chicago, he/she flies.”

I Suppose you see that Alice went to Baltimore, Bob drove,Charlie went to Chicago, and Donna flew.

Alice

Baltimore

Bob

drove

Charlie

Chicago

Donna

flew

I Which cards do you need to flip to test the theory?

Answer: Later.

Page 51: 70: Discrete Math and Probability.

Wason’s experiment:1

Suppose we have four cards on a table:

I 1st about Alice, 2nd about Bob, 3rd Charlie, 4th Donna.

I Card contains person’s destination on one side,and mode of travel.

I Consider the theory:“If a person travels to Chicago, he/she flies.”

I Suppose you see that Alice went to Baltimore, Bob drove,Charlie went to Chicago, and Donna flew.

Alice

Baltimore

Bob

drove

Charlie

Chicago

Donna

flew

I Which cards do you need to flip to test the theory?

Answer:

Later.

Page 52: 70: Discrete Math and Probability.

Wason’s experiment:1

Suppose we have four cards on a table:

I 1st about Alice, 2nd about Bob, 3rd Charlie, 4th Donna.

I Card contains person’s destination on one side,and mode of travel.

I Consider the theory:“If a person travels to Chicago, he/she flies.”

I Suppose you see that Alice went to Baltimore, Bob drove,Charlie went to Chicago, and Donna flew.

Alice

Baltimore

Bob

drove

Charlie

Chicago

Donna

flew

I Which cards do you need to flip to test the theory?

Answer: Later.

Page 53: 70: Discrete Math and Probability.

CS70: Lecture 1. Outline.

Today: Note 1.

Note 0 is background. Do read/skim it.

The language of proofs!

1. Propositions.

2. Propositional Forms.

3. Implication.

4. Truth Tables

5. Quantifiers

6. More De Morgan’s Laws

Page 54: 70: Discrete Math and Probability.

CS70: Lecture 1. Outline.

Today: Note 1. Note 0 is background.

Do read/skim it.

The language of proofs!

1. Propositions.

2. Propositional Forms.

3. Implication.

4. Truth Tables

5. Quantifiers

6. More De Morgan’s Laws

Page 55: 70: Discrete Math and Probability.

CS70: Lecture 1. Outline.

Today: Note 1. Note 0 is background. Do read/skim it.

The language of proofs!

1. Propositions.

2. Propositional Forms.

3. Implication.

4. Truth Tables

5. Quantifiers

6. More De Morgan’s Laws

Page 56: 70: Discrete Math and Probability.

CS70: Lecture 1. Outline.

Today: Note 1. Note 0 is background. Do read/skim it.

The language of proofs!

1. Propositions.

2. Propositional Forms.

3. Implication.

4. Truth Tables

5. Quantifiers

6. More De Morgan’s Laws

Page 57: 70: Discrete Math and Probability.

CS70: Lecture 1. Outline.

Today: Note 1. Note 0 is background. Do read/skim it.

The language of proofs!

1. Propositions.

2. Propositional Forms.

3. Implication.

4. Truth Tables

5. Quantifiers

6. More De Morgan’s Laws

Page 58: 70: Discrete Math and Probability.

Propositions: Statements that are true or false.

√2 is irrational

Proposition True

2+2 = 4

Proposition True

2+2 = 3

Proposition False

826th digit of pi is 4

Proposition False

Johny Depp is a good actor

Not a Proposition

All evens > 2 are sums of 2 primes

Proposition False

4+5

Not a Proposition.

x +x

Not a Proposition.

Alice travelled to Chicago

Proposition. False

Again: “value” of a proposition is ...

True or False

Page 59: 70: Discrete Math and Probability.

Propositions: Statements that are true or false.

√2 is irrational Proposition

True

2+2 = 4

Proposition True

2+2 = 3

Proposition False

826th digit of pi is 4

Proposition False

Johny Depp is a good actor

Not a Proposition

All evens > 2 are sums of 2 primes

Proposition False

4+5

Not a Proposition.

x +x

Not a Proposition.

Alice travelled to Chicago

Proposition. False

Again: “value” of a proposition is ...

True or False

Page 60: 70: Discrete Math and Probability.

Propositions: Statements that are true or false.

√2 is irrational Proposition True

2+2 = 4

Proposition True

2+2 = 3

Proposition False

826th digit of pi is 4

Proposition False

Johny Depp is a good actor

Not a Proposition

All evens > 2 are sums of 2 primes

Proposition False

4+5

Not a Proposition.

x +x

Not a Proposition.

Alice travelled to Chicago

Proposition. False

Again: “value” of a proposition is ...

True or False

Page 61: 70: Discrete Math and Probability.

Propositions: Statements that are true or false.

√2 is irrational Proposition True

2+2 = 4 Proposition

True

2+2 = 3

Proposition False

826th digit of pi is 4

Proposition False

Johny Depp is a good actor

Not a Proposition

All evens > 2 are sums of 2 primes

Proposition False

4+5

Not a Proposition.

x +x

Not a Proposition.

Alice travelled to Chicago

Proposition. False

Again: “value” of a proposition is ...

True or False

Page 62: 70: Discrete Math and Probability.

Propositions: Statements that are true or false.

√2 is irrational Proposition True

2+2 = 4 Proposition True2+2 = 3

Proposition False

826th digit of pi is 4

Proposition False

Johny Depp is a good actor

Not a Proposition

All evens > 2 are sums of 2 primes

Proposition False

4+5

Not a Proposition.

x +x

Not a Proposition.

Alice travelled to Chicago

Proposition. False

Again: “value” of a proposition is ...

True or False

Page 63: 70: Discrete Math and Probability.

Propositions: Statements that are true or false.

√2 is irrational Proposition True

2+2 = 4 Proposition True2+2 = 3 Proposition

False

826th digit of pi is 4

Proposition False

Johny Depp is a good actor

Not a Proposition

All evens > 2 are sums of 2 primes

Proposition False

4+5

Not a Proposition.

x +x

Not a Proposition.

Alice travelled to Chicago

Proposition. False

Again: “value” of a proposition is ...

True or False

Page 64: 70: Discrete Math and Probability.

Propositions: Statements that are true or false.

√2 is irrational Proposition True

2+2 = 4 Proposition True2+2 = 3 Proposition False826th digit of pi is 4

Proposition False

Johny Depp is a good actor

Not a Proposition

All evens > 2 are sums of 2 primes

Proposition False

4+5

Not a Proposition.

x +x

Not a Proposition.

Alice travelled to Chicago

Proposition. False

Again: “value” of a proposition is ...

True or False

Page 65: 70: Discrete Math and Probability.

Propositions: Statements that are true or false.

√2 is irrational Proposition True

2+2 = 4 Proposition True2+2 = 3 Proposition False826th digit of pi is 4 Proposition

False

Johny Depp is a good actor

Not a Proposition

All evens > 2 are sums of 2 primes

Proposition False

4+5

Not a Proposition.

x +x

Not a Proposition.

Alice travelled to Chicago

Proposition. False

Again: “value” of a proposition is ...

True or False

Page 66: 70: Discrete Math and Probability.

Propositions: Statements that are true or false.

√2 is irrational Proposition True

2+2 = 4 Proposition True2+2 = 3 Proposition False826th digit of pi is 4 Proposition FalseJohny Depp is a good actor

Not a Proposition

All evens > 2 are sums of 2 primes

Proposition False

4+5

Not a Proposition.

x +x

Not a Proposition.

Alice travelled to Chicago

Proposition. False

Again: “value” of a proposition is ...

True or False

Page 67: 70: Discrete Math and Probability.

Propositions: Statements that are true or false.

√2 is irrational Proposition True

2+2 = 4 Proposition True2+2 = 3 Proposition False826th digit of pi is 4 Proposition FalseJohny Depp is a good actor Not a PropositionAll evens > 2 are sums of 2 primes

Proposition False

4+5

Not a Proposition.

x +x

Not a Proposition.

Alice travelled to Chicago

Proposition. False

Again: “value” of a proposition is ...

True or False

Page 68: 70: Discrete Math and Probability.

Propositions: Statements that are true or false.

√2 is irrational Proposition True

2+2 = 4 Proposition True2+2 = 3 Proposition False826th digit of pi is 4 Proposition FalseJohny Depp is a good actor Not a PropositionAll evens > 2 are sums of 2 primes Proposition

False

4+5

Not a Proposition.

x +x

Not a Proposition.

Alice travelled to Chicago

Proposition. False

Again: “value” of a proposition is ...

True or False

Page 69: 70: Discrete Math and Probability.

Propositions: Statements that are true or false.

√2 is irrational Proposition True

2+2 = 4 Proposition True2+2 = 3 Proposition False826th digit of pi is 4 Proposition FalseJohny Depp is a good actor Not a PropositionAll evens > 2 are sums of 2 primes Proposition False4+5

Not a Proposition.

x +x

Not a Proposition.

Alice travelled to Chicago

Proposition. False

Again: “value” of a proposition is ...

True or False

Page 70: 70: Discrete Math and Probability.

Propositions: Statements that are true or false.

√2 is irrational Proposition True

2+2 = 4 Proposition True2+2 = 3 Proposition False826th digit of pi is 4 Proposition FalseJohny Depp is a good actor Not a PropositionAll evens > 2 are sums of 2 primes Proposition False4+5 Not a Proposition.x +x

Not a Proposition.

Alice travelled to Chicago

Proposition. False

Again: “value” of a proposition is ...

True or False

Page 71: 70: Discrete Math and Probability.

Propositions: Statements that are true or false.

√2 is irrational Proposition True

2+2 = 4 Proposition True2+2 = 3 Proposition False826th digit of pi is 4 Proposition FalseJohny Depp is a good actor Not a PropositionAll evens > 2 are sums of 2 primes Proposition False4+5 Not a Proposition.x +x Not a Proposition.Alice travelled to Chicago

Proposition. False

Again: “value” of a proposition is ...

True or False

Page 72: 70: Discrete Math and Probability.

Propositions: Statements that are true or false.

√2 is irrational Proposition True

2+2 = 4 Proposition True2+2 = 3 Proposition False826th digit of pi is 4 Proposition FalseJohny Depp is a good actor Not a PropositionAll evens > 2 are sums of 2 primes Proposition False4+5 Not a Proposition.x +x Not a Proposition.Alice travelled to Chicago Proposition.

False

Again: “value” of a proposition is ...

True or False

Page 73: 70: Discrete Math and Probability.

Propositions: Statements that are true or false.

√2 is irrational Proposition True

2+2 = 4 Proposition True2+2 = 3 Proposition False826th digit of pi is 4 Proposition FalseJohny Depp is a good actor Not a PropositionAll evens > 2 are sums of 2 primes Proposition False4+5 Not a Proposition.x +x Not a Proposition.Alice travelled to Chicago Proposition. False

Again: “value” of a proposition is ... True or False

Page 74: 70: Discrete Math and Probability.

Propositional Forms.

Put propositions together to make another...

Conjunction (“and”): P ∧Q

“P ∧Q” is True when both P and Q are True . Else False .

Disjunction (“or”): P ∨Q

“P ∨Q” is True when at least one P or Q is True . Else False .

Negation (“not”): ¬P

“¬P” is True when P is False . Else False .

Examples:

¬ “(2+2 = 4)” – a proposition that is ... False

“2+2 = 3”∧ “2+2 = 4” – a proposition that is ... False

“2+2 = 3”∨ “2+2 = 4” – a proposition that is ... True

Page 75: 70: Discrete Math and Probability.

Propositional Forms.

Put propositions together to make another...

Conjunction (“and”): P ∧Q

“P ∧Q” is True when both P and Q are True . Else False .

Disjunction (“or”): P ∨Q

“P ∨Q” is True when at least one P or Q is True . Else False .

Negation (“not”): ¬P

“¬P” is True when P is False . Else False .

Examples:

¬ “(2+2 = 4)” – a proposition that is ... False

“2+2 = 3”∧ “2+2 = 4” – a proposition that is ... False

“2+2 = 3”∨ “2+2 = 4” – a proposition that is ... True

Page 76: 70: Discrete Math and Probability.

Propositional Forms.

Put propositions together to make another...

Conjunction (“and”): P ∧Q

“P ∧Q” is True when both P and Q are True .

Else False .

Disjunction (“or”): P ∨Q

“P ∨Q” is True when at least one P or Q is True . Else False .

Negation (“not”): ¬P

“¬P” is True when P is False . Else False .

Examples:

¬ “(2+2 = 4)” – a proposition that is ... False

“2+2 = 3”∧ “2+2 = 4” – a proposition that is ... False

“2+2 = 3”∨ “2+2 = 4” – a proposition that is ... True

Page 77: 70: Discrete Math and Probability.

Propositional Forms.

Put propositions together to make another...

Conjunction (“and”): P ∧Q

“P ∧Q” is True when both P and Q are True . Else False .

Disjunction (“or”): P ∨Q

“P ∨Q” is True when at least one P or Q is True . Else False .

Negation (“not”): ¬P

“¬P” is True when P is False . Else False .

Examples:

¬ “(2+2 = 4)” – a proposition that is ... False

“2+2 = 3”∧ “2+2 = 4” – a proposition that is ... False

“2+2 = 3”∨ “2+2 = 4” – a proposition that is ... True

Page 78: 70: Discrete Math and Probability.

Propositional Forms.

Put propositions together to make another...

Conjunction (“and”): P ∧Q

“P ∧Q” is True when both P and Q are True . Else False .

Disjunction (“or”): P ∨Q

“P ∨Q” is True when at least one P or Q is True . Else False .

Negation (“not”): ¬P

“¬P” is True when P is False . Else False .

Examples:

¬ “(2+2 = 4)” – a proposition that is ... False

“2+2 = 3”∧ “2+2 = 4” – a proposition that is ... False

“2+2 = 3”∨ “2+2 = 4” – a proposition that is ... True

Page 79: 70: Discrete Math and Probability.

Propositional Forms.

Put propositions together to make another...

Conjunction (“and”): P ∧Q

“P ∧Q” is True when both P and Q are True . Else False .

Disjunction (“or”): P ∨Q

“P ∨Q” is True when at least one P or Q is True .

Else False .

Negation (“not”): ¬P

“¬P” is True when P is False . Else False .

Examples:

¬ “(2+2 = 4)” – a proposition that is ... False

“2+2 = 3”∧ “2+2 = 4” – a proposition that is ... False

“2+2 = 3”∨ “2+2 = 4” – a proposition that is ... True

Page 80: 70: Discrete Math and Probability.

Propositional Forms.

Put propositions together to make another...

Conjunction (“and”): P ∧Q

“P ∧Q” is True when both P and Q are True . Else False .

Disjunction (“or”): P ∨Q

“P ∨Q” is True when at least one P or Q is True . Else False .

Negation (“not”): ¬P

“¬P” is True when P is False . Else False .

Examples:

¬ “(2+2 = 4)” – a proposition that is ... False

“2+2 = 3”∧ “2+2 = 4” – a proposition that is ... False

“2+2 = 3”∨ “2+2 = 4” – a proposition that is ... True

Page 81: 70: Discrete Math and Probability.

Propositional Forms.

Put propositions together to make another...

Conjunction (“and”): P ∧Q

“P ∧Q” is True when both P and Q are True . Else False .

Disjunction (“or”): P ∨Q

“P ∨Q” is True when at least one P or Q is True . Else False .

Negation (“not”): ¬P

“¬P” is True when P is False . Else False .

Examples:

¬ “(2+2 = 4)” – a proposition that is ... False

“2+2 = 3”∧ “2+2 = 4” – a proposition that is ... False

“2+2 = 3”∨ “2+2 = 4” – a proposition that is ... True

Page 82: 70: Discrete Math and Probability.

Propositional Forms.

Put propositions together to make another...

Conjunction (“and”): P ∧Q

“P ∧Q” is True when both P and Q are True . Else False .

Disjunction (“or”): P ∨Q

“P ∨Q” is True when at least one P or Q is True . Else False .

Negation (“not”): ¬P

“¬P” is True when P is False .

Else False .

Examples:

¬ “(2+2 = 4)” – a proposition that is ... False

“2+2 = 3”∧ “2+2 = 4” – a proposition that is ... False

“2+2 = 3”∨ “2+2 = 4” – a proposition that is ... True

Page 83: 70: Discrete Math and Probability.

Propositional Forms.

Put propositions together to make another...

Conjunction (“and”): P ∧Q

“P ∧Q” is True when both P and Q are True . Else False .

Disjunction (“or”): P ∨Q

“P ∨Q” is True when at least one P or Q is True . Else False .

Negation (“not”): ¬P

“¬P” is True when P is False . Else False .

Examples:

¬ “(2+2 = 4)” – a proposition that is ... False

“2+2 = 3”∧ “2+2 = 4” – a proposition that is ... False

“2+2 = 3”∨ “2+2 = 4” – a proposition that is ... True

Page 84: 70: Discrete Math and Probability.

Propositional Forms.

Put propositions together to make another...

Conjunction (“and”): P ∧Q

“P ∧Q” is True when both P and Q are True . Else False .

Disjunction (“or”): P ∨Q

“P ∨Q” is True when at least one P or Q is True . Else False .

Negation (“not”): ¬P

“¬P” is True when P is False . Else False .

Examples:

¬ “(2+2 = 4)” – a proposition that is ... False

“2+2 = 3”∧ “2+2 = 4” – a proposition that is ... False

“2+2 = 3”∨ “2+2 = 4” – a proposition that is ... True

Page 85: 70: Discrete Math and Probability.

Propositional Forms.

Put propositions together to make another...

Conjunction (“and”): P ∧Q

“P ∧Q” is True when both P and Q are True . Else False .

Disjunction (“or”): P ∨Q

“P ∨Q” is True when at least one P or Q is True . Else False .

Negation (“not”): ¬P

“¬P” is True when P is False . Else False .

Examples:

¬ “(2+2 = 4)” – a proposition that is ...

False

“2+2 = 3”∧ “2+2 = 4” – a proposition that is ... False

“2+2 = 3”∨ “2+2 = 4” – a proposition that is ... True

Page 86: 70: Discrete Math and Probability.

Propositional Forms.

Put propositions together to make another...

Conjunction (“and”): P ∧Q

“P ∧Q” is True when both P and Q are True . Else False .

Disjunction (“or”): P ∨Q

“P ∨Q” is True when at least one P or Q is True . Else False .

Negation (“not”): ¬P

“¬P” is True when P is False . Else False .

Examples:

¬ “(2+2 = 4)” – a proposition that is ... False

“2+2 = 3”∧ “2+2 = 4” – a proposition that is ... False

“2+2 = 3”∨ “2+2 = 4” – a proposition that is ... True

Page 87: 70: Discrete Math and Probability.

Propositional Forms.

Put propositions together to make another...

Conjunction (“and”): P ∧Q

“P ∧Q” is True when both P and Q are True . Else False .

Disjunction (“or”): P ∨Q

“P ∨Q” is True when at least one P or Q is True . Else False .

Negation (“not”): ¬P

“¬P” is True when P is False . Else False .

Examples:

¬ “(2+2 = 4)” – a proposition that is ... False

“2+2 = 3”∧ “2+2 = 4” – a proposition that is ...

False

“2+2 = 3”∨ “2+2 = 4” – a proposition that is ... True

Page 88: 70: Discrete Math and Probability.

Propositional Forms.

Put propositions together to make another...

Conjunction (“and”): P ∧Q

“P ∧Q” is True when both P and Q are True . Else False .

Disjunction (“or”): P ∨Q

“P ∨Q” is True when at least one P or Q is True . Else False .

Negation (“not”): ¬P

“¬P” is True when P is False . Else False .

Examples:

¬ “(2+2 = 4)” – a proposition that is ... False

“2+2 = 3”∧ “2+2 = 4” – a proposition that is ... False

“2+2 = 3”∨ “2+2 = 4” – a proposition that is ... True

Page 89: 70: Discrete Math and Probability.

Propositional Forms.

Put propositions together to make another...

Conjunction (“and”): P ∧Q

“P ∧Q” is True when both P and Q are True . Else False .

Disjunction (“or”): P ∨Q

“P ∨Q” is True when at least one P or Q is True . Else False .

Negation (“not”): ¬P

“¬P” is True when P is False . Else False .

Examples:

¬ “(2+2 = 4)” – a proposition that is ... False

“2+2 = 3”∧ “2+2 = 4” – a proposition that is ... False

“2+2 = 3”∨ “2+2 = 4” – a proposition that is ...

True

Page 90: 70: Discrete Math and Probability.

Propositional Forms.

Put propositions together to make another...

Conjunction (“and”): P ∧Q

“P ∧Q” is True when both P and Q are True . Else False .

Disjunction (“or”): P ∨Q

“P ∨Q” is True when at least one P or Q is True . Else False .

Negation (“not”): ¬P

“¬P” is True when P is False . Else False .

Examples:

¬ “(2+2 = 4)” – a proposition that is ... False

“2+2 = 3”∧ “2+2 = 4” – a proposition that is ... False

“2+2 = 3”∨ “2+2 = 4” – a proposition that is ... True

Page 91: 70: Discrete Math and Probability.

Propositional Forms.

Put propositions together to make another...

Conjunction (“and”): P ∧Q

“P ∧Q” is True when both P and Q are True . Else False .

Disjunction (“or”): P ∨Q

“P ∨Q” is True when at least one P or Q is True . Else False .

Negation (“not”): ¬P

“¬P” is True when P is False . Else False .

Examples:

¬ “(2+2 = 4)” – a proposition that is ... False

“2+2 = 3”∧ “2+2 = 4” – a proposition that is ... False

“2+2 = 3”∨ “2+2 = 4” – a proposition that is ... True

Page 92: 70: Discrete Math and Probability.

Propositional Forms: quick check!

P = “√

2 is rational”

Q = “826th digit of pi is 2”

P is ...False .Q is ...True .

P ∧Q ... False

P ∨Q ... True

¬P ... True

Page 93: 70: Discrete Math and Probability.

Propositional Forms: quick check!

P = “√

2 is rational”Q = “826th digit of pi is 2”

P is ...False .Q is ...True .

P ∧Q ... False

P ∨Q ... True

¬P ... True

Page 94: 70: Discrete Math and Probability.

Propositional Forms: quick check!

P = “√

2 is rational”Q = “826th digit of pi is 2”

P is ...False .Q is ...True .

P ∧Q ... False

P ∨Q ... True

¬P ... True

Page 95: 70: Discrete Math and Probability.

Propositional Forms: quick check!

P = “√

2 is rational”Q = “826th digit of pi is 2”

P is ...

False .Q is ...True .

P ∧Q ... False

P ∨Q ... True

¬P ... True

Page 96: 70: Discrete Math and Probability.

Propositional Forms: quick check!

P = “√

2 is rational”Q = “826th digit of pi is 2”

P is ...False .

Q is ...True .

P ∧Q ... False

P ∨Q ... True

¬P ... True

Page 97: 70: Discrete Math and Probability.

Propositional Forms: quick check!

P = “√

2 is rational”Q = “826th digit of pi is 2”

P is ...False .Q is ...

True .

P ∧Q ... False

P ∨Q ... True

¬P ... True

Page 98: 70: Discrete Math and Probability.

Propositional Forms: quick check!

P = “√

2 is rational”Q = “826th digit of pi is 2”

P is ...False .Q is ...True .

P ∧Q ... False

P ∨Q ... True

¬P ... True

Page 99: 70: Discrete Math and Probability.

Propositional Forms: quick check!

P = “√

2 is rational”Q = “826th digit of pi is 2”

P is ...False .Q is ...True .

P ∧Q ...

False

P ∨Q ... True

¬P ... True

Page 100: 70: Discrete Math and Probability.

Propositional Forms: quick check!

P = “√

2 is rational”Q = “826th digit of pi is 2”

P is ...False .Q is ...True .

P ∧Q ... False

P ∨Q ... True

¬P ... True

Page 101: 70: Discrete Math and Probability.

Propositional Forms: quick check!

P = “√

2 is rational”Q = “826th digit of pi is 2”

P is ...False .Q is ...True .

P ∧Q ... False

P ∨Q ...

True

¬P ... True

Page 102: 70: Discrete Math and Probability.

Propositional Forms: quick check!

P = “√

2 is rational”Q = “826th digit of pi is 2”

P is ...False .Q is ...True .

P ∧Q ... False

P ∨Q ... True

¬P ... True

Page 103: 70: Discrete Math and Probability.

Propositional Forms: quick check!

P = “√

2 is rational”Q = “826th digit of pi is 2”

P is ...False .Q is ...True .

P ∧Q ... False

P ∨Q ... True

¬P ...

True

Page 104: 70: Discrete Math and Probability.

Propositional Forms: quick check!

P = “√

2 is rational”Q = “826th digit of pi is 2”

P is ...False .Q is ...True .

P ∧Q ... False

P ∨Q ... True

¬P ... True

Page 105: 70: Discrete Math and Probability.

Put them together..

Propositions:P1 - Person 1 rides the bus.

P2 - Person 2 rides the bus.....

But we can’t have either of the following happen; That either person 1or person 2 ride the bus and person 3 or 4 ride the bus. Or thatperson 2 or person 3 ride the bus and that either person 4 ride thebus or person 5 doesn’t.

Propositional Form:¬(((P1∨P2)∧ (P3∨P4))∨ ((P2∨P3)∧ (P4∨¬P5)))

Can person 3 ride the bus?Can person 3 and person 4 ride the bus together?

This seems ...complicated.

We can program!!!!

We need a way to keep track!

Page 106: 70: Discrete Math and Probability.

Put them together..

Propositions:P1 - Person 1 rides the bus.P2 - Person 2 rides the bus.

....

But we can’t have either of the following happen; That either person 1or person 2 ride the bus and person 3 or 4 ride the bus. Or thatperson 2 or person 3 ride the bus and that either person 4 ride thebus or person 5 doesn’t.

Propositional Form:¬(((P1∨P2)∧ (P3∨P4))∨ ((P2∨P3)∧ (P4∨¬P5)))

Can person 3 ride the bus?Can person 3 and person 4 ride the bus together?

This seems ...complicated.

We can program!!!!

We need a way to keep track!

Page 107: 70: Discrete Math and Probability.

Put them together..

Propositions:P1 - Person 1 rides the bus.P2 - Person 2 rides the bus.....

But we can’t have either of the following happen; That either person 1or person 2 ride the bus and person 3 or 4 ride the bus. Or thatperson 2 or person 3 ride the bus and that either person 4 ride thebus or person 5 doesn’t.

Propositional Form:¬(((P1∨P2)∧ (P3∨P4))∨ ((P2∨P3)∧ (P4∨¬P5)))

Can person 3 ride the bus?Can person 3 and person 4 ride the bus together?

This seems ...complicated.

We can program!!!!

We need a way to keep track!

Page 108: 70: Discrete Math and Probability.

Put them together..

Propositions:P1 - Person 1 rides the bus.P2 - Person 2 rides the bus.....

But we can’t have either of the following happen; That either person 1or person 2 ride the bus and person 3 or 4 ride the bus. Or thatperson 2 or person 3 ride the bus and that either person 4 ride thebus or person 5 doesn’t.

Propositional Form:¬(((P1∨P2)∧ (P3∨P4))∨ ((P2∨P3)∧ (P4∨¬P5)))

Can person 3 ride the bus?Can person 3 and person 4 ride the bus together?

This seems ...complicated.

We can program!!!!

We need a way to keep track!

Page 109: 70: Discrete Math and Probability.

Put them together..

Propositions:P1 - Person 1 rides the bus.P2 - Person 2 rides the bus.....

But we can’t have either of the following happen; That either person 1or person 2 ride the bus and person 3 or 4 ride the bus. Or thatperson 2 or person 3 ride the bus and that either person 4 ride thebus or person 5 doesn’t.

Propositional Form:¬(((P1∨P2)∧ (P3∨P4))∨ ((P2∨P3)∧ (P4∨¬P5)))

Can person 3 ride the bus?Can person 3 and person 4 ride the bus together?

This seems ...complicated.

We can program!!!!

We need a way to keep track!

Page 110: 70: Discrete Math and Probability.

Put them together..

Propositions:P1 - Person 1 rides the bus.P2 - Person 2 rides the bus.....

But we can’t have either of the following happen; That either person 1or person 2 ride the bus and person 3 or 4 ride the bus. Or thatperson 2 or person 3 ride the bus and that either person 4 ride thebus or person 5 doesn’t.

Propositional Form:¬(((P1∨P2)∧ (P3∨P4))∨ ((P2∨P3)∧ (P4∨¬P5)))

Can person 3 ride the bus?

Can person 3 and person 4 ride the bus together?

This seems ...complicated.

We can program!!!!

We need a way to keep track!

Page 111: 70: Discrete Math and Probability.

Put them together..

Propositions:P1 - Person 1 rides the bus.P2 - Person 2 rides the bus.....

But we can’t have either of the following happen; That either person 1or person 2 ride the bus and person 3 or 4 ride the bus. Or thatperson 2 or person 3 ride the bus and that either person 4 ride thebus or person 5 doesn’t.

Propositional Form:¬(((P1∨P2)∧ (P3∨P4))∨ ((P2∨P3)∧ (P4∨¬P5)))

Can person 3 ride the bus?Can person 3 and person 4 ride the bus together?

This seems ...complicated.

We can program!!!!

We need a way to keep track!

Page 112: 70: Discrete Math and Probability.

Put them together..

Propositions:P1 - Person 1 rides the bus.P2 - Person 2 rides the bus.....

But we can’t have either of the following happen; That either person 1or person 2 ride the bus and person 3 or 4 ride the bus. Or thatperson 2 or person 3 ride the bus and that either person 4 ride thebus or person 5 doesn’t.

Propositional Form:¬(((P1∨P2)∧ (P3∨P4))∨ ((P2∨P3)∧ (P4∨¬P5)))

Can person 3 ride the bus?Can person 3 and person 4 ride the bus together?

This seems ...complicated.

We can program!!!!

We need a way to keep track!

Page 113: 70: Discrete Math and Probability.

Put them together..

Propositions:P1 - Person 1 rides the bus.P2 - Person 2 rides the bus.....

But we can’t have either of the following happen; That either person 1or person 2 ride the bus and person 3 or 4 ride the bus. Or thatperson 2 or person 3 ride the bus and that either person 4 ride thebus or person 5 doesn’t.

Propositional Form:¬(((P1∨P2)∧ (P3∨P4))∨ ((P2∨P3)∧ (P4∨¬P5)))

Can person 3 ride the bus?Can person 3 and person 4 ride the bus together?

This seems ...

complicated.

We can program!!!!

We need a way to keep track!

Page 114: 70: Discrete Math and Probability.

Put them together..

Propositions:P1 - Person 1 rides the bus.P2 - Person 2 rides the bus.....

But we can’t have either of the following happen; That either person 1or person 2 ride the bus and person 3 or 4 ride the bus. Or thatperson 2 or person 3 ride the bus and that either person 4 ride thebus or person 5 doesn’t.

Propositional Form:¬(((P1∨P2)∧ (P3∨P4))∨ ((P2∨P3)∧ (P4∨¬P5)))

Can person 3 ride the bus?Can person 3 and person 4 ride the bus together?

This seems ...complicated.

We can program!!!!

We need a way to keep track!

Page 115: 70: Discrete Math and Probability.

Put them together..

Propositions:P1 - Person 1 rides the bus.P2 - Person 2 rides the bus.....

But we can’t have either of the following happen; That either person 1or person 2 ride the bus and person 3 or 4 ride the bus. Or thatperson 2 or person 3 ride the bus and that either person 4 ride thebus or person 5 doesn’t.

Propositional Form:¬(((P1∨P2)∧ (P3∨P4))∨ ((P2∨P3)∧ (P4∨¬P5)))

Can person 3 ride the bus?Can person 3 and person 4 ride the bus together?

This seems ...complicated.

We can program!!!!

We need a way to keep track!

Page 116: 70: Discrete Math and Probability.

Put them together..

Propositions:P1 - Person 1 rides the bus.P2 - Person 2 rides the bus.....

But we can’t have either of the following happen; That either person 1or person 2 ride the bus and person 3 or 4 ride the bus. Or thatperson 2 or person 3 ride the bus and that either person 4 ride thebus or person 5 doesn’t.

Propositional Form:¬(((P1∨P2)∧ (P3∨P4))∨ ((P2∨P3)∧ (P4∨¬P5)))

Can person 3 ride the bus?Can person 3 and person 4 ride the bus together?

This seems ...complicated.

We can program!!!!

We need a way to keep track!

Page 117: 70: Discrete Math and Probability.

Truth Tables for Propositional Forms.P Q P ∧QT T TT F

F

F T

F

F F

F

P Q P ∨QT T

T

T F

T

F T

T

F F

F

Notice: ∧ and ∨ are commutative.

One use for truth tables: Logical Equivalence of propositional forms!

Example: ¬(P ∧Q) logically equivalent to ¬P ∨¬Q

...because the two propositional forms have the same...

....Truth Table!

P Q ¬(P ∨Q) ¬P ∧¬QT T

F F

T F

F F

F T

F F

F F

T T

DeMorgan’s Law’s for Negation: distribute and flip!

¬(P ∧Q)

≡ ¬P ∨¬Q

¬(P ∨Q)

≡ ¬P ∧¬Q

Page 118: 70: Discrete Math and Probability.

Truth Tables for Propositional Forms.P Q P ∧QT T TT F FF T

F

F F

F

P Q P ∨QT T

T

T F

T

F T

T

F F

F

Notice: ∧ and ∨ are commutative.

One use for truth tables: Logical Equivalence of propositional forms!

Example: ¬(P ∧Q) logically equivalent to ¬P ∨¬Q

...because the two propositional forms have the same...

....Truth Table!

P Q ¬(P ∨Q) ¬P ∧¬QT T

F F

T F

F F

F T

F F

F F

T T

DeMorgan’s Law’s for Negation: distribute and flip!

¬(P ∧Q)

≡ ¬P ∨¬Q

¬(P ∨Q)

≡ ¬P ∧¬Q

Page 119: 70: Discrete Math and Probability.

Truth Tables for Propositional Forms.P Q P ∧QT T TT F FF T FF F

F

P Q P ∨QT T

T

T F

T

F T

T

F F

F

Notice: ∧ and ∨ are commutative.

One use for truth tables: Logical Equivalence of propositional forms!

Example: ¬(P ∧Q) logically equivalent to ¬P ∨¬Q

...because the two propositional forms have the same...

....Truth Table!

P Q ¬(P ∨Q) ¬P ∧¬QT T

F F

T F

F F

F T

F F

F F

T T

DeMorgan’s Law’s for Negation: distribute and flip!

¬(P ∧Q)

≡ ¬P ∨¬Q

¬(P ∨Q)

≡ ¬P ∧¬Q

Page 120: 70: Discrete Math and Probability.

Truth Tables for Propositional Forms.P Q P ∧QT T TT F FF T FF F F

P Q P ∨QT T

T

T F

T

F T

T

F F

F

Notice: ∧ and ∨ are commutative.

One use for truth tables: Logical Equivalence of propositional forms!

Example: ¬(P ∧Q) logically equivalent to ¬P ∨¬Q

...because the two propositional forms have the same...

....Truth Table!

P Q ¬(P ∨Q) ¬P ∧¬QT T

F F

T F

F F

F T

F F

F F

T T

DeMorgan’s Law’s for Negation: distribute and flip!

¬(P ∧Q)

≡ ¬P ∨¬Q

¬(P ∨Q)

≡ ¬P ∧¬Q

Page 121: 70: Discrete Math and Probability.

Truth Tables for Propositional Forms.P Q P ∧QT T TT F FF T FF F F

P Q P ∨QT T

T

T F

T

F T

T

F F

F

Notice: ∧ and ∨ are commutative.

One use for truth tables: Logical Equivalence of propositional forms!

Example: ¬(P ∧Q) logically equivalent to ¬P ∨¬Q

...because the two propositional forms have the same...

....Truth Table!

P Q ¬(P ∨Q) ¬P ∧¬QT T

F F

T F

F F

F T

F F

F F

T T

DeMorgan’s Law’s for Negation: distribute and flip!

¬(P ∧Q)

≡ ¬P ∨¬Q

¬(P ∨Q)

≡ ¬P ∧¬Q

Page 122: 70: Discrete Math and Probability.

Truth Tables for Propositional Forms.P Q P ∧QT T TT F FF T FF F F

P Q P ∨QT T TT F

T

F T

T

F F

F

Notice: ∧ and ∨ are commutative.

One use for truth tables: Logical Equivalence of propositional forms!

Example: ¬(P ∧Q) logically equivalent to ¬P ∨¬Q

...because the two propositional forms have the same...

....Truth Table!

P Q ¬(P ∨Q) ¬P ∧¬QT T

F F

T F

F F

F T

F F

F F

T T

DeMorgan’s Law’s for Negation: distribute and flip!

¬(P ∧Q)

≡ ¬P ∨¬Q

¬(P ∨Q)

≡ ¬P ∧¬Q

Page 123: 70: Discrete Math and Probability.

Truth Tables for Propositional Forms.P Q P ∧QT T TT F FF T FF F F

P Q P ∨QT T TT F TF T

T

F F

F

Notice: ∧ and ∨ are commutative.

One use for truth tables: Logical Equivalence of propositional forms!

Example: ¬(P ∧Q) logically equivalent to ¬P ∨¬Q

...because the two propositional forms have the same...

....Truth Table!

P Q ¬(P ∨Q) ¬P ∧¬QT T

F F

T F

F F

F T

F F

F F

T T

DeMorgan’s Law’s for Negation: distribute and flip!

¬(P ∧Q)

≡ ¬P ∨¬Q

¬(P ∨Q)

≡ ¬P ∧¬Q

Page 124: 70: Discrete Math and Probability.

Truth Tables for Propositional Forms.P Q P ∧QT T TT F FF T FF F F

P Q P ∨QT T TT F TF T TF F

F

Notice: ∧ and ∨ are commutative.

One use for truth tables: Logical Equivalence of propositional forms!

Example: ¬(P ∧Q) logically equivalent to ¬P ∨¬Q

...because the two propositional forms have the same...

....Truth Table!

P Q ¬(P ∨Q) ¬P ∧¬QT T

F F

T F

F F

F T

F F

F F

T T

DeMorgan’s Law’s for Negation: distribute and flip!

¬(P ∧Q)

≡ ¬P ∨¬Q

¬(P ∨Q)

≡ ¬P ∧¬Q

Page 125: 70: Discrete Math and Probability.

Truth Tables for Propositional Forms.P Q P ∧QT T TT F FF T FF F F

P Q P ∨QT T TT F TF T TF F F

Notice: ∧ and ∨ are commutative.

One use for truth tables: Logical Equivalence of propositional forms!

Example: ¬(P ∧Q) logically equivalent to ¬P ∨¬Q

...because the two propositional forms have the same...

....Truth Table!

P Q ¬(P ∨Q) ¬P ∧¬QT T

F F

T F

F F

F T

F F

F F

T T

DeMorgan’s Law’s for Negation: distribute and flip!

¬(P ∧Q)

≡ ¬P ∨¬Q

¬(P ∨Q)

≡ ¬P ∧¬Q

Page 126: 70: Discrete Math and Probability.

Truth Tables for Propositional Forms.P Q P ∧QT T TT F FF T FF F F

P Q P ∨QT T TT F TF T TF F F

Notice: ∧ and ∨ are commutative.

One use for truth tables: Logical Equivalence of propositional forms!

Example: ¬(P ∧Q) logically equivalent to ¬P ∨¬Q

...because the two propositional forms have the same...

....Truth Table!

P Q ¬(P ∨Q) ¬P ∧¬QT T

F F

T F

F F

F T

F F

F F

T T

DeMorgan’s Law’s for Negation: distribute and flip!

¬(P ∧Q)

≡ ¬P ∨¬Q

¬(P ∨Q)

≡ ¬P ∧¬Q

Page 127: 70: Discrete Math and Probability.

Truth Tables for Propositional Forms.P Q P ∧QT T TT F FF T FF F F

P Q P ∨QT T TT F TF T TF F F

Notice: ∧ and ∨ are commutative.

One use for truth tables: Logical Equivalence of propositional forms!

Example: ¬(P ∧Q) logically equivalent to ¬P ∨¬Q

...because the two propositional forms have the same...

....Truth Table!

P Q ¬(P ∨Q) ¬P ∧¬QT T

F F

T F

F F

F T

F F

F F

T T

DeMorgan’s Law’s for Negation: distribute and flip!

¬(P ∧Q)

≡ ¬P ∨¬Q

¬(P ∨Q)

≡ ¬P ∧¬Q

Page 128: 70: Discrete Math and Probability.

Truth Tables for Propositional Forms.P Q P ∧QT T TT F FF T FF F F

P Q P ∨QT T TT F TF T TF F F

Notice: ∧ and ∨ are commutative.

One use for truth tables: Logical Equivalence of propositional forms!

Example: ¬(P ∧Q) logically equivalent to ¬P ∨¬Q

...because the two propositional forms have the same...

....Truth Table!

P Q ¬(P ∨Q) ¬P ∧¬QT T

F F

T F

F F

F T

F F

F F

T T

DeMorgan’s Law’s for Negation: distribute and flip!

¬(P ∧Q)

≡ ¬P ∨¬Q

¬(P ∨Q)

≡ ¬P ∧¬Q

Page 129: 70: Discrete Math and Probability.

Truth Tables for Propositional Forms.P Q P ∧QT T TT F FF T FF F F

P Q P ∨QT T TT F TF T TF F F

Notice: ∧ and ∨ are commutative.

One use for truth tables: Logical Equivalence of propositional forms!

Example: ¬(P ∧Q) logically equivalent to ¬P ∨¬Q

...because the two propositional forms have the same...

....Truth Table!

P Q ¬(P ∨Q) ¬P ∧¬QT T

F F

T F

F F

F T

F F

F F

T T

DeMorgan’s Law’s for Negation: distribute and flip!

¬(P ∧Q)

≡ ¬P ∨¬Q

¬(P ∨Q)

≡ ¬P ∧¬Q

Page 130: 70: Discrete Math and Probability.

Truth Tables for Propositional Forms.P Q P ∧QT T TT F FF T FF F F

P Q P ∨QT T TT F TF T TF F F

Notice: ∧ and ∨ are commutative.

One use for truth tables: Logical Equivalence of propositional forms!

Example: ¬(P ∧Q) logically equivalent to ¬P ∨¬Q

...because the two propositional forms have the same...

....Truth Table!

P Q ¬(P ∨Q) ¬P ∧¬QT T F

F

T F

F F

F T

F F

F F

T T

DeMorgan’s Law’s for Negation: distribute and flip!

¬(P ∧Q)

≡ ¬P ∨¬Q

¬(P ∨Q)

≡ ¬P ∧¬Q

Page 131: 70: Discrete Math and Probability.

Truth Tables for Propositional Forms.P Q P ∧QT T TT F FF T FF F F

P Q P ∨QT T TT F TF T TF F F

Notice: ∧ and ∨ are commutative.

One use for truth tables: Logical Equivalence of propositional forms!

Example: ¬(P ∧Q) logically equivalent to ¬P ∨¬Q

...because the two propositional forms have the same...

....Truth Table!

P Q ¬(P ∨Q) ¬P ∧¬QT T F FT F

F F

F T

F F

F F

T T

DeMorgan’s Law’s for Negation: distribute and flip!

¬(P ∧Q)

≡ ¬P ∨¬Q

¬(P ∨Q)

≡ ¬P ∧¬Q

Page 132: 70: Discrete Math and Probability.

Truth Tables for Propositional Forms.P Q P ∧QT T TT F FF T FF F F

P Q P ∨QT T TT F TF T TF F F

Notice: ∧ and ∨ are commutative.

One use for truth tables: Logical Equivalence of propositional forms!

Example: ¬(P ∧Q) logically equivalent to ¬P ∨¬Q

...because the two propositional forms have the same...

....Truth Table!

P Q ¬(P ∨Q) ¬P ∧¬QT T F FT F F

F

F T

F F

F F

T T

DeMorgan’s Law’s for Negation: distribute and flip!

¬(P ∧Q)

≡ ¬P ∨¬Q

¬(P ∨Q)

≡ ¬P ∧¬Q

Page 133: 70: Discrete Math and Probability.

Truth Tables for Propositional Forms.P Q P ∧QT T TT F FF T FF F F

P Q P ∨QT T TT F TF T TF F F

Notice: ∧ and ∨ are commutative.

One use for truth tables: Logical Equivalence of propositional forms!

Example: ¬(P ∧Q) logically equivalent to ¬P ∨¬Q

...because the two propositional forms have the same...

....Truth Table!

P Q ¬(P ∨Q) ¬P ∧¬QT T F FT F F FF T

F F

F F

T T

DeMorgan’s Law’s for Negation: distribute and flip!

¬(P ∧Q)

≡ ¬P ∨¬Q

¬(P ∨Q)

≡ ¬P ∧¬Q

Page 134: 70: Discrete Math and Probability.

Truth Tables for Propositional Forms.P Q P ∧QT T TT F FF T FF F F

P Q P ∨QT T TT F TF T TF F F

Notice: ∧ and ∨ are commutative.

One use for truth tables: Logical Equivalence of propositional forms!

Example: ¬(P ∧Q) logically equivalent to ¬P ∨¬Q

...because the two propositional forms have the same...

....Truth Table!

P Q ¬(P ∨Q) ¬P ∧¬QT T F FT F F FF T F

F

F F

T T

DeMorgan’s Law’s for Negation: distribute and flip!

¬(P ∧Q)

≡ ¬P ∨¬Q

¬(P ∨Q)

≡ ¬P ∧¬Q

Page 135: 70: Discrete Math and Probability.

Truth Tables for Propositional Forms.P Q P ∧QT T TT F FF T FF F F

P Q P ∨QT T TT F TF T TF F F

Notice: ∧ and ∨ are commutative.

One use for truth tables: Logical Equivalence of propositional forms!

Example: ¬(P ∧Q) logically equivalent to ¬P ∨¬Q

...because the two propositional forms have the same...

....Truth Table!

P Q ¬(P ∨Q) ¬P ∧¬QT T F FT F F FF T F FF F

T T

DeMorgan’s Law’s for Negation: distribute and flip!

¬(P ∧Q)

≡ ¬P ∨¬Q

¬(P ∨Q)

≡ ¬P ∧¬Q

Page 136: 70: Discrete Math and Probability.

Truth Tables for Propositional Forms.P Q P ∧QT T TT F FF T FF F F

P Q P ∨QT T TT F TF T TF F F

Notice: ∧ and ∨ are commutative.

One use for truth tables: Logical Equivalence of propositional forms!

Example: ¬(P ∧Q) logically equivalent to ¬P ∨¬Q

...because the two propositional forms have the same...

....Truth Table!

P Q ¬(P ∨Q) ¬P ∧¬QT T F FT F F FF T F FF F T

T

DeMorgan’s Law’s for Negation: distribute and flip!

¬(P ∧Q)

≡ ¬P ∨¬Q

¬(P ∨Q)

≡ ¬P ∧¬Q

Page 137: 70: Discrete Math and Probability.

Truth Tables for Propositional Forms.P Q P ∧QT T TT F FF T FF F F

P Q P ∨QT T TT F TF T TF F F

Notice: ∧ and ∨ are commutative.

One use for truth tables: Logical Equivalence of propositional forms!

Example: ¬(P ∧Q) logically equivalent to ¬P ∨¬Q

...because the two propositional forms have the same...

....Truth Table!

P Q ¬(P ∨Q) ¬P ∧¬QT T F FT F F FF T F FF F T T

DeMorgan’s Law’s for Negation: distribute and flip!

¬(P ∧Q)

≡ ¬P ∨¬Q

¬(P ∨Q)

≡ ¬P ∧¬Q

Page 138: 70: Discrete Math and Probability.

Truth Tables for Propositional Forms.P Q P ∧QT T TT F FF T FF F F

P Q P ∨QT T TT F TF T TF F F

Notice: ∧ and ∨ are commutative.

One use for truth tables: Logical Equivalence of propositional forms!

Example: ¬(P ∧Q) logically equivalent to ¬P ∨¬Q

...because the two propositional forms have the same...

....Truth Table!

P Q ¬(P ∨Q) ¬P ∧¬QT T F FT F F FF T F FF F T T

DeMorgan’s Law’s for Negation: distribute and flip!

¬(P ∧Q)

≡ ¬P ∨¬Q ¬(P ∨Q)

≡ ¬P ∧¬Q

Page 139: 70: Discrete Math and Probability.

Truth Tables for Propositional Forms.P Q P ∧QT T TT F FF T FF F F

P Q P ∨QT T TT F TF T TF F F

Notice: ∧ and ∨ are commutative.

One use for truth tables: Logical Equivalence of propositional forms!

Example: ¬(P ∧Q) logically equivalent to ¬P ∨¬Q

...because the two propositional forms have the same...

....Truth Table!

P Q ¬(P ∨Q) ¬P ∧¬QT T F FT F F FF T F FF F T T

DeMorgan’s Law’s for Negation: distribute and flip!

¬(P ∧Q) ≡ ¬P ∨¬Q

¬(P ∨Q)

≡ ¬P ∧¬Q

Page 140: 70: Discrete Math and Probability.

Truth Tables for Propositional Forms.P Q P ∧QT T TT F FF T FF F F

P Q P ∨QT T TT F TF T TF F F

Notice: ∧ and ∨ are commutative.

One use for truth tables: Logical Equivalence of propositional forms!

Example: ¬(P ∧Q) logically equivalent to ¬P ∨¬Q

...because the two propositional forms have the same...

....Truth Table!

P Q ¬(P ∨Q) ¬P ∧¬QT T F FT F F FF T F FF F T T

DeMorgan’s Law’s for Negation: distribute and flip!

¬(P ∧Q) ≡ ¬P ∨¬Q ¬(P ∨Q)

≡ ¬P ∧¬Q

Page 141: 70: Discrete Math and Probability.

Truth Tables for Propositional Forms.P Q P ∧QT T TT F FF T FF F F

P Q P ∨QT T TT F TF T TF F F

Notice: ∧ and ∨ are commutative.

One use for truth tables: Logical Equivalence of propositional forms!

Example: ¬(P ∧Q) logically equivalent to ¬P ∨¬Q

...because the two propositional forms have the same...

....Truth Table!

P Q ¬(P ∨Q) ¬P ∧¬QT T F FT F F FF T F FF F T T

DeMorgan’s Law’s for Negation: distribute and flip!

¬(P ∧Q) ≡ ¬P ∨¬Q ¬(P ∨Q) ≡ ¬P ∧¬Q

Page 142: 70: Discrete Math and Probability.

Distributive?

P ∧ (Q∨R)≡ (P ∧Q)∨ (P ∧R)?

Simplify: (T ∧Q)≡Q, (F ∧Q)≡ F .

Cases:P is True .

LHS: T ∧ (Q∨R)≡ (Q∨R).RHS: (T ∧Q)∨ (T ∧R)≡ (Q∨R).

P is False .LHS: F ∧ (Q∨R)≡ F .RHS: (F ∧Q)∨ (F ∧R)≡ (F ∨F )≡ F .

P ∨ (Q∧R)≡ (P ∨Q)∧ (P ∨R)?

Simplify: T ∨Q ≡ T , F ∨Q ≡Q.

Foil 1:(A∨B)∧ (C∨D)≡ (A∧C)∨ (A∧D)∨ (B∧C)∨ (B∧D)?

Foil 2:(A∧B)∨ (C∧D)≡ (A∨C)∧ (A∨D)∧ (B∨C)∧ (B∨D)?

Page 143: 70: Discrete Math and Probability.

Distributive?

P ∧ (Q∨R)≡ (P ∧Q)∨ (P ∧R)?

Simplify: (T ∧Q)≡Q,

(F ∧Q)≡ F .

Cases:P is True .

LHS: T ∧ (Q∨R)≡ (Q∨R).RHS: (T ∧Q)∨ (T ∧R)≡ (Q∨R).

P is False .LHS: F ∧ (Q∨R)≡ F .RHS: (F ∧Q)∨ (F ∧R)≡ (F ∨F )≡ F .

P ∨ (Q∧R)≡ (P ∨Q)∧ (P ∨R)?

Simplify: T ∨Q ≡ T , F ∨Q ≡Q.

Foil 1:(A∨B)∧ (C∨D)≡ (A∧C)∨ (A∧D)∨ (B∧C)∨ (B∧D)?

Foil 2:(A∧B)∨ (C∧D)≡ (A∨C)∧ (A∨D)∧ (B∨C)∧ (B∨D)?

Page 144: 70: Discrete Math and Probability.

Distributive?

P ∧ (Q∨R)≡ (P ∧Q)∨ (P ∧R)?

Simplify: (T ∧Q)≡Q, (F ∧Q)≡ F .

Cases:P is True .

LHS: T ∧ (Q∨R)≡ (Q∨R).RHS: (T ∧Q)∨ (T ∧R)≡ (Q∨R).

P is False .LHS: F ∧ (Q∨R)≡ F .RHS: (F ∧Q)∨ (F ∧R)≡ (F ∨F )≡ F .

P ∨ (Q∧R)≡ (P ∨Q)∧ (P ∨R)?

Simplify: T ∨Q ≡ T , F ∨Q ≡Q.

Foil 1:(A∨B)∧ (C∨D)≡ (A∧C)∨ (A∧D)∨ (B∧C)∨ (B∧D)?

Foil 2:(A∧B)∨ (C∧D)≡ (A∨C)∧ (A∨D)∧ (B∨C)∧ (B∨D)?

Page 145: 70: Discrete Math and Probability.

Distributive?

P ∧ (Q∨R)≡ (P ∧Q)∨ (P ∧R)?

Simplify: (T ∧Q)≡Q, (F ∧Q)≡ F .

Cases:P is True .

LHS: T ∧ (Q∨R)

≡ (Q∨R).RHS: (T ∧Q)∨ (T ∧R)≡ (Q∨R).

P is False .LHS: F ∧ (Q∨R)≡ F .RHS: (F ∧Q)∨ (F ∧R)≡ (F ∨F )≡ F .

P ∨ (Q∧R)≡ (P ∨Q)∧ (P ∨R)?

Simplify: T ∨Q ≡ T , F ∨Q ≡Q.

Foil 1:(A∨B)∧ (C∨D)≡ (A∧C)∨ (A∧D)∨ (B∧C)∨ (B∧D)?

Foil 2:(A∧B)∨ (C∧D)≡ (A∨C)∧ (A∨D)∧ (B∨C)∧ (B∨D)?

Page 146: 70: Discrete Math and Probability.

Distributive?

P ∧ (Q∨R)≡ (P ∧Q)∨ (P ∧R)?

Simplify: (T ∧Q)≡Q, (F ∧Q)≡ F .

Cases:P is True .

LHS: T ∧ (Q∨R)≡ (Q∨R).

RHS: (T ∧Q)∨ (T ∧R)≡ (Q∨R).P is False .

LHS: F ∧ (Q∨R)≡ F .RHS: (F ∧Q)∨ (F ∧R)≡ (F ∨F )≡ F .

P ∨ (Q∧R)≡ (P ∨Q)∧ (P ∨R)?

Simplify: T ∨Q ≡ T , F ∨Q ≡Q.

Foil 1:(A∨B)∧ (C∨D)≡ (A∧C)∨ (A∧D)∨ (B∧C)∨ (B∧D)?

Foil 2:(A∧B)∨ (C∧D)≡ (A∨C)∧ (A∨D)∧ (B∨C)∧ (B∨D)?

Page 147: 70: Discrete Math and Probability.

Distributive?

P ∧ (Q∨R)≡ (P ∧Q)∨ (P ∧R)?

Simplify: (T ∧Q)≡Q, (F ∧Q)≡ F .

Cases:P is True .

LHS: T ∧ (Q∨R)≡ (Q∨R).RHS: (T ∧Q)∨ (T ∧R)

≡ (Q∨R).P is False .

LHS: F ∧ (Q∨R)≡ F .RHS: (F ∧Q)∨ (F ∧R)≡ (F ∨F )≡ F .

P ∨ (Q∧R)≡ (P ∨Q)∧ (P ∨R)?

Simplify: T ∨Q ≡ T , F ∨Q ≡Q.

Foil 1:(A∨B)∧ (C∨D)≡ (A∧C)∨ (A∧D)∨ (B∧C)∨ (B∧D)?

Foil 2:(A∧B)∨ (C∧D)≡ (A∨C)∧ (A∨D)∧ (B∨C)∧ (B∨D)?

Page 148: 70: Discrete Math and Probability.

Distributive?

P ∧ (Q∨R)≡ (P ∧Q)∨ (P ∧R)?

Simplify: (T ∧Q)≡Q, (F ∧Q)≡ F .

Cases:P is True .

LHS: T ∧ (Q∨R)≡ (Q∨R).RHS: (T ∧Q)∨ (T ∧R)≡ (Q∨R).

P is False .LHS: F ∧ (Q∨R)≡ F .RHS: (F ∧Q)∨ (F ∧R)≡ (F ∨F )≡ F .

P ∨ (Q∧R)≡ (P ∨Q)∧ (P ∨R)?

Simplify: T ∨Q ≡ T , F ∨Q ≡Q.

Foil 1:(A∨B)∧ (C∨D)≡ (A∧C)∨ (A∧D)∨ (B∧C)∨ (B∧D)?

Foil 2:(A∧B)∨ (C∧D)≡ (A∨C)∧ (A∨D)∧ (B∨C)∧ (B∨D)?

Page 149: 70: Discrete Math and Probability.

Distributive?

P ∧ (Q∨R)≡ (P ∧Q)∨ (P ∧R)?

Simplify: (T ∧Q)≡Q, (F ∧Q)≡ F .

Cases:P is True .

LHS: T ∧ (Q∨R)≡ (Q∨R).RHS: (T ∧Q)∨ (T ∧R)≡ (Q∨R).

P is False .

LHS: F ∧ (Q∨R)≡ F .RHS: (F ∧Q)∨ (F ∧R)≡ (F ∨F )≡ F .

P ∨ (Q∧R)≡ (P ∨Q)∧ (P ∨R)?

Simplify: T ∨Q ≡ T , F ∨Q ≡Q.

Foil 1:(A∨B)∧ (C∨D)≡ (A∧C)∨ (A∧D)∨ (B∧C)∨ (B∧D)?

Foil 2:(A∧B)∨ (C∧D)≡ (A∨C)∧ (A∨D)∧ (B∨C)∧ (B∨D)?

Page 150: 70: Discrete Math and Probability.

Distributive?

P ∧ (Q∨R)≡ (P ∧Q)∨ (P ∧R)?

Simplify: (T ∧Q)≡Q, (F ∧Q)≡ F .

Cases:P is True .

LHS: T ∧ (Q∨R)≡ (Q∨R).RHS: (T ∧Q)∨ (T ∧R)≡ (Q∨R).

P is False .LHS: F ∧ (Q∨R)

≡ F .RHS: (F ∧Q)∨ (F ∧R)≡ (F ∨F )≡ F .

P ∨ (Q∧R)≡ (P ∨Q)∧ (P ∨R)?

Simplify: T ∨Q ≡ T , F ∨Q ≡Q.

Foil 1:(A∨B)∧ (C∨D)≡ (A∧C)∨ (A∧D)∨ (B∧C)∨ (B∧D)?

Foil 2:(A∧B)∨ (C∧D)≡ (A∨C)∧ (A∨D)∧ (B∨C)∧ (B∨D)?

Page 151: 70: Discrete Math and Probability.

Distributive?

P ∧ (Q∨R)≡ (P ∧Q)∨ (P ∧R)?

Simplify: (T ∧Q)≡Q, (F ∧Q)≡ F .

Cases:P is True .

LHS: T ∧ (Q∨R)≡ (Q∨R).RHS: (T ∧Q)∨ (T ∧R)≡ (Q∨R).

P is False .LHS: F ∧ (Q∨R)≡ F .

RHS: (F ∧Q)∨ (F ∧R)≡ (F ∨F )≡ F .

P ∨ (Q∧R)≡ (P ∨Q)∧ (P ∨R)?

Simplify: T ∨Q ≡ T , F ∨Q ≡Q.

Foil 1:(A∨B)∧ (C∨D)≡ (A∧C)∨ (A∧D)∨ (B∧C)∨ (B∧D)?

Foil 2:(A∧B)∨ (C∧D)≡ (A∨C)∧ (A∨D)∧ (B∨C)∧ (B∨D)?

Page 152: 70: Discrete Math and Probability.

Distributive?

P ∧ (Q∨R)≡ (P ∧Q)∨ (P ∧R)?

Simplify: (T ∧Q)≡Q, (F ∧Q)≡ F .

Cases:P is True .

LHS: T ∧ (Q∨R)≡ (Q∨R).RHS: (T ∧Q)∨ (T ∧R)≡ (Q∨R).

P is False .LHS: F ∧ (Q∨R)≡ F .RHS: (F ∧Q)∨ (F ∧R)

≡ (F ∨F )≡ F .

P ∨ (Q∧R)≡ (P ∨Q)∧ (P ∨R)?

Simplify: T ∨Q ≡ T , F ∨Q ≡Q.

Foil 1:(A∨B)∧ (C∨D)≡ (A∧C)∨ (A∧D)∨ (B∧C)∨ (B∧D)?

Foil 2:(A∧B)∨ (C∧D)≡ (A∨C)∧ (A∨D)∧ (B∨C)∧ (B∨D)?

Page 153: 70: Discrete Math and Probability.

Distributive?

P ∧ (Q∨R)≡ (P ∧Q)∨ (P ∧R)?

Simplify: (T ∧Q)≡Q, (F ∧Q)≡ F .

Cases:P is True .

LHS: T ∧ (Q∨R)≡ (Q∨R).RHS: (T ∧Q)∨ (T ∧R)≡ (Q∨R).

P is False .LHS: F ∧ (Q∨R)≡ F .RHS: (F ∧Q)∨ (F ∧R)≡ (F ∨F )

≡ F .

P ∨ (Q∧R)≡ (P ∨Q)∧ (P ∨R)?

Simplify: T ∨Q ≡ T , F ∨Q ≡Q.

Foil 1:(A∨B)∧ (C∨D)≡ (A∧C)∨ (A∧D)∨ (B∧C)∨ (B∧D)?

Foil 2:(A∧B)∨ (C∧D)≡ (A∨C)∧ (A∨D)∧ (B∨C)∧ (B∨D)?

Page 154: 70: Discrete Math and Probability.

Distributive?

P ∧ (Q∨R)≡ (P ∧Q)∨ (P ∧R)?

Simplify: (T ∧Q)≡Q, (F ∧Q)≡ F .

Cases:P is True .

LHS: T ∧ (Q∨R)≡ (Q∨R).RHS: (T ∧Q)∨ (T ∧R)≡ (Q∨R).

P is False .LHS: F ∧ (Q∨R)≡ F .RHS: (F ∧Q)∨ (F ∧R)≡ (F ∨F )≡ F .

P ∨ (Q∧R)≡ (P ∨Q)∧ (P ∨R)?

Simplify: T ∨Q ≡ T , F ∨Q ≡Q.

Foil 1:(A∨B)∧ (C∨D)≡ (A∧C)∨ (A∧D)∨ (B∧C)∨ (B∧D)?

Foil 2:(A∧B)∨ (C∧D)≡ (A∨C)∧ (A∨D)∧ (B∨C)∧ (B∨D)?

Page 155: 70: Discrete Math and Probability.

Distributive?

P ∧ (Q∨R)≡ (P ∧Q)∨ (P ∧R)?

Simplify: (T ∧Q)≡Q, (F ∧Q)≡ F .

Cases:P is True .

LHS: T ∧ (Q∨R)≡ (Q∨R).RHS: (T ∧Q)∨ (T ∧R)≡ (Q∨R).

P is False .LHS: F ∧ (Q∨R)≡ F .RHS: (F ∧Q)∨ (F ∧R)≡ (F ∨F )≡ F .

P ∨ (Q∧R)≡ (P ∨Q)∧ (P ∨R)?

Simplify: T ∨Q ≡ T , F ∨Q ≡Q.

Foil 1:(A∨B)∧ (C∨D)≡ (A∧C)∨ (A∧D)∨ (B∧C)∨ (B∧D)?

Foil 2:(A∧B)∨ (C∧D)≡ (A∨C)∧ (A∨D)∧ (B∨C)∧ (B∨D)?

Page 156: 70: Discrete Math and Probability.

Distributive?

P ∧ (Q∨R)≡ (P ∧Q)∨ (P ∧R)?

Simplify: (T ∧Q)≡Q, (F ∧Q)≡ F .

Cases:P is True .

LHS: T ∧ (Q∨R)≡ (Q∨R).RHS: (T ∧Q)∨ (T ∧R)≡ (Q∨R).

P is False .LHS: F ∧ (Q∨R)≡ F .RHS: (F ∧Q)∨ (F ∧R)≡ (F ∨F )≡ F .

P ∨ (Q∧R)≡ (P ∨Q)∧ (P ∨R)?

Simplify: T ∨Q ≡ T , F ∨Q ≡Q.

Foil 1:(A∨B)∧ (C∨D)≡ (A∧C)∨ (A∧D)∨ (B∧C)∨ (B∧D)?

Foil 2:(A∧B)∨ (C∧D)≡ (A∨C)∧ (A∨D)∧ (B∨C)∧ (B∨D)?

Page 157: 70: Discrete Math and Probability.

Distributive?

P ∧ (Q∨R)≡ (P ∧Q)∨ (P ∧R)?

Simplify: (T ∧Q)≡Q, (F ∧Q)≡ F .

Cases:P is True .

LHS: T ∧ (Q∨R)≡ (Q∨R).RHS: (T ∧Q)∨ (T ∧R)≡ (Q∨R).

P is False .LHS: F ∧ (Q∨R)≡ F .RHS: (F ∧Q)∨ (F ∧R)≡ (F ∨F )≡ F .

P ∨ (Q∧R)≡ (P ∨Q)∧ (P ∨R)?

Simplify: T ∨Q ≡ T ,

F ∨Q ≡Q.

Foil 1:(A∨B)∧ (C∨D)≡ (A∧C)∨ (A∧D)∨ (B∧C)∨ (B∧D)?

Foil 2:(A∧B)∨ (C∧D)≡ (A∨C)∧ (A∨D)∧ (B∨C)∧ (B∨D)?

Page 158: 70: Discrete Math and Probability.

Distributive?

P ∧ (Q∨R)≡ (P ∧Q)∨ (P ∧R)?

Simplify: (T ∧Q)≡Q, (F ∧Q)≡ F .

Cases:P is True .

LHS: T ∧ (Q∨R)≡ (Q∨R).RHS: (T ∧Q)∨ (T ∧R)≡ (Q∨R).

P is False .LHS: F ∧ (Q∨R)≡ F .RHS: (F ∧Q)∨ (F ∧R)≡ (F ∨F )≡ F .

P ∨ (Q∧R)≡ (P ∨Q)∧ (P ∨R)?

Simplify: T ∨Q ≡ T , F ∨Q ≡Q.

Foil 1:(A∨B)∧ (C∨D)≡ (A∧C)∨ (A∧D)∨ (B∧C)∨ (B∧D)?

Foil 2:(A∧B)∨ (C∧D)≡ (A∨C)∧ (A∨D)∧ (B∨C)∧ (B∨D)?

Page 159: 70: Discrete Math and Probability.

Distributive?

P ∧ (Q∨R)≡ (P ∧Q)∨ (P ∧R)?

Simplify: (T ∧Q)≡Q, (F ∧Q)≡ F .

Cases:P is True .

LHS: T ∧ (Q∨R)≡ (Q∨R).RHS: (T ∧Q)∨ (T ∧R)≡ (Q∨R).

P is False .LHS: F ∧ (Q∨R)≡ F .RHS: (F ∧Q)∨ (F ∧R)≡ (F ∨F )≡ F .

P ∨ (Q∧R)≡ (P ∨Q)∧ (P ∨R)?

Simplify: T ∨Q ≡ T , F ∨Q ≡Q.

Foil 1:

(A∨B)∧ (C∨D)≡ (A∧C)∨ (A∧D)∨ (B∧C)∨ (B∧D)?

Foil 2:(A∧B)∨ (C∧D)≡ (A∨C)∧ (A∨D)∧ (B∨C)∧ (B∨D)?

Page 160: 70: Discrete Math and Probability.

Distributive?

P ∧ (Q∨R)≡ (P ∧Q)∨ (P ∧R)?

Simplify: (T ∧Q)≡Q, (F ∧Q)≡ F .

Cases:P is True .

LHS: T ∧ (Q∨R)≡ (Q∨R).RHS: (T ∧Q)∨ (T ∧R)≡ (Q∨R).

P is False .LHS: F ∧ (Q∨R)≡ F .RHS: (F ∧Q)∨ (F ∧R)≡ (F ∨F )≡ F .

P ∨ (Q∧R)≡ (P ∨Q)∧ (P ∨R)?

Simplify: T ∨Q ≡ T , F ∨Q ≡Q.

Foil 1:(A∨B)∧ (C∨D)≡ (A∧C)∨ (A∧D)∨ (B∧C)∨ (B∧D)?

Foil 2:(A∧B)∨ (C∧D)≡ (A∨C)∧ (A∨D)∧ (B∨C)∧ (B∨D)?

Page 161: 70: Discrete Math and Probability.

Distributive?

P ∧ (Q∨R)≡ (P ∧Q)∨ (P ∧R)?

Simplify: (T ∧Q)≡Q, (F ∧Q)≡ F .

Cases:P is True .

LHS: T ∧ (Q∨R)≡ (Q∨R).RHS: (T ∧Q)∨ (T ∧R)≡ (Q∨R).

P is False .LHS: F ∧ (Q∨R)≡ F .RHS: (F ∧Q)∨ (F ∧R)≡ (F ∨F )≡ F .

P ∨ (Q∧R)≡ (P ∨Q)∧ (P ∨R)?

Simplify: T ∨Q ≡ T , F ∨Q ≡Q.

Foil 1:(A∨B)∧ (C∨D)≡ (A∧C)∨ (A∧D)∨ (B∧C)∨ (B∧D)?

Foil 2:

(A∧B)∨ (C∧D)≡ (A∨C)∧ (A∨D)∧ (B∨C)∧ (B∨D)?

Page 162: 70: Discrete Math and Probability.

Distributive?

P ∧ (Q∨R)≡ (P ∧Q)∨ (P ∧R)?

Simplify: (T ∧Q)≡Q, (F ∧Q)≡ F .

Cases:P is True .

LHS: T ∧ (Q∨R)≡ (Q∨R).RHS: (T ∧Q)∨ (T ∧R)≡ (Q∨R).

P is False .LHS: F ∧ (Q∨R)≡ F .RHS: (F ∧Q)∨ (F ∧R)≡ (F ∨F )≡ F .

P ∨ (Q∧R)≡ (P ∨Q)∧ (P ∨R)?

Simplify: T ∨Q ≡ T , F ∨Q ≡Q.

Foil 1:(A∨B)∧ (C∨D)≡ (A∧C)∨ (A∧D)∨ (B∧C)∨ (B∧D)?

Foil 2:(A∧B)∨ (C∧D)≡ (A∨C)∧ (A∨D)∧ (B∨C)∧ (B∨D)?

Page 163: 70: Discrete Math and Probability.

Implication.

P =⇒ Q interpreted as

If P, then Q.

True Statements: P, P =⇒ Q.Conclude: Q is true.

Examples:

Statement: If you stand in the rain, then you’ll get wet.P = “you stand in the rain”Q = “you will get wet”

Statement: “Stand in the rain”Can conclude: “you’ll get wet.”

Statement: If a right triangle has sidelengths a≤ b ≤ c, thena2 +b2 = c2.

P = “a right triangle has sidelengths a≤ b ≤ c”,Q = “a2 +b2 = c2”.

Page 164: 70: Discrete Math and Probability.

Implication.

P =⇒ Q interpreted as

If P, then Q.

True Statements: P, P =⇒ Q.Conclude: Q is true.

Examples:

Statement: If you stand in the rain, then you’ll get wet.P = “you stand in the rain”Q = “you will get wet”

Statement: “Stand in the rain”Can conclude: “you’ll get wet.”

Statement: If a right triangle has sidelengths a≤ b ≤ c, thena2 +b2 = c2.

P = “a right triangle has sidelengths a≤ b ≤ c”,Q = “a2 +b2 = c2”.

Page 165: 70: Discrete Math and Probability.

Implication.

P =⇒ Q interpreted as

If P, then Q.

True Statements: P, P =⇒ Q.Conclude: Q is true.

Examples:

Statement: If you stand in the rain, then you’ll get wet.P = “you stand in the rain”Q = “you will get wet”

Statement: “Stand in the rain”Can conclude: “you’ll get wet.”

Statement: If a right triangle has sidelengths a≤ b ≤ c, thena2 +b2 = c2.

P = “a right triangle has sidelengths a≤ b ≤ c”,Q = “a2 +b2 = c2”.

Page 166: 70: Discrete Math and Probability.

Implication.

P =⇒ Q interpreted as

If P, then Q.

True Statements: P, P =⇒ Q.

Conclude: Q is true.

Examples:

Statement: If you stand in the rain, then you’ll get wet.P = “you stand in the rain”Q = “you will get wet”

Statement: “Stand in the rain”Can conclude: “you’ll get wet.”

Statement: If a right triangle has sidelengths a≤ b ≤ c, thena2 +b2 = c2.

P = “a right triangle has sidelengths a≤ b ≤ c”,Q = “a2 +b2 = c2”.

Page 167: 70: Discrete Math and Probability.

Implication.

P =⇒ Q interpreted as

If P, then Q.

True Statements: P, P =⇒ Q.Conclude: Q is true.

Examples:

Statement: If you stand in the rain, then you’ll get wet.P = “you stand in the rain”Q = “you will get wet”

Statement: “Stand in the rain”Can conclude: “you’ll get wet.”

Statement: If a right triangle has sidelengths a≤ b ≤ c, thena2 +b2 = c2.

P = “a right triangle has sidelengths a≤ b ≤ c”,Q = “a2 +b2 = c2”.

Page 168: 70: Discrete Math and Probability.

Implication.

P =⇒ Q interpreted as

If P, then Q.

True Statements: P, P =⇒ Q.Conclude: Q is true.

Examples:

Statement: If you stand in the rain, then you’ll get wet.P = “you stand in the rain”Q = “you will get wet”

Statement: “Stand in the rain”Can conclude: “you’ll get wet.”

Statement: If a right triangle has sidelengths a≤ b ≤ c, thena2 +b2 = c2.

P = “a right triangle has sidelengths a≤ b ≤ c”,Q = “a2 +b2 = c2”.

Page 169: 70: Discrete Math and Probability.

Implication.

P =⇒ Q interpreted as

If P, then Q.

True Statements: P, P =⇒ Q.Conclude: Q is true.

Examples:

Statement: If you stand in the rain, then you’ll get wet.

P = “you stand in the rain”Q = “you will get wet”

Statement: “Stand in the rain”Can conclude: “you’ll get wet.”

Statement: If a right triangle has sidelengths a≤ b ≤ c, thena2 +b2 = c2.

P = “a right triangle has sidelengths a≤ b ≤ c”,Q = “a2 +b2 = c2”.

Page 170: 70: Discrete Math and Probability.

Implication.

P =⇒ Q interpreted as

If P, then Q.

True Statements: P, P =⇒ Q.Conclude: Q is true.

Examples:

Statement: If you stand in the rain, then you’ll get wet.P = “you stand in the rain”

Q = “you will get wet”Statement: “Stand in the rain”Can conclude: “you’ll get wet.”

Statement: If a right triangle has sidelengths a≤ b ≤ c, thena2 +b2 = c2.

P = “a right triangle has sidelengths a≤ b ≤ c”,Q = “a2 +b2 = c2”.

Page 171: 70: Discrete Math and Probability.

Implication.

P =⇒ Q interpreted as

If P, then Q.

True Statements: P, P =⇒ Q.Conclude: Q is true.

Examples:

Statement: If you stand in the rain, then you’ll get wet.P = “you stand in the rain”Q = “you will get wet”

Statement: “Stand in the rain”Can conclude: “you’ll get wet.”

Statement: If a right triangle has sidelengths a≤ b ≤ c, thena2 +b2 = c2.

P = “a right triangle has sidelengths a≤ b ≤ c”,Q = “a2 +b2 = c2”.

Page 172: 70: Discrete Math and Probability.

Implication.

P =⇒ Q interpreted as

If P, then Q.

True Statements: P, P =⇒ Q.Conclude: Q is true.

Examples:

Statement: If you stand in the rain, then you’ll get wet.P = “you stand in the rain”Q = “you will get wet”

Statement: “Stand in the rain”

Can conclude: “you’ll get wet.”

Statement: If a right triangle has sidelengths a≤ b ≤ c, thena2 +b2 = c2.

P = “a right triangle has sidelengths a≤ b ≤ c”,Q = “a2 +b2 = c2”.

Page 173: 70: Discrete Math and Probability.

Implication.

P =⇒ Q interpreted as

If P, then Q.

True Statements: P, P =⇒ Q.Conclude: Q is true.

Examples:

Statement: If you stand in the rain, then you’ll get wet.P = “you stand in the rain”Q = “you will get wet”

Statement: “Stand in the rain”Can conclude: “you’ll get wet.”

Statement: If a right triangle has sidelengths a≤ b ≤ c, thena2 +b2 = c2.

P = “a right triangle has sidelengths a≤ b ≤ c”,Q = “a2 +b2 = c2”.

Page 174: 70: Discrete Math and Probability.

Implication.

P =⇒ Q interpreted as

If P, then Q.

True Statements: P, P =⇒ Q.Conclude: Q is true.

Examples:

Statement: If you stand in the rain, then you’ll get wet.P = “you stand in the rain”Q = “you will get wet”

Statement: “Stand in the rain”Can conclude: “you’ll get wet.”

Statement: If a right triangle has sidelengths a≤ b ≤ c, thena2 +b2 = c2.

P = “a right triangle has sidelengths a≤ b ≤ c”,Q = “a2 +b2 = c2”.

Page 175: 70: Discrete Math and Probability.

Implication.

P =⇒ Q interpreted as

If P, then Q.

True Statements: P, P =⇒ Q.Conclude: Q is true.

Examples:

Statement: If you stand in the rain, then you’ll get wet.P = “you stand in the rain”Q = “you will get wet”

Statement: “Stand in the rain”Can conclude: “you’ll get wet.”

Statement: If a right triangle has sidelengths a≤ b ≤ c, thena2 +b2 = c2.

P = “a right triangle has sidelengths a≤ b ≤ c”,

Q = “a2 +b2 = c2”.

Page 176: 70: Discrete Math and Probability.

Implication.

P =⇒ Q interpreted as

If P, then Q.

True Statements: P, P =⇒ Q.Conclude: Q is true.

Examples:

Statement: If you stand in the rain, then you’ll get wet.P = “you stand in the rain”Q = “you will get wet”

Statement: “Stand in the rain”Can conclude: “you’ll get wet.”

Statement: If a right triangle has sidelengths a≤ b ≤ c, thena2 +b2 = c2.

P = “a right triangle has sidelengths a≤ b ≤ c”,Q = “a2 +b2 = c2”.

Page 177: 70: Discrete Math and Probability.

Non-Consequences/consequences of ImplicationThe statement “P =⇒ Q”

only is False if P is True and Q is False .

False implies nothingP False means Q can be True or FalseAnything implies true.P can be True or False when Q is True

If chemical plant pollutes river, fish die.If fish die, did chemical plant pollute river?

Not necessarily.

P =⇒ Q and Q are True does not mean P is True

Be careful!

Instead we have:P =⇒ Q and P are True does mean Q is True .

The chemical plant pollutes river. Can we conclude fish die?

Some Fun: use propositional formulas to describe implication?((P =⇒ Q)∧P) =⇒ Q.

Page 178: 70: Discrete Math and Probability.

Non-Consequences/consequences of ImplicationThe statement “P =⇒ Q”

only is False if P is True and Q is False .

False implies nothingP False means Q can be True or FalseAnything implies true.P can be True or False when Q is True

If chemical plant pollutes river, fish die.If fish die, did chemical plant pollute river?

Not necessarily.

P =⇒ Q and Q are True does not mean P is True

Be careful!

Instead we have:P =⇒ Q and P are True does mean Q is True .

The chemical plant pollutes river. Can we conclude fish die?

Some Fun: use propositional formulas to describe implication?((P =⇒ Q)∧P) =⇒ Q.

Page 179: 70: Discrete Math and Probability.

Non-Consequences/consequences of ImplicationThe statement “P =⇒ Q”

only is False if P is True and Q is False .

False implies nothing

P False means Q can be True or FalseAnything implies true.P can be True or False when Q is True

If chemical plant pollutes river, fish die.If fish die, did chemical plant pollute river?

Not necessarily.

P =⇒ Q and Q are True does not mean P is True

Be careful!

Instead we have:P =⇒ Q and P are True does mean Q is True .

The chemical plant pollutes river. Can we conclude fish die?

Some Fun: use propositional formulas to describe implication?((P =⇒ Q)∧P) =⇒ Q.

Page 180: 70: Discrete Math and Probability.

Non-Consequences/consequences of ImplicationThe statement “P =⇒ Q”

only is False if P is True and Q is False .

False implies nothingP False means

Q can be True or FalseAnything implies true.P can be True or False when Q is True

If chemical plant pollutes river, fish die.If fish die, did chemical plant pollute river?

Not necessarily.

P =⇒ Q and Q are True does not mean P is True

Be careful!

Instead we have:P =⇒ Q and P are True does mean Q is True .

The chemical plant pollutes river. Can we conclude fish die?

Some Fun: use propositional formulas to describe implication?((P =⇒ Q)∧P) =⇒ Q.

Page 181: 70: Discrete Math and Probability.

Non-Consequences/consequences of ImplicationThe statement “P =⇒ Q”

only is False if P is True and Q is False .

False implies nothingP False means Q can be True

or FalseAnything implies true.P can be True or False when Q is True

If chemical plant pollutes river, fish die.If fish die, did chemical plant pollute river?

Not necessarily.

P =⇒ Q and Q are True does not mean P is True

Be careful!

Instead we have:P =⇒ Q and P are True does mean Q is True .

The chemical plant pollutes river. Can we conclude fish die?

Some Fun: use propositional formulas to describe implication?((P =⇒ Q)∧P) =⇒ Q.

Page 182: 70: Discrete Math and Probability.

Non-Consequences/consequences of ImplicationThe statement “P =⇒ Q”

only is False if P is True and Q is False .

False implies nothingP False means Q can be True or False

Anything implies true.P can be True or False when Q is True

If chemical plant pollutes river, fish die.If fish die, did chemical plant pollute river?

Not necessarily.

P =⇒ Q and Q are True does not mean P is True

Be careful!

Instead we have:P =⇒ Q and P are True does mean Q is True .

The chemical plant pollutes river. Can we conclude fish die?

Some Fun: use propositional formulas to describe implication?((P =⇒ Q)∧P) =⇒ Q.

Page 183: 70: Discrete Math and Probability.

Non-Consequences/consequences of ImplicationThe statement “P =⇒ Q”

only is False if P is True and Q is False .

False implies nothingP False means Q can be True or FalseAnything implies true.

P can be True or False when Q is True

If chemical plant pollutes river, fish die.If fish die, did chemical plant pollute river?

Not necessarily.

P =⇒ Q and Q are True does not mean P is True

Be careful!

Instead we have:P =⇒ Q and P are True does mean Q is True .

The chemical plant pollutes river. Can we conclude fish die?

Some Fun: use propositional formulas to describe implication?((P =⇒ Q)∧P) =⇒ Q.

Page 184: 70: Discrete Math and Probability.

Non-Consequences/consequences of ImplicationThe statement “P =⇒ Q”

only is False if P is True and Q is False .

False implies nothingP False means Q can be True or FalseAnything implies true.P can be True or False when

Q is True

If chemical plant pollutes river, fish die.If fish die, did chemical plant pollute river?

Not necessarily.

P =⇒ Q and Q are True does not mean P is True

Be careful!

Instead we have:P =⇒ Q and P are True does mean Q is True .

The chemical plant pollutes river. Can we conclude fish die?

Some Fun: use propositional formulas to describe implication?((P =⇒ Q)∧P) =⇒ Q.

Page 185: 70: Discrete Math and Probability.

Non-Consequences/consequences of ImplicationThe statement “P =⇒ Q”

only is False if P is True and Q is False .

False implies nothingP False means Q can be True or FalseAnything implies true.P can be True or False when Q is True

If chemical plant pollutes river, fish die.If fish die, did chemical plant pollute river?

Not necessarily.

P =⇒ Q and Q are True does not mean P is True

Be careful!

Instead we have:P =⇒ Q and P are True does mean Q is True .

The chemical plant pollutes river. Can we conclude fish die?

Some Fun: use propositional formulas to describe implication?((P =⇒ Q)∧P) =⇒ Q.

Page 186: 70: Discrete Math and Probability.

Non-Consequences/consequences of ImplicationThe statement “P =⇒ Q”

only is False if P is True and Q is False .

False implies nothingP False means Q can be True or FalseAnything implies true.P can be True or False when Q is True

If chemical plant pollutes river, fish die.

If fish die, did chemical plant pollute river?

Not necessarily.

P =⇒ Q and Q are True does not mean P is True

Be careful!

Instead we have:P =⇒ Q and P are True does mean Q is True .

The chemical plant pollutes river. Can we conclude fish die?

Some Fun: use propositional formulas to describe implication?((P =⇒ Q)∧P) =⇒ Q.

Page 187: 70: Discrete Math and Probability.

Non-Consequences/consequences of ImplicationThe statement “P =⇒ Q”

only is False if P is True and Q is False .

False implies nothingP False means Q can be True or FalseAnything implies true.P can be True or False when Q is True

If chemical plant pollutes river, fish die.If fish die, did chemical plant pollute river?

Not necessarily.

P =⇒ Q and Q are True does not mean P is True

Be careful!

Instead we have:P =⇒ Q and P are True does mean Q is True .

The chemical plant pollutes river. Can we conclude fish die?

Some Fun: use propositional formulas to describe implication?((P =⇒ Q)∧P) =⇒ Q.

Page 188: 70: Discrete Math and Probability.

Non-Consequences/consequences of ImplicationThe statement “P =⇒ Q”

only is False if P is True and Q is False .

False implies nothingP False means Q can be True or FalseAnything implies true.P can be True or False when Q is True

If chemical plant pollutes river, fish die.If fish die, did chemical plant pollute river?

Not necessarily.

P =⇒ Q and Q are True does not mean P is True

Be careful!

Instead we have:P =⇒ Q and P are True does mean Q is True .

The chemical plant pollutes river. Can we conclude fish die?

Some Fun: use propositional formulas to describe implication?((P =⇒ Q)∧P) =⇒ Q.

Page 189: 70: Discrete Math and Probability.

Non-Consequences/consequences of ImplicationThe statement “P =⇒ Q”

only is False if P is True and Q is False .

False implies nothingP False means Q can be True or FalseAnything implies true.P can be True or False when Q is True

If chemical plant pollutes river, fish die.If fish die, did chemical plant pollute river?

Not necessarily.

P =⇒ Q and Q are True does not mean P is True

Be careful!

Instead we have:P =⇒ Q and P are True does mean Q is True .

The chemical plant pollutes river. Can we conclude fish die?

Some Fun: use propositional formulas to describe implication?((P =⇒ Q)∧P) =⇒ Q.

Page 190: 70: Discrete Math and Probability.

Non-Consequences/consequences of ImplicationThe statement “P =⇒ Q”

only is False if P is True and Q is False .

False implies nothingP False means Q can be True or FalseAnything implies true.P can be True or False when Q is True

If chemical plant pollutes river, fish die.If fish die, did chemical plant pollute river?

Not necessarily.

P =⇒ Q and Q are True does not mean P is True

Be careful!

Instead we have:P =⇒ Q and P are True does mean Q is True .

The chemical plant pollutes river. Can we conclude fish die?

Some Fun: use propositional formulas to describe implication?((P =⇒ Q)∧P) =⇒ Q.

Page 191: 70: Discrete Math and Probability.

Non-Consequences/consequences of ImplicationThe statement “P =⇒ Q”

only is False if P is True and Q is False .

False implies nothingP False means Q can be True or FalseAnything implies true.P can be True or False when Q is True

If chemical plant pollutes river, fish die.If fish die, did chemical plant pollute river?

Not necessarily.

P =⇒ Q and Q are True does not mean P is True

Be careful!

Instead we have:

P =⇒ Q and P are True does mean Q is True .

The chemical plant pollutes river. Can we conclude fish die?

Some Fun: use propositional formulas to describe implication?((P =⇒ Q)∧P) =⇒ Q.

Page 192: 70: Discrete Math and Probability.

Non-Consequences/consequences of ImplicationThe statement “P =⇒ Q”

only is False if P is True and Q is False .

False implies nothingP False means Q can be True or FalseAnything implies true.P can be True or False when Q is True

If chemical plant pollutes river, fish die.If fish die, did chemical plant pollute river?

Not necessarily.

P =⇒ Q and Q are True does not mean P is True

Be careful!

Instead we have:P =⇒ Q and P are True does mean Q is True .

The chemical plant pollutes river. Can we conclude fish die?

Some Fun: use propositional formulas to describe implication?((P =⇒ Q)∧P) =⇒ Q.

Page 193: 70: Discrete Math and Probability.

Non-Consequences/consequences of ImplicationThe statement “P =⇒ Q”

only is False if P is True and Q is False .

False implies nothingP False means Q can be True or FalseAnything implies true.P can be True or False when Q is True

If chemical plant pollutes river, fish die.If fish die, did chemical plant pollute river?

Not necessarily.

P =⇒ Q and Q are True does not mean P is True

Be careful!

Instead we have:P =⇒ Q and P are True does mean Q is True .

The chemical plant pollutes river.

Can we conclude fish die?

Some Fun: use propositional formulas to describe implication?((P =⇒ Q)∧P) =⇒ Q.

Page 194: 70: Discrete Math and Probability.

Non-Consequences/consequences of ImplicationThe statement “P =⇒ Q”

only is False if P is True and Q is False .

False implies nothingP False means Q can be True or FalseAnything implies true.P can be True or False when Q is True

If chemical plant pollutes river, fish die.If fish die, did chemical plant pollute river?

Not necessarily.

P =⇒ Q and Q are True does not mean P is True

Be careful!

Instead we have:P =⇒ Q and P are True does mean Q is True .

The chemical plant pollutes river. Can we conclude fish die?

Some Fun: use propositional formulas to describe implication?((P =⇒ Q)∧P) =⇒ Q.

Page 195: 70: Discrete Math and Probability.

Non-Consequences/consequences of ImplicationThe statement “P =⇒ Q”

only is False if P is True and Q is False .

False implies nothingP False means Q can be True or FalseAnything implies true.P can be True or False when Q is True

If chemical plant pollutes river, fish die.If fish die, did chemical plant pollute river?

Not necessarily.

P =⇒ Q and Q are True does not mean P is True

Be careful!

Instead we have:P =⇒ Q and P are True does mean Q is True .

The chemical plant pollutes river. Can we conclude fish die?

Some Fun: use propositional formulas to describe implication?((P =⇒ Q)∧P) =⇒ Q.

Page 196: 70: Discrete Math and Probability.

Non-Consequences/consequences of ImplicationThe statement “P =⇒ Q”

only is False if P is True and Q is False .

False implies nothingP False means Q can be True or FalseAnything implies true.P can be True or False when Q is True

If chemical plant pollutes river, fish die.If fish die, did chemical plant pollute river?

Not necessarily.

P =⇒ Q and Q are True does not mean P is True

Be careful!

Instead we have:P =⇒ Q and P are True does mean Q is True .

The chemical plant pollutes river. Can we conclude fish die?

Some Fun: use propositional formulas to describe implication?

((P =⇒ Q)∧P) =⇒ Q.

Page 197: 70: Discrete Math and Probability.

Non-Consequences/consequences of ImplicationThe statement “P =⇒ Q”

only is False if P is True and Q is False .

False implies nothingP False means Q can be True or FalseAnything implies true.P can be True or False when Q is True

If chemical plant pollutes river, fish die.If fish die, did chemical plant pollute river?

Not necessarily.

P =⇒ Q and Q are True does not mean P is True

Be careful!

Instead we have:P =⇒ Q and P are True does mean Q is True .

The chemical plant pollutes river. Can we conclude fish die?

Some Fun: use propositional formulas to describe implication?((P =⇒ Q)∧P) =⇒ Q.

Page 198: 70: Discrete Math and Probability.

Implication and English.

P =⇒ Q

I If P, then Q.

I Q if P.Just reversing the order.

I P only if Q.Remember if P is true then Q must be true.

this suggests that P can only be true if Q is true.since if Q is false P must have been false.

I P is sufficient for Q.This means that proving P allows youto conclude that Q is true.

I Q is necessary for P.For P to be true it is necessary that Q is true.Or if Q is false then we know that P is false.

Page 199: 70: Discrete Math and Probability.

Implication and English.

P =⇒ Q

I If P, then Q.

I Q if P.Just reversing the order.

I P only if Q.Remember if P is true then Q must be true.

this suggests that P can only be true if Q is true.since if Q is false P must have been false.

I P is sufficient for Q.This means that proving P allows youto conclude that Q is true.

I Q is necessary for P.For P to be true it is necessary that Q is true.Or if Q is false then we know that P is false.

Page 200: 70: Discrete Math and Probability.

Implication and English.

P =⇒ Q

I If P, then Q.

I Q if P.Just reversing the order.

I P only if Q.Remember if P is true then Q must be true.

this suggests that P can only be true if Q is true.since if Q is false P must have been false.

I P is sufficient for Q.This means that proving P allows youto conclude that Q is true.

I Q is necessary for P.For P to be true it is necessary that Q is true.Or if Q is false then we know that P is false.

Page 201: 70: Discrete Math and Probability.

Implication and English.

P =⇒ Q

I If P, then Q.

I Q if P.Just reversing the order.

I P only if Q.Remember if P is true then Q must be true.

this suggests that P can only be true if Q is true.since if Q is false P must have been false.

I P is sufficient for Q.This means that proving P allows youto conclude that Q is true.

I Q is necessary for P.For P to be true it is necessary that Q is true.Or if Q is false then we know that P is false.

Page 202: 70: Discrete Math and Probability.

Implication and English.

P =⇒ Q

I If P, then Q.

I Q if P.Just reversing the order.

I P only if Q.Remember if P is true then Q must be true.

this suggests that P can only be true if Q is true.since if Q is false P must have been false.

I P is sufficient for Q.This means that proving P allows youto conclude that Q is true.

I Q is necessary for P.For P to be true it is necessary that Q is true.Or if Q is false then we know that P is false.

Page 203: 70: Discrete Math and Probability.

Implication and English.

P =⇒ Q

I If P, then Q.

I Q if P.Just reversing the order.

I P only if Q.Remember if P is true then Q must be true.

this suggests that P can only be true if Q is true.since if Q is false P must have been false.

I P is sufficient for Q.This means that proving P allows youto conclude that Q is true.

I Q is necessary for P.For P to be true it is necessary that Q is true.Or if Q is false then we know that P is false.

Page 204: 70: Discrete Math and Probability.

Truth Table: implication.

P Q P =⇒ QT T TT F

F

F T

T

F F

T

P Q ¬P ∨QT T

T

T F

F

F T

T

F F

T

¬P ∨Q ≡ P =⇒ Q.

These two propositional forms are logically equivalent!

Page 205: 70: Discrete Math and Probability.

Truth Table: implication.

P Q P =⇒ QT T TT F FF T

T

F F

T

P Q ¬P ∨QT T

T

T F

F

F T

T

F F

T

¬P ∨Q ≡ P =⇒ Q.

These two propositional forms are logically equivalent!

Page 206: 70: Discrete Math and Probability.

Truth Table: implication.

P Q P =⇒ QT T TT F FF T TF F

T

P Q ¬P ∨QT T

T

T F

F

F T

T

F F

T

¬P ∨Q ≡ P =⇒ Q.

These two propositional forms are logically equivalent!

Page 207: 70: Discrete Math and Probability.

Truth Table: implication.

P Q P =⇒ QT T TT F FF T TF F T

P Q ¬P ∨QT T

T

T F

F

F T

T

F F

T

¬P ∨Q ≡ P =⇒ Q.

These two propositional forms are logically equivalent!

Page 208: 70: Discrete Math and Probability.

Truth Table: implication.

P Q P =⇒ QT T TT F FF T TF F T

P Q ¬P ∨QT T

T

T F

F

F T

T

F F

T

¬P ∨Q ≡ P =⇒ Q.

These two propositional forms are logically equivalent!

Page 209: 70: Discrete Math and Probability.

Truth Table: implication.

P Q P =⇒ QT T TT F FF T TF F T

P Q ¬P ∨QT T TT F

F

F T

T

F F

T

¬P ∨Q ≡ P =⇒ Q.

These two propositional forms are logically equivalent!

Page 210: 70: Discrete Math and Probability.

Truth Table: implication.

P Q P =⇒ QT T TT F FF T TF F T

P Q ¬P ∨QT T TT F FF T

T

F F

T

¬P ∨Q ≡ P =⇒ Q.

These two propositional forms are logically equivalent!

Page 211: 70: Discrete Math and Probability.

Truth Table: implication.

P Q P =⇒ QT T TT F FF T TF F T

P Q ¬P ∨QT T TT F FF T TF F

T

¬P ∨Q ≡ P =⇒ Q.

These two propositional forms are logically equivalent!

Page 212: 70: Discrete Math and Probability.

Truth Table: implication.

P Q P =⇒ QT T TT F FF T TF F T

P Q ¬P ∨QT T TT F FF T TF F T

¬P ∨Q ≡ P =⇒ Q.

These two propositional forms are logically equivalent!

Page 213: 70: Discrete Math and Probability.

Truth Table: implication.

P Q P =⇒ QT T TT F FF T TF F T

P Q ¬P ∨QT T TT F FF T TF F T

¬P ∨Q ≡ P =⇒ Q.

These two propositional forms are logically equivalent!

Page 214: 70: Discrete Math and Probability.

Truth Table: implication.

P Q P =⇒ QT T TT F FF T TF F T

P Q ¬P ∨QT T TT F FF T TF F T

¬P ∨Q ≡ P =⇒ Q.

These two propositional forms are logically equivalent!

Page 215: 70: Discrete Math and Probability.

Contrapositive, ConverseI Contrapositive of P =⇒ Q is ¬Q =⇒ ¬P.

I If the plant pollutes, fish die.I If the fish don’t die, the plant does not pollute.

(contrapositive)

I If you stand in the rain, you get wet.I If you did not stand in the rain, you did not get wet.

(not contrapositive!)

converse!

I If you did not get wet, you did not stand in the rain.(contrapositive.)

Logically equivalent! Notation: ≡.P =⇒ Q ≡ ¬P ∨Q ≡ ¬(¬Q)∨¬P ≡ ¬Q =⇒ ¬P.

I Converse of P =⇒ Q is Q =⇒ P.If fish die the plant pollutes.Not logically equivalent!

I Definition: If P =⇒ Q and Q =⇒ P is P if and only if Q orP ⇐⇒ Q.(Logically Equivalent: ⇐⇒ . )

Page 216: 70: Discrete Math and Probability.

Contrapositive, ConverseI Contrapositive of P =⇒ Q is ¬Q =⇒ ¬P.

I If the plant pollutes, fish die.

I If the fish don’t die, the plant does not pollute.(contrapositive)

I If you stand in the rain, you get wet.I If you did not stand in the rain, you did not get wet.

(not contrapositive!)

converse!

I If you did not get wet, you did not stand in the rain.(contrapositive.)

Logically equivalent! Notation: ≡.P =⇒ Q ≡ ¬P ∨Q ≡ ¬(¬Q)∨¬P ≡ ¬Q =⇒ ¬P.

I Converse of P =⇒ Q is Q =⇒ P.If fish die the plant pollutes.Not logically equivalent!

I Definition: If P =⇒ Q and Q =⇒ P is P if and only if Q orP ⇐⇒ Q.(Logically Equivalent: ⇐⇒ . )

Page 217: 70: Discrete Math and Probability.

Contrapositive, ConverseI Contrapositive of P =⇒ Q is ¬Q =⇒ ¬P.

I If the plant pollutes, fish die.I If the fish don’t die, the plant does not pollute.

(contrapositive)

I If you stand in the rain, you get wet.I If you did not stand in the rain, you did not get wet.

(not contrapositive!)

converse!

I If you did not get wet, you did not stand in the rain.(contrapositive.)

Logically equivalent! Notation: ≡.P =⇒ Q ≡ ¬P ∨Q ≡ ¬(¬Q)∨¬P ≡ ¬Q =⇒ ¬P.

I Converse of P =⇒ Q is Q =⇒ P.If fish die the plant pollutes.Not logically equivalent!

I Definition: If P =⇒ Q and Q =⇒ P is P if and only if Q orP ⇐⇒ Q.(Logically Equivalent: ⇐⇒ . )

Page 218: 70: Discrete Math and Probability.

Contrapositive, ConverseI Contrapositive of P =⇒ Q is ¬Q =⇒ ¬P.

I If the plant pollutes, fish die.I If the fish don’t die, the plant does not pollute.

(contrapositive)

I If you stand in the rain, you get wet.I If you did not stand in the rain, you did not get wet.

(not contrapositive!)

converse!

I If you did not get wet, you did not stand in the rain.(contrapositive.)

Logically equivalent! Notation: ≡.P =⇒ Q ≡ ¬P ∨Q ≡ ¬(¬Q)∨¬P ≡ ¬Q =⇒ ¬P.

I Converse of P =⇒ Q is Q =⇒ P.If fish die the plant pollutes.Not logically equivalent!

I Definition: If P =⇒ Q and Q =⇒ P is P if and only if Q orP ⇐⇒ Q.(Logically Equivalent: ⇐⇒ . )

Page 219: 70: Discrete Math and Probability.

Contrapositive, ConverseI Contrapositive of P =⇒ Q is ¬Q =⇒ ¬P.

I If the plant pollutes, fish die.I If the fish don’t die, the plant does not pollute.

(contrapositive)

I If you stand in the rain, you get wet.

I If you did not stand in the rain, you did not get wet.(not contrapositive!)

converse!

I If you did not get wet, you did not stand in the rain.(contrapositive.)

Logically equivalent! Notation: ≡.P =⇒ Q ≡ ¬P ∨Q ≡ ¬(¬Q)∨¬P ≡ ¬Q =⇒ ¬P.

I Converse of P =⇒ Q is Q =⇒ P.If fish die the plant pollutes.Not logically equivalent!

I Definition: If P =⇒ Q and Q =⇒ P is P if and only if Q orP ⇐⇒ Q.(Logically Equivalent: ⇐⇒ . )

Page 220: 70: Discrete Math and Probability.

Contrapositive, ConverseI Contrapositive of P =⇒ Q is ¬Q =⇒ ¬P.

I If the plant pollutes, fish die.I If the fish don’t die, the plant does not pollute.

(contrapositive)

I If you stand in the rain, you get wet.I If you did not stand in the rain, you did not get wet.

(not contrapositive!)

converse!

I If you did not get wet, you did not stand in the rain.(contrapositive.)

Logically equivalent! Notation: ≡.P =⇒ Q ≡ ¬P ∨Q ≡ ¬(¬Q)∨¬P ≡ ¬Q =⇒ ¬P.

I Converse of P =⇒ Q is Q =⇒ P.If fish die the plant pollutes.Not logically equivalent!

I Definition: If P =⇒ Q and Q =⇒ P is P if and only if Q orP ⇐⇒ Q.(Logically Equivalent: ⇐⇒ . )

Page 221: 70: Discrete Math and Probability.

Contrapositive, ConverseI Contrapositive of P =⇒ Q is ¬Q =⇒ ¬P.

I If the plant pollutes, fish die.I If the fish don’t die, the plant does not pollute.

(contrapositive)

I If you stand in the rain, you get wet.I If you did not stand in the rain, you did not get wet.

(not contrapositive!)

converse!I If you did not get wet, you did not stand in the rain.

(contrapositive.)

Logically equivalent! Notation: ≡.P =⇒ Q ≡ ¬P ∨Q ≡ ¬(¬Q)∨¬P ≡ ¬Q =⇒ ¬P.

I Converse of P =⇒ Q is Q =⇒ P.If fish die the plant pollutes.Not logically equivalent!

I Definition: If P =⇒ Q and Q =⇒ P is P if and only if Q orP ⇐⇒ Q.(Logically Equivalent: ⇐⇒ . )

Page 222: 70: Discrete Math and Probability.

Contrapositive, ConverseI Contrapositive of P =⇒ Q is ¬Q =⇒ ¬P.

I If the plant pollutes, fish die.I If the fish don’t die, the plant does not pollute.

(contrapositive)

I If you stand in the rain, you get wet.I If you did not stand in the rain, you did not get wet.

(not contrapositive!)

converse!

I If you did not get wet, you did not stand in the rain.

(contrapositive.)

Logically equivalent! Notation: ≡.P =⇒ Q ≡ ¬P ∨Q ≡ ¬(¬Q)∨¬P ≡ ¬Q =⇒ ¬P.

I Converse of P =⇒ Q is Q =⇒ P.If fish die the plant pollutes.Not logically equivalent!

I Definition: If P =⇒ Q and Q =⇒ P is P if and only if Q orP ⇐⇒ Q.(Logically Equivalent: ⇐⇒ . )

Page 223: 70: Discrete Math and Probability.

Contrapositive, ConverseI Contrapositive of P =⇒ Q is ¬Q =⇒ ¬P.

I If the plant pollutes, fish die.I If the fish don’t die, the plant does not pollute.

(contrapositive)

I If you stand in the rain, you get wet.I If you did not stand in the rain, you did not get wet.

(not contrapositive!)

converse!

I If you did not get wet, you did not stand in the rain.(contrapositive.)

Logically equivalent! Notation: ≡.P =⇒ Q ≡ ¬P ∨Q ≡ ¬(¬Q)∨¬P ≡ ¬Q =⇒ ¬P.

I Converse of P =⇒ Q is Q =⇒ P.If fish die the plant pollutes.Not logically equivalent!

I Definition: If P =⇒ Q and Q =⇒ P is P if and only if Q orP ⇐⇒ Q.(Logically Equivalent: ⇐⇒ . )

Page 224: 70: Discrete Math and Probability.

Contrapositive, ConverseI Contrapositive of P =⇒ Q is ¬Q =⇒ ¬P.

I If the plant pollutes, fish die.I If the fish don’t die, the plant does not pollute.

(contrapositive)

I If you stand in the rain, you get wet.I If you did not stand in the rain, you did not get wet.

(not contrapositive!)

converse!

I If you did not get wet, you did not stand in the rain.(contrapositive.)

Logically equivalent! Notation: ≡.

P =⇒ Q ≡ ¬P ∨Q ≡ ¬(¬Q)∨¬P ≡ ¬Q =⇒ ¬P.I Converse of P =⇒ Q is Q =⇒ P.

If fish die the plant pollutes.Not logically equivalent!

I Definition: If P =⇒ Q and Q =⇒ P is P if and only if Q orP ⇐⇒ Q.(Logically Equivalent: ⇐⇒ . )

Page 225: 70: Discrete Math and Probability.

Contrapositive, ConverseI Contrapositive of P =⇒ Q is ¬Q =⇒ ¬P.

I If the plant pollutes, fish die.I If the fish don’t die, the plant does not pollute.

(contrapositive)

I If you stand in the rain, you get wet.I If you did not stand in the rain, you did not get wet.

(not contrapositive!)

converse!

I If you did not get wet, you did not stand in the rain.(contrapositive.)

Logically equivalent! Notation: ≡.P =⇒ Q

≡ ¬P ∨Q ≡ ¬(¬Q)∨¬P ≡ ¬Q =⇒ ¬P.I Converse of P =⇒ Q is Q =⇒ P.

If fish die the plant pollutes.Not logically equivalent!

I Definition: If P =⇒ Q and Q =⇒ P is P if and only if Q orP ⇐⇒ Q.(Logically Equivalent: ⇐⇒ . )

Page 226: 70: Discrete Math and Probability.

Contrapositive, ConverseI Contrapositive of P =⇒ Q is ¬Q =⇒ ¬P.

I If the plant pollutes, fish die.I If the fish don’t die, the plant does not pollute.

(contrapositive)

I If you stand in the rain, you get wet.I If you did not stand in the rain, you did not get wet.

(not contrapositive!)

converse!

I If you did not get wet, you did not stand in the rain.(contrapositive.)

Logically equivalent! Notation: ≡.P =⇒ Q ≡ ¬P ∨Q

≡ ¬(¬Q)∨¬P ≡ ¬Q =⇒ ¬P.I Converse of P =⇒ Q is Q =⇒ P.

If fish die the plant pollutes.Not logically equivalent!

I Definition: If P =⇒ Q and Q =⇒ P is P if and only if Q orP ⇐⇒ Q.(Logically Equivalent: ⇐⇒ . )

Page 227: 70: Discrete Math and Probability.

Contrapositive, ConverseI Contrapositive of P =⇒ Q is ¬Q =⇒ ¬P.

I If the plant pollutes, fish die.I If the fish don’t die, the plant does not pollute.

(contrapositive)

I If you stand in the rain, you get wet.I If you did not stand in the rain, you did not get wet.

(not contrapositive!)

converse!

I If you did not get wet, you did not stand in the rain.(contrapositive.)

Logically equivalent! Notation: ≡.P =⇒ Q ≡ ¬P ∨Q ≡ ¬(¬Q)∨¬P

≡ ¬Q =⇒ ¬P.I Converse of P =⇒ Q is Q =⇒ P.

If fish die the plant pollutes.Not logically equivalent!

I Definition: If P =⇒ Q and Q =⇒ P is P if and only if Q orP ⇐⇒ Q.(Logically Equivalent: ⇐⇒ . )

Page 228: 70: Discrete Math and Probability.

Contrapositive, ConverseI Contrapositive of P =⇒ Q is ¬Q =⇒ ¬P.

I If the plant pollutes, fish die.I If the fish don’t die, the plant does not pollute.

(contrapositive)

I If you stand in the rain, you get wet.I If you did not stand in the rain, you did not get wet.

(not contrapositive!)

converse!

I If you did not get wet, you did not stand in the rain.(contrapositive.)

Logically equivalent! Notation: ≡.P =⇒ Q ≡ ¬P ∨Q ≡ ¬(¬Q)∨¬P ≡ ¬Q =⇒ ¬P.

I Converse of P =⇒ Q is Q =⇒ P.If fish die the plant pollutes.Not logically equivalent!

I Definition: If P =⇒ Q and Q =⇒ P is P if and only if Q orP ⇐⇒ Q.(Logically Equivalent: ⇐⇒ . )

Page 229: 70: Discrete Math and Probability.

Contrapositive, ConverseI Contrapositive of P =⇒ Q is ¬Q =⇒ ¬P.

I If the plant pollutes, fish die.I If the fish don’t die, the plant does not pollute.

(contrapositive)

I If you stand in the rain, you get wet.I If you did not stand in the rain, you did not get wet.

(not contrapositive!)

converse!

I If you did not get wet, you did not stand in the rain.(contrapositive.)

Logically equivalent! Notation: ≡.P =⇒ Q ≡ ¬P ∨Q ≡ ¬(¬Q)∨¬P ≡ ¬Q =⇒ ¬P.

I Converse of P =⇒ Q is Q =⇒ P.

If fish die the plant pollutes.Not logically equivalent!

I Definition: If P =⇒ Q and Q =⇒ P is P if and only if Q orP ⇐⇒ Q.(Logically Equivalent: ⇐⇒ . )

Page 230: 70: Discrete Math and Probability.

Contrapositive, ConverseI Contrapositive of P =⇒ Q is ¬Q =⇒ ¬P.

I If the plant pollutes, fish die.I If the fish don’t die, the plant does not pollute.

(contrapositive)

I If you stand in the rain, you get wet.I If you did not stand in the rain, you did not get wet.

(not contrapositive!)

converse!

I If you did not get wet, you did not stand in the rain.(contrapositive.)

Logically equivalent! Notation: ≡.P =⇒ Q ≡ ¬P ∨Q ≡ ¬(¬Q)∨¬P ≡ ¬Q =⇒ ¬P.

I Converse of P =⇒ Q is Q =⇒ P.If fish die the plant pollutes.

Not logically equivalent!I Definition: If P =⇒ Q and Q =⇒ P is P if and only if Q or

P ⇐⇒ Q.(Logically Equivalent: ⇐⇒ . )

Page 231: 70: Discrete Math and Probability.

Contrapositive, ConverseI Contrapositive of P =⇒ Q is ¬Q =⇒ ¬P.

I If the plant pollutes, fish die.I If the fish don’t die, the plant does not pollute.

(contrapositive)

I If you stand in the rain, you get wet.I If you did not stand in the rain, you did not get wet.

(not contrapositive!) converse!I If you did not get wet, you did not stand in the rain.

(contrapositive.)

Logically equivalent! Notation: ≡.P =⇒ Q ≡ ¬P ∨Q ≡ ¬(¬Q)∨¬P ≡ ¬Q =⇒ ¬P.

I Converse of P =⇒ Q is Q =⇒ P.If fish die the plant pollutes.

Not logically equivalent!I Definition: If P =⇒ Q and Q =⇒ P is P if and only if Q or

P ⇐⇒ Q.(Logically Equivalent: ⇐⇒ . )

Page 232: 70: Discrete Math and Probability.

Contrapositive, ConverseI Contrapositive of P =⇒ Q is ¬Q =⇒ ¬P.

I If the plant pollutes, fish die.I If the fish don’t die, the plant does not pollute.

(contrapositive)

I If you stand in the rain, you get wet.I If you did not stand in the rain, you did not get wet.

(not contrapositive!) converse!I If you did not get wet, you did not stand in the rain.

(contrapositive.)

Logically equivalent! Notation: ≡.P =⇒ Q ≡ ¬P ∨Q ≡ ¬(¬Q)∨¬P ≡ ¬Q =⇒ ¬P.

I Converse of P =⇒ Q is Q =⇒ P.If fish die the plant pollutes.Not logically equivalent!

I Definition: If P =⇒ Q and Q =⇒ P is P if and only if Q orP ⇐⇒ Q.(Logically Equivalent: ⇐⇒ . )

Page 233: 70: Discrete Math and Probability.

Contrapositive, ConverseI Contrapositive of P =⇒ Q is ¬Q =⇒ ¬P.

I If the plant pollutes, fish die.I If the fish don’t die, the plant does not pollute.

(contrapositive)

I If you stand in the rain, you get wet.I If you did not stand in the rain, you did not get wet.

(not contrapositive!) converse!I If you did not get wet, you did not stand in the rain.

(contrapositive.)

Logically equivalent! Notation: ≡.P =⇒ Q ≡ ¬P ∨Q ≡ ¬(¬Q)∨¬P ≡ ¬Q =⇒ ¬P.

I Converse of P =⇒ Q is Q =⇒ P.If fish die the plant pollutes.Not logically equivalent!

I Definition: If P =⇒ Q and Q =⇒ P is P if and only if Q orP ⇐⇒ Q.(Logically Equivalent: ⇐⇒ . )

Page 234: 70: Discrete Math and Probability.

Variables.Propositions?

I ∑ni=1 i = n(n+1)

2 .

I x > 2I n is even and the sum of two primes

No. They have a free variable.

We call them predicates, e.g., Q(x) = “x is even”Same as boolean valued functions from 61A or 61AS!I P(n) = “∑n

i=1 i = n(n+1)2 .”

I R(x) = “x > 2”I G(n) = “n is even and the sum of two primes”I Remember Wason’s experiment!

F (x) = “Person x flew.”C(x) = “Person x went to Chicago

I C(x) =⇒ F (x). Theory from Wason’s.If person x goes to Chicago then person x flew.

Next: Statements about boolean valued functions!!

Page 235: 70: Discrete Math and Probability.

Variables.Propositions?

I ∑ni=1 i = n(n+1)

2 .

I x > 2

I n is even and the sum of two primes

No. They have a free variable.

We call them predicates, e.g., Q(x) = “x is even”Same as boolean valued functions from 61A or 61AS!I P(n) = “∑n

i=1 i = n(n+1)2 .”

I R(x) = “x > 2”I G(n) = “n is even and the sum of two primes”I Remember Wason’s experiment!

F (x) = “Person x flew.”C(x) = “Person x went to Chicago

I C(x) =⇒ F (x). Theory from Wason’s.If person x goes to Chicago then person x flew.

Next: Statements about boolean valued functions!!

Page 236: 70: Discrete Math and Probability.

Variables.Propositions?

I ∑ni=1 i = n(n+1)

2 .

I x > 2I n is even and the sum of two primes

No. They have a free variable.

We call them predicates, e.g., Q(x) = “x is even”Same as boolean valued functions from 61A or 61AS!I P(n) = “∑n

i=1 i = n(n+1)2 .”

I R(x) = “x > 2”I G(n) = “n is even and the sum of two primes”I Remember Wason’s experiment!

F (x) = “Person x flew.”C(x) = “Person x went to Chicago

I C(x) =⇒ F (x). Theory from Wason’s.If person x goes to Chicago then person x flew.

Next: Statements about boolean valued functions!!

Page 237: 70: Discrete Math and Probability.

Variables.Propositions?

I ∑ni=1 i = n(n+1)

2 .

I x > 2I n is even and the sum of two primes

No. They have a free variable.

We call them predicates, e.g., Q(x) = “x is even”Same as boolean valued functions from 61A or 61AS!I P(n) = “∑n

i=1 i = n(n+1)2 .”

I R(x) = “x > 2”I G(n) = “n is even and the sum of two primes”I Remember Wason’s experiment!

F (x) = “Person x flew.”C(x) = “Person x went to Chicago

I C(x) =⇒ F (x). Theory from Wason’s.If person x goes to Chicago then person x flew.

Next: Statements about boolean valued functions!!

Page 238: 70: Discrete Math and Probability.

Variables.Propositions?

I ∑ni=1 i = n(n+1)

2 .

I x > 2I n is even and the sum of two primes

No. They have a free variable.

We call them predicates, e.g., Q(x) = “x is even”

Same as boolean valued functions from 61A or 61AS!I P(n) = “∑n

i=1 i = n(n+1)2 .”

I R(x) = “x > 2”I G(n) = “n is even and the sum of two primes”I Remember Wason’s experiment!

F (x) = “Person x flew.”C(x) = “Person x went to Chicago

I C(x) =⇒ F (x). Theory from Wason’s.If person x goes to Chicago then person x flew.

Next: Statements about boolean valued functions!!

Page 239: 70: Discrete Math and Probability.

Variables.Propositions?

I ∑ni=1 i = n(n+1)

2 .

I x > 2I n is even and the sum of two primes

No. They have a free variable.

We call them predicates, e.g., Q(x) = “x is even”Same as boolean valued functions from 61A or 61AS!

I P(n) = “∑ni=1 i = n(n+1)

2 .”I R(x) = “x > 2”I G(n) = “n is even and the sum of two primes”I Remember Wason’s experiment!

F (x) = “Person x flew.”C(x) = “Person x went to Chicago

I C(x) =⇒ F (x). Theory from Wason’s.If person x goes to Chicago then person x flew.

Next: Statements about boolean valued functions!!

Page 240: 70: Discrete Math and Probability.

Variables.Propositions?

I ∑ni=1 i = n(n+1)

2 .

I x > 2I n is even and the sum of two primes

No. They have a free variable.

We call them predicates, e.g., Q(x) = “x is even”Same as boolean valued functions from 61A or 61AS!I P(n) = “∑n

i=1 i = n(n+1)2 .”

I R(x) = “x > 2”I G(n) = “n is even and the sum of two primes”I Remember Wason’s experiment!

F (x) = “Person x flew.”C(x) = “Person x went to Chicago

I C(x) =⇒ F (x). Theory from Wason’s.If person x goes to Chicago then person x flew.

Next: Statements about boolean valued functions!!

Page 241: 70: Discrete Math and Probability.

Variables.Propositions?

I ∑ni=1 i = n(n+1)

2 .

I x > 2I n is even and the sum of two primes

No. They have a free variable.

We call them predicates, e.g., Q(x) = “x is even”Same as boolean valued functions from 61A or 61AS!I P(n) = “∑n

i=1 i = n(n+1)2 .”

I R(x) = “x > 2”

I G(n) = “n is even and the sum of two primes”I Remember Wason’s experiment!

F (x) = “Person x flew.”C(x) = “Person x went to Chicago

I C(x) =⇒ F (x). Theory from Wason’s.If person x goes to Chicago then person x flew.

Next: Statements about boolean valued functions!!

Page 242: 70: Discrete Math and Probability.

Variables.Propositions?

I ∑ni=1 i = n(n+1)

2 .

I x > 2I n is even and the sum of two primes

No. They have a free variable.

We call them predicates, e.g., Q(x) = “x is even”Same as boolean valued functions from 61A or 61AS!I P(n) = “∑n

i=1 i = n(n+1)2 .”

I R(x) = “x > 2”I G(n) = “n is even and the sum of two primes”

I Remember Wason’s experiment!F (x) = “Person x flew.”C(x) = “Person x went to Chicago

I C(x) =⇒ F (x). Theory from Wason’s.If person x goes to Chicago then person x flew.

Next: Statements about boolean valued functions!!

Page 243: 70: Discrete Math and Probability.

Variables.Propositions?

I ∑ni=1 i = n(n+1)

2 .

I x > 2I n is even and the sum of two primes

No. They have a free variable.

We call them predicates, e.g., Q(x) = “x is even”Same as boolean valued functions from 61A or 61AS!I P(n) = “∑n

i=1 i = n(n+1)2 .”

I R(x) = “x > 2”I G(n) = “n is even and the sum of two primes”I Remember Wason’s experiment!

F (x) = “Person x flew.”

C(x) = “Person x went to ChicagoI C(x) =⇒ F (x). Theory from Wason’s.

If person x goes to Chicago then person x flew.

Next: Statements about boolean valued functions!!

Page 244: 70: Discrete Math and Probability.

Variables.Propositions?

I ∑ni=1 i = n(n+1)

2 .

I x > 2I n is even and the sum of two primes

No. They have a free variable.

We call them predicates, e.g., Q(x) = “x is even”Same as boolean valued functions from 61A or 61AS!I P(n) = “∑n

i=1 i = n(n+1)2 .”

I R(x) = “x > 2”I G(n) = “n is even and the sum of two primes”I Remember Wason’s experiment!

F (x) = “Person x flew.”C(x) = “Person x went to Chicago

I C(x) =⇒ F (x). Theory from Wason’s.If person x goes to Chicago then person x flew.

Next: Statements about boolean valued functions!!

Page 245: 70: Discrete Math and Probability.

Variables.Propositions?

I ∑ni=1 i = n(n+1)

2 .

I x > 2I n is even and the sum of two primes

No. They have a free variable.

We call them predicates, e.g., Q(x) = “x is even”Same as boolean valued functions from 61A or 61AS!I P(n) = “∑n

i=1 i = n(n+1)2 .”

I R(x) = “x > 2”I G(n) = “n is even and the sum of two primes”I Remember Wason’s experiment!

F (x) = “Person x flew.”C(x) = “Person x went to Chicago

I C(x) =⇒ F (x).

Theory from Wason’s.If person x goes to Chicago then person x flew.

Next: Statements about boolean valued functions!!

Page 246: 70: Discrete Math and Probability.

Variables.Propositions?

I ∑ni=1 i = n(n+1)

2 .

I x > 2I n is even and the sum of two primes

No. They have a free variable.

We call them predicates, e.g., Q(x) = “x is even”Same as boolean valued functions from 61A or 61AS!I P(n) = “∑n

i=1 i = n(n+1)2 .”

I R(x) = “x > 2”I G(n) = “n is even and the sum of two primes”I Remember Wason’s experiment!

F (x) = “Person x flew.”C(x) = “Person x went to Chicago

I C(x) =⇒ F (x). Theory from Wason’s.

If person x goes to Chicago then person x flew.

Next: Statements about boolean valued functions!!

Page 247: 70: Discrete Math and Probability.

Variables.Propositions?

I ∑ni=1 i = n(n+1)

2 .

I x > 2I n is even and the sum of two primes

No. They have a free variable.

We call them predicates, e.g., Q(x) = “x is even”Same as boolean valued functions from 61A or 61AS!I P(n) = “∑n

i=1 i = n(n+1)2 .”

I R(x) = “x > 2”I G(n) = “n is even and the sum of two primes”I Remember Wason’s experiment!

F (x) = “Person x flew.”C(x) = “Person x went to Chicago

I C(x) =⇒ F (x). Theory from Wason’s.If person x goes to Chicago then person x flew.

Next: Statements about boolean valued functions!!

Page 248: 70: Discrete Math and Probability.

Variables.Propositions?

I ∑ni=1 i = n(n+1)

2 .

I x > 2I n is even and the sum of two primes

No. They have a free variable.

We call them predicates, e.g., Q(x) = “x is even”Same as boolean valued functions from 61A or 61AS!I P(n) = “∑n

i=1 i = n(n+1)2 .”

I R(x) = “x > 2”I G(n) = “n is even and the sum of two primes”I Remember Wason’s experiment!

F (x) = “Person x flew.”C(x) = “Person x went to Chicago

I C(x) =⇒ F (x). Theory from Wason’s.If person x goes to Chicago then person x flew.

Next:

Statements about boolean valued functions!!

Page 249: 70: Discrete Math and Probability.

Variables.Propositions?

I ∑ni=1 i = n(n+1)

2 .

I x > 2I n is even and the sum of two primes

No. They have a free variable.

We call them predicates, e.g., Q(x) = “x is even”Same as boolean valued functions from 61A or 61AS!I P(n) = “∑n

i=1 i = n(n+1)2 .”

I R(x) = “x > 2”I G(n) = “n is even and the sum of two primes”I Remember Wason’s experiment!

F (x) = “Person x flew.”C(x) = “Person x went to Chicago

I C(x) =⇒ F (x). Theory from Wason’s.If person x goes to Chicago then person x flew.

Next: Statements about boolean valued functions!!

Page 250: 70: Discrete Math and Probability.

Quantifiers..There exists quantifier:

(∃x ∈ S)(P(x)) means ”There exists an x in S where P(x) is true.”

For example:(∃x ∈ N)(x = x2)

Equivalent to “(0 = 0)∨ (1 = 1)∨ (2 = 4)∨ . . .”Much shorter to use a quantifier!

For all quantifier;(∀x ∈ S) (P(x)). means “For all x in S, we have P(x) is True .”

Examples:

“Adding 1 makes a bigger number.”

(∀x ∈ N) (x +1 > x)

”the square of a number is always non-negative”

(∀x ∈ N)(x2 >= 0)

Wait! What is N?

Page 251: 70: Discrete Math and Probability.

Quantifiers..There exists quantifier:

(∃x ∈ S)(P(x)) means ”There exists an x in S where P(x) is true.”

For example:(∃x ∈ N)(x = x2)

Equivalent to “(0 = 0)∨ (1 = 1)∨ (2 = 4)∨ . . .”Much shorter to use a quantifier!

For all quantifier;(∀x ∈ S) (P(x)). means “For all x in S, we have P(x) is True .”

Examples:

“Adding 1 makes a bigger number.”

(∀x ∈ N) (x +1 > x)

”the square of a number is always non-negative”

(∀x ∈ N)(x2 >= 0)

Wait! What is N?

Page 252: 70: Discrete Math and Probability.

Quantifiers..There exists quantifier:

(∃x ∈ S)(P(x)) means ”There exists an x in S where P(x) is true.”

For example:(∃x ∈ N)(x = x2)

Equivalent to “(0 = 0)∨ (1 = 1)∨ (2 = 4)∨ . . .”Much shorter to use a quantifier!

For all quantifier;(∀x ∈ S) (P(x)). means “For all x in S, we have P(x) is True .”

Examples:

“Adding 1 makes a bigger number.”

(∀x ∈ N) (x +1 > x)

”the square of a number is always non-negative”

(∀x ∈ N)(x2 >= 0)

Wait! What is N?

Page 253: 70: Discrete Math and Probability.

Quantifiers..There exists quantifier:

(∃x ∈ S)(P(x)) means ”There exists an x in S where P(x) is true.”

For example:(∃x ∈ N)(x = x2)

Equivalent to “(0 = 0)

∨ (1 = 1)∨ (2 = 4)∨ . . .”Much shorter to use a quantifier!

For all quantifier;(∀x ∈ S) (P(x)). means “For all x in S, we have P(x) is True .”

Examples:

“Adding 1 makes a bigger number.”

(∀x ∈ N) (x +1 > x)

”the square of a number is always non-negative”

(∀x ∈ N)(x2 >= 0)

Wait! What is N?

Page 254: 70: Discrete Math and Probability.

Quantifiers..There exists quantifier:

(∃x ∈ S)(P(x)) means ”There exists an x in S where P(x) is true.”

For example:(∃x ∈ N)(x = x2)

Equivalent to “(0 = 0)∨ (1 = 1)

∨ (2 = 4)∨ . . .”Much shorter to use a quantifier!

For all quantifier;(∀x ∈ S) (P(x)). means “For all x in S, we have P(x) is True .”

Examples:

“Adding 1 makes a bigger number.”

(∀x ∈ N) (x +1 > x)

”the square of a number is always non-negative”

(∀x ∈ N)(x2 >= 0)

Wait! What is N?

Page 255: 70: Discrete Math and Probability.

Quantifiers..There exists quantifier:

(∃x ∈ S)(P(x)) means ”There exists an x in S where P(x) is true.”

For example:(∃x ∈ N)(x = x2)

Equivalent to “(0 = 0)∨ (1 = 1)∨ (2 = 4)

∨ . . .”Much shorter to use a quantifier!

For all quantifier;(∀x ∈ S) (P(x)). means “For all x in S, we have P(x) is True .”

Examples:

“Adding 1 makes a bigger number.”

(∀x ∈ N) (x +1 > x)

”the square of a number is always non-negative”

(∀x ∈ N)(x2 >= 0)

Wait! What is N?

Page 256: 70: Discrete Math and Probability.

Quantifiers..There exists quantifier:

(∃x ∈ S)(P(x)) means ”There exists an x in S where P(x) is true.”

For example:(∃x ∈ N)(x = x2)

Equivalent to “(0 = 0)∨ (1 = 1)∨ (2 = 4)∨ . . .”

Much shorter to use a quantifier!

For all quantifier;(∀x ∈ S) (P(x)). means “For all x in S, we have P(x) is True .”

Examples:

“Adding 1 makes a bigger number.”

(∀x ∈ N) (x +1 > x)

”the square of a number is always non-negative”

(∀x ∈ N)(x2 >= 0)

Wait! What is N?

Page 257: 70: Discrete Math and Probability.

Quantifiers..There exists quantifier:

(∃x ∈ S)(P(x)) means ”There exists an x in S where P(x) is true.”

For example:(∃x ∈ N)(x = x2)

Equivalent to “(0 = 0)∨ (1 = 1)∨ (2 = 4)∨ . . .”Much shorter to use a quantifier!

For all quantifier;(∀x ∈ S) (P(x)). means “For all x in S, we have P(x) is True .”

Examples:

“Adding 1 makes a bigger number.”

(∀x ∈ N) (x +1 > x)

”the square of a number is always non-negative”

(∀x ∈ N)(x2 >= 0)

Wait! What is N?

Page 258: 70: Discrete Math and Probability.

Quantifiers..There exists quantifier:

(∃x ∈ S)(P(x)) means ”There exists an x in S where P(x) is true.”

For example:(∃x ∈ N)(x = x2)

Equivalent to “(0 = 0)∨ (1 = 1)∨ (2 = 4)∨ . . .”Much shorter to use a quantifier!

For all quantifier;(∀x ∈ S) (P(x)). means “For all x in S, we have P(x) is True .”

Examples:

“Adding 1 makes a bigger number.”

(∀x ∈ N) (x +1 > x)

”the square of a number is always non-negative”

(∀x ∈ N)(x2 >= 0)

Wait! What is N?

Page 259: 70: Discrete Math and Probability.

Quantifiers..There exists quantifier:

(∃x ∈ S)(P(x)) means ”There exists an x in S where P(x) is true.”

For example:(∃x ∈ N)(x = x2)

Equivalent to “(0 = 0)∨ (1 = 1)∨ (2 = 4)∨ . . .”Much shorter to use a quantifier!

For all quantifier;(∀x ∈ S) (P(x)). means “For all x in S, we have P(x) is True .”

Examples:

“Adding 1 makes a bigger number.”

(∀x ∈ N) (x +1 > x)

”the square of a number is always non-negative”

(∀x ∈ N)(x2 >= 0)

Wait! What is N?

Page 260: 70: Discrete Math and Probability.

Quantifiers..There exists quantifier:

(∃x ∈ S)(P(x)) means ”There exists an x in S where P(x) is true.”

For example:(∃x ∈ N)(x = x2)

Equivalent to “(0 = 0)∨ (1 = 1)∨ (2 = 4)∨ . . .”Much shorter to use a quantifier!

For all quantifier;(∀x ∈ S) (P(x)). means “For all x in S, we have P(x) is True .”

Examples:

“Adding 1 makes a bigger number.”

(∀x ∈ N) (x +1 > x)

”the square of a number is always non-negative”

(∀x ∈ N)(x2 >= 0)

Wait! What is N?

Page 261: 70: Discrete Math and Probability.

Quantifiers..There exists quantifier:

(∃x ∈ S)(P(x)) means ”There exists an x in S where P(x) is true.”

For example:(∃x ∈ N)(x = x2)

Equivalent to “(0 = 0)∨ (1 = 1)∨ (2 = 4)∨ . . .”Much shorter to use a quantifier!

For all quantifier;(∀x ∈ S) (P(x)). means “For all x in S, we have P(x) is True .”

Examples:

“Adding 1 makes a bigger number.”

(∀x ∈ N) (x +1 > x)

”the square of a number is always non-negative”

(∀x ∈ N)(x2 >= 0)

Wait! What is N?

Page 262: 70: Discrete Math and Probability.

Quantifiers..There exists quantifier:

(∃x ∈ S)(P(x)) means ”There exists an x in S where P(x) is true.”

For example:(∃x ∈ N)(x = x2)

Equivalent to “(0 = 0)∨ (1 = 1)∨ (2 = 4)∨ . . .”Much shorter to use a quantifier!

For all quantifier;(∀x ∈ S) (P(x)). means “For all x in S, we have P(x) is True .”

Examples:

“Adding 1 makes a bigger number.”

(∀x ∈ N) (x +1 > x)

”the square of a number is always non-negative”

(∀x ∈ N)(x2 >= 0)

Wait! What is N?

Page 263: 70: Discrete Math and Probability.

Quantifiers..There exists quantifier:

(∃x ∈ S)(P(x)) means ”There exists an x in S where P(x) is true.”

For example:(∃x ∈ N)(x = x2)

Equivalent to “(0 = 0)∨ (1 = 1)∨ (2 = 4)∨ . . .”Much shorter to use a quantifier!

For all quantifier;(∀x ∈ S) (P(x)). means “For all x in S, we have P(x) is True .”

Examples:

“Adding 1 makes a bigger number.”

(∀x ∈ N) (x +1 > x)

”the square of a number is always non-negative”

(∀x ∈ N)(x2 >= 0)

Wait! What is N?

Page 264: 70: Discrete Math and Probability.

Quantifiers..There exists quantifier:

(∃x ∈ S)(P(x)) means ”There exists an x in S where P(x) is true.”

For example:(∃x ∈ N)(x = x2)

Equivalent to “(0 = 0)∨ (1 = 1)∨ (2 = 4)∨ . . .”Much shorter to use a quantifier!

For all quantifier;(∀x ∈ S) (P(x)). means “For all x in S, we have P(x) is True .”

Examples:

“Adding 1 makes a bigger number.”

(∀x ∈ N) (x +1 > x)

”the square of a number is always non-negative”

(∀x ∈ N)(x2 >= 0)

Wait!

What is N?

Page 265: 70: Discrete Math and Probability.

Quantifiers..There exists quantifier:

(∃x ∈ S)(P(x)) means ”There exists an x in S where P(x) is true.”

For example:(∃x ∈ N)(x = x2)

Equivalent to “(0 = 0)∨ (1 = 1)∨ (2 = 4)∨ . . .”Much shorter to use a quantifier!

For all quantifier;(∀x ∈ S) (P(x)). means “For all x in S, we have P(x) is True .”

Examples:

“Adding 1 makes a bigger number.”

(∀x ∈ N) (x +1 > x)

”the square of a number is always non-negative”

(∀x ∈ N)(x2 >= 0)

Wait! What is N?

Page 266: 70: Discrete Math and Probability.

Quantifiers: universes.

Proposition: “For all natural numbers n, ∑ni=1 i = n(n+1)

2 .”

Proposition has universe:

“the natural numbers”.

Universe examples include..

I N= {0,1, . . .} (natural numbers).

I Z= {. . . ,−1,0, . . .} (integers)

I Z+ (positive integers)

I R (real numbers)

I Any set: S = {Alice,Bob,Charlie,Donna}.I See note 0 for more!

Page 267: 70: Discrete Math and Probability.

Quantifiers: universes.

Proposition: “For all natural numbers n, ∑ni=1 i = n(n+1)

2 .”

Proposition has universe: “the natural numbers”.

Universe examples include..

I N= {0,1, . . .} (natural numbers).

I Z= {. . . ,−1,0, . . .} (integers)

I Z+ (positive integers)

I R (real numbers)

I Any set: S = {Alice,Bob,Charlie,Donna}.I See note 0 for more!

Page 268: 70: Discrete Math and Probability.

Quantifiers: universes.

Proposition: “For all natural numbers n, ∑ni=1 i = n(n+1)

2 .”

Proposition has universe: “the natural numbers”.

Universe examples include..

I N= {0,1, . . .} (natural numbers).

I Z= {. . . ,−1,0, . . .} (integers)

I Z+ (positive integers)

I R (real numbers)

I Any set: S = {Alice,Bob,Charlie,Donna}.I See note 0 for more!

Page 269: 70: Discrete Math and Probability.

Quantifiers: universes.

Proposition: “For all natural numbers n, ∑ni=1 i = n(n+1)

2 .”

Proposition has universe: “the natural numbers”.

Universe examples include..

I N= {0,1, . . .} (natural numbers).

I Z= {. . . ,−1,0, . . .} (integers)

I Z+ (positive integers)

I R (real numbers)

I Any set: S = {Alice,Bob,Charlie,Donna}.I See note 0 for more!

Page 270: 70: Discrete Math and Probability.

Quantifiers: universes.

Proposition: “For all natural numbers n, ∑ni=1 i = n(n+1)

2 .”

Proposition has universe: “the natural numbers”.

Universe examples include..

I N= {0,1, . . .} (natural numbers).

I Z= {. . . ,−1,0, . . .} (integers)

I Z+ (positive integers)

I R (real numbers)

I Any set: S = {Alice,Bob,Charlie,Donna}.I See note 0 for more!

Page 271: 70: Discrete Math and Probability.

Quantifiers: universes.

Proposition: “For all natural numbers n, ∑ni=1 i = n(n+1)

2 .”

Proposition has universe: “the natural numbers”.

Universe examples include..

I N= {0,1, . . .} (natural numbers).

I Z= {. . . ,−1,0, . . .} (integers)

I Z+ (positive integers)

I R (real numbers)

I Any set: S = {Alice,Bob,Charlie,Donna}.I See note 0 for more!

Page 272: 70: Discrete Math and Probability.

Quantifiers: universes.

Proposition: “For all natural numbers n, ∑ni=1 i = n(n+1)

2 .”

Proposition has universe: “the natural numbers”.

Universe examples include..

I N= {0,1, . . .} (natural numbers).

I Z= {. . . ,−1,0, . . .} (integers)

I Z+ (positive integers)

I R (real numbers)

I Any set: S = {Alice,Bob,Charlie,Donna}.I See note 0 for more!

Page 273: 70: Discrete Math and Probability.

Quantifiers: universes.

Proposition: “For all natural numbers n, ∑ni=1 i = n(n+1)

2 .”

Proposition has universe: “the natural numbers”.

Universe examples include..

I N= {0,1, . . .} (natural numbers).

I Z= {. . . ,−1,0, . . .} (integers)

I Z+ (positive integers)

I R (real numbers)

I Any set: S = {Alice,Bob,Charlie,Donna}.

I See note 0 for more!

Page 274: 70: Discrete Math and Probability.

Quantifiers: universes.

Proposition: “For all natural numbers n, ∑ni=1 i = n(n+1)

2 .”

Proposition has universe: “the natural numbers”.

Universe examples include..

I N= {0,1, . . .} (natural numbers).

I Z= {. . . ,−1,0, . . .} (integers)

I Z+ (positive integers)

I R (real numbers)

I Any set: S = {Alice,Bob,Charlie,Donna}.I See note 0 for more!

Page 275: 70: Discrete Math and Probability.

Back to: Wason’s experiment:1Theory:

“If a person travels to Chicago, he/she flies.”

Suppose you see that Alice went to Baltimore, Bob drove, Charliewent to Chicago, and Donna flew.

Which cards do you need to flip to test the theory?

P(x) = “Person x went to Chicago.” Q(x) = “Person x flew”

Statement/theory: ∀x ∈ {A,B,C,D},P(x) =⇒ Q(x)

P(A) = False . Do we care about Q(A)?No. P(A) =⇒ Q(A), when P(A) is False , Q(A) can be anything.

Q(B) = False . Do we care about P(B)?Yes. P(B) =⇒ Q(B) ≡ ¬Q(B) =⇒ ¬P(B).So P(Bob) must be False .

P(C) = True . Do we care about P(C)?Yes. P(C) =⇒ Q(C) means Q(C) must be true.

Q(D) = True . Do we care about P(D)?No. P(D) =⇒ Q(D) holds whatever P(D) is when Q(D) is true.

Only have to turn over cards for Bob and Charlie.

Page 276: 70: Discrete Math and Probability.

Back to: Wason’s experiment:1Theory:“If a person travels to Chicago, he/she flies.”

Suppose you see that Alice went to Baltimore, Bob drove, Charliewent to Chicago, and Donna flew.

Which cards do you need to flip to test the theory?

P(x) = “Person x went to Chicago.” Q(x) = “Person x flew”

Statement/theory: ∀x ∈ {A,B,C,D},P(x) =⇒ Q(x)

P(A) = False . Do we care about Q(A)?No. P(A) =⇒ Q(A), when P(A) is False , Q(A) can be anything.

Q(B) = False . Do we care about P(B)?Yes. P(B) =⇒ Q(B) ≡ ¬Q(B) =⇒ ¬P(B).So P(Bob) must be False .

P(C) = True . Do we care about P(C)?Yes. P(C) =⇒ Q(C) means Q(C) must be true.

Q(D) = True . Do we care about P(D)?No. P(D) =⇒ Q(D) holds whatever P(D) is when Q(D) is true.

Only have to turn over cards for Bob and Charlie.

Page 277: 70: Discrete Math and Probability.

Back to: Wason’s experiment:1Theory:“If a person travels to Chicago, he/she flies.”

Suppose you see that Alice went to Baltimore, Bob drove, Charliewent to Chicago, and Donna flew.

Which cards do you need to flip to test the theory?

P(x) = “Person x went to Chicago.” Q(x) = “Person x flew”

Statement/theory: ∀x ∈ {A,B,C,D},P(x) =⇒ Q(x)

P(A) = False . Do we care about Q(A)?No. P(A) =⇒ Q(A), when P(A) is False , Q(A) can be anything.

Q(B) = False . Do we care about P(B)?Yes. P(B) =⇒ Q(B) ≡ ¬Q(B) =⇒ ¬P(B).So P(Bob) must be False .

P(C) = True . Do we care about P(C)?Yes. P(C) =⇒ Q(C) means Q(C) must be true.

Q(D) = True . Do we care about P(D)?No. P(D) =⇒ Q(D) holds whatever P(D) is when Q(D) is true.

Only have to turn over cards for Bob and Charlie.

Page 278: 70: Discrete Math and Probability.

Back to: Wason’s experiment:1Theory:“If a person travels to Chicago, he/she flies.”

Suppose you see that Alice went to Baltimore, Bob drove, Charliewent to Chicago, and Donna flew.

Which cards do you need to flip to test the theory?

P(x) = “Person x went to Chicago.” Q(x) = “Person x flew”

Statement/theory: ∀x ∈ {A,B,C,D},P(x) =⇒ Q(x)

P(A) = False . Do we care about Q(A)?No. P(A) =⇒ Q(A), when P(A) is False , Q(A) can be anything.

Q(B) = False . Do we care about P(B)?Yes. P(B) =⇒ Q(B) ≡ ¬Q(B) =⇒ ¬P(B).So P(Bob) must be False .

P(C) = True . Do we care about P(C)?Yes. P(C) =⇒ Q(C) means Q(C) must be true.

Q(D) = True . Do we care about P(D)?No. P(D) =⇒ Q(D) holds whatever P(D) is when Q(D) is true.

Only have to turn over cards for Bob and Charlie.

Page 279: 70: Discrete Math and Probability.

Back to: Wason’s experiment:1Theory:“If a person travels to Chicago, he/she flies.”

Suppose you see that Alice went to Baltimore, Bob drove, Charliewent to Chicago, and Donna flew.

Which cards do you need to flip to test the theory?

P(x) = “Person x went to Chicago.” Q(x) = “Person x flew”

Statement/theory: ∀x ∈ {A,B,C,D},P(x) =⇒ Q(x)

P(A) = False . Do we care about Q(A)?No. P(A) =⇒ Q(A), when P(A) is False , Q(A) can be anything.

Q(B) = False . Do we care about P(B)?Yes. P(B) =⇒ Q(B) ≡ ¬Q(B) =⇒ ¬P(B).So P(Bob) must be False .

P(C) = True . Do we care about P(C)?Yes. P(C) =⇒ Q(C) means Q(C) must be true.

Q(D) = True . Do we care about P(D)?No. P(D) =⇒ Q(D) holds whatever P(D) is when Q(D) is true.

Only have to turn over cards for Bob and Charlie.

Page 280: 70: Discrete Math and Probability.

Back to: Wason’s experiment:1Theory:“If a person travels to Chicago, he/she flies.”

Suppose you see that Alice went to Baltimore, Bob drove, Charliewent to Chicago, and Donna flew.

Which cards do you need to flip to test the theory?

P(x) = “Person x went to Chicago.”

Q(x) = “Person x flew”

Statement/theory: ∀x ∈ {A,B,C,D},P(x) =⇒ Q(x)

P(A) = False . Do we care about Q(A)?No. P(A) =⇒ Q(A), when P(A) is False , Q(A) can be anything.

Q(B) = False . Do we care about P(B)?Yes. P(B) =⇒ Q(B) ≡ ¬Q(B) =⇒ ¬P(B).So P(Bob) must be False .

P(C) = True . Do we care about P(C)?Yes. P(C) =⇒ Q(C) means Q(C) must be true.

Q(D) = True . Do we care about P(D)?No. P(D) =⇒ Q(D) holds whatever P(D) is when Q(D) is true.

Only have to turn over cards for Bob and Charlie.

Page 281: 70: Discrete Math and Probability.

Back to: Wason’s experiment:1Theory:“If a person travels to Chicago, he/she flies.”

Suppose you see that Alice went to Baltimore, Bob drove, Charliewent to Chicago, and Donna flew.

Which cards do you need to flip to test the theory?

P(x) = “Person x went to Chicago.” Q(x) = “Person x flew”

Statement/theory: ∀x ∈ {A,B,C,D},P(x) =⇒ Q(x)

P(A) = False . Do we care about Q(A)?No. P(A) =⇒ Q(A), when P(A) is False , Q(A) can be anything.

Q(B) = False . Do we care about P(B)?Yes. P(B) =⇒ Q(B) ≡ ¬Q(B) =⇒ ¬P(B).So P(Bob) must be False .

P(C) = True . Do we care about P(C)?Yes. P(C) =⇒ Q(C) means Q(C) must be true.

Q(D) = True . Do we care about P(D)?No. P(D) =⇒ Q(D) holds whatever P(D) is when Q(D) is true.

Only have to turn over cards for Bob and Charlie.

Page 282: 70: Discrete Math and Probability.

Back to: Wason’s experiment:1Theory:“If a person travels to Chicago, he/she flies.”

Suppose you see that Alice went to Baltimore, Bob drove, Charliewent to Chicago, and Donna flew.

Which cards do you need to flip to test the theory?

P(x) = “Person x went to Chicago.” Q(x) = “Person x flew”

Statement/theory: ∀x ∈ {A,B,C,D},P(x)

=⇒ Q(x)

P(A) = False . Do we care about Q(A)?No. P(A) =⇒ Q(A), when P(A) is False , Q(A) can be anything.

Q(B) = False . Do we care about P(B)?Yes. P(B) =⇒ Q(B) ≡ ¬Q(B) =⇒ ¬P(B).So P(Bob) must be False .

P(C) = True . Do we care about P(C)?Yes. P(C) =⇒ Q(C) means Q(C) must be true.

Q(D) = True . Do we care about P(D)?No. P(D) =⇒ Q(D) holds whatever P(D) is when Q(D) is true.

Only have to turn over cards for Bob and Charlie.

Page 283: 70: Discrete Math and Probability.

Back to: Wason’s experiment:1Theory:“If a person travels to Chicago, he/she flies.”

Suppose you see that Alice went to Baltimore, Bob drove, Charliewent to Chicago, and Donna flew.

Which cards do you need to flip to test the theory?

P(x) = “Person x went to Chicago.” Q(x) = “Person x flew”

Statement/theory: ∀x ∈ {A,B,C,D},P(x) =⇒ Q(x)

P(A) = False . Do we care about Q(A)?No. P(A) =⇒ Q(A), when P(A) is False , Q(A) can be anything.

Q(B) = False . Do we care about P(B)?Yes. P(B) =⇒ Q(B) ≡ ¬Q(B) =⇒ ¬P(B).So P(Bob) must be False .

P(C) = True . Do we care about P(C)?Yes. P(C) =⇒ Q(C) means Q(C) must be true.

Q(D) = True . Do we care about P(D)?No. P(D) =⇒ Q(D) holds whatever P(D) is when Q(D) is true.

Only have to turn over cards for Bob and Charlie.

Page 284: 70: Discrete Math and Probability.

Back to: Wason’s experiment:1Theory:“If a person travels to Chicago, he/she flies.”

Suppose you see that Alice went to Baltimore, Bob drove, Charliewent to Chicago, and Donna flew.

Which cards do you need to flip to test the theory?

P(x) = “Person x went to Chicago.” Q(x) = “Person x flew”

Statement/theory: ∀x ∈ {A,B,C,D},P(x) =⇒ Q(x)

P(A) = False .

Do we care about Q(A)?No. P(A) =⇒ Q(A), when P(A) is False , Q(A) can be anything.

Q(B) = False . Do we care about P(B)?Yes. P(B) =⇒ Q(B) ≡ ¬Q(B) =⇒ ¬P(B).So P(Bob) must be False .

P(C) = True . Do we care about P(C)?Yes. P(C) =⇒ Q(C) means Q(C) must be true.

Q(D) = True . Do we care about P(D)?No. P(D) =⇒ Q(D) holds whatever P(D) is when Q(D) is true.

Only have to turn over cards for Bob and Charlie.

Page 285: 70: Discrete Math and Probability.

Back to: Wason’s experiment:1Theory:“If a person travels to Chicago, he/she flies.”

Suppose you see that Alice went to Baltimore, Bob drove, Charliewent to Chicago, and Donna flew.

Which cards do you need to flip to test the theory?

P(x) = “Person x went to Chicago.” Q(x) = “Person x flew”

Statement/theory: ∀x ∈ {A,B,C,D},P(x) =⇒ Q(x)

P(A) = False . Do we care about Q(A)?

No. P(A) =⇒ Q(A), when P(A) is False , Q(A) can be anything.

Q(B) = False . Do we care about P(B)?Yes. P(B) =⇒ Q(B) ≡ ¬Q(B) =⇒ ¬P(B).So P(Bob) must be False .

P(C) = True . Do we care about P(C)?Yes. P(C) =⇒ Q(C) means Q(C) must be true.

Q(D) = True . Do we care about P(D)?No. P(D) =⇒ Q(D) holds whatever P(D) is when Q(D) is true.

Only have to turn over cards for Bob and Charlie.

Page 286: 70: Discrete Math and Probability.

Back to: Wason’s experiment:1Theory:“If a person travels to Chicago, he/she flies.”

Suppose you see that Alice went to Baltimore, Bob drove, Charliewent to Chicago, and Donna flew.

Which cards do you need to flip to test the theory?

P(x) = “Person x went to Chicago.” Q(x) = “Person x flew”

Statement/theory: ∀x ∈ {A,B,C,D},P(x) =⇒ Q(x)

P(A) = False . Do we care about Q(A)?No.

P(A) =⇒ Q(A), when P(A) is False , Q(A) can be anything.

Q(B) = False . Do we care about P(B)?Yes. P(B) =⇒ Q(B) ≡ ¬Q(B) =⇒ ¬P(B).So P(Bob) must be False .

P(C) = True . Do we care about P(C)?Yes. P(C) =⇒ Q(C) means Q(C) must be true.

Q(D) = True . Do we care about P(D)?No. P(D) =⇒ Q(D) holds whatever P(D) is when Q(D) is true.

Only have to turn over cards for Bob and Charlie.

Page 287: 70: Discrete Math and Probability.

Back to: Wason’s experiment:1Theory:“If a person travels to Chicago, he/she flies.”

Suppose you see that Alice went to Baltimore, Bob drove, Charliewent to Chicago, and Donna flew.

Which cards do you need to flip to test the theory?

P(x) = “Person x went to Chicago.” Q(x) = “Person x flew”

Statement/theory: ∀x ∈ {A,B,C,D},P(x) =⇒ Q(x)

P(A) = False . Do we care about Q(A)?No. P(A) =⇒ Q(A), when P(A) is False , Q(A) can be anything.

Q(B) = False . Do we care about P(B)?Yes. P(B) =⇒ Q(B) ≡ ¬Q(B) =⇒ ¬P(B).So P(Bob) must be False .

P(C) = True . Do we care about P(C)?Yes. P(C) =⇒ Q(C) means Q(C) must be true.

Q(D) = True . Do we care about P(D)?No. P(D) =⇒ Q(D) holds whatever P(D) is when Q(D) is true.

Only have to turn over cards for Bob and Charlie.

Page 288: 70: Discrete Math and Probability.

Back to: Wason’s experiment:1Theory:“If a person travels to Chicago, he/she flies.”

Suppose you see that Alice went to Baltimore, Bob drove, Charliewent to Chicago, and Donna flew.

Which cards do you need to flip to test the theory?

P(x) = “Person x went to Chicago.” Q(x) = “Person x flew”

Statement/theory: ∀x ∈ {A,B,C,D},P(x) =⇒ Q(x)

P(A) = False . Do we care about Q(A)?No. P(A) =⇒ Q(A), when P(A) is False , Q(A) can be anything.

Q(B) = False .

Do we care about P(B)?Yes. P(B) =⇒ Q(B) ≡ ¬Q(B) =⇒ ¬P(B).So P(Bob) must be False .

P(C) = True . Do we care about P(C)?Yes. P(C) =⇒ Q(C) means Q(C) must be true.

Q(D) = True . Do we care about P(D)?No. P(D) =⇒ Q(D) holds whatever P(D) is when Q(D) is true.

Only have to turn over cards for Bob and Charlie.

Page 289: 70: Discrete Math and Probability.

Back to: Wason’s experiment:1Theory:“If a person travels to Chicago, he/she flies.”

Suppose you see that Alice went to Baltimore, Bob drove, Charliewent to Chicago, and Donna flew.

Which cards do you need to flip to test the theory?

P(x) = “Person x went to Chicago.” Q(x) = “Person x flew”

Statement/theory: ∀x ∈ {A,B,C,D},P(x) =⇒ Q(x)

P(A) = False . Do we care about Q(A)?No. P(A) =⇒ Q(A), when P(A) is False , Q(A) can be anything.

Q(B) = False . Do we care about P(B)?

Yes. P(B) =⇒ Q(B) ≡ ¬Q(B) =⇒ ¬P(B).So P(Bob) must be False .

P(C) = True . Do we care about P(C)?Yes. P(C) =⇒ Q(C) means Q(C) must be true.

Q(D) = True . Do we care about P(D)?No. P(D) =⇒ Q(D) holds whatever P(D) is when Q(D) is true.

Only have to turn over cards for Bob and Charlie.

Page 290: 70: Discrete Math and Probability.

Back to: Wason’s experiment:1Theory:“If a person travels to Chicago, he/she flies.”

Suppose you see that Alice went to Baltimore, Bob drove, Charliewent to Chicago, and Donna flew.

Which cards do you need to flip to test the theory?

P(x) = “Person x went to Chicago.” Q(x) = “Person x flew”

Statement/theory: ∀x ∈ {A,B,C,D},P(x) =⇒ Q(x)

P(A) = False . Do we care about Q(A)?No. P(A) =⇒ Q(A), when P(A) is False , Q(A) can be anything.

Q(B) = False . Do we care about P(B)?Yes.

P(B) =⇒ Q(B) ≡ ¬Q(B) =⇒ ¬P(B).So P(Bob) must be False .

P(C) = True . Do we care about P(C)?Yes. P(C) =⇒ Q(C) means Q(C) must be true.

Q(D) = True . Do we care about P(D)?No. P(D) =⇒ Q(D) holds whatever P(D) is when Q(D) is true.

Only have to turn over cards for Bob and Charlie.

Page 291: 70: Discrete Math and Probability.

Back to: Wason’s experiment:1Theory:“If a person travels to Chicago, he/she flies.”

Suppose you see that Alice went to Baltimore, Bob drove, Charliewent to Chicago, and Donna flew.

Which cards do you need to flip to test the theory?

P(x) = “Person x went to Chicago.” Q(x) = “Person x flew”

Statement/theory: ∀x ∈ {A,B,C,D},P(x) =⇒ Q(x)

P(A) = False . Do we care about Q(A)?No. P(A) =⇒ Q(A), when P(A) is False , Q(A) can be anything.

Q(B) = False . Do we care about P(B)?Yes. P(B) =⇒ Q(B)

≡ ¬Q(B) =⇒ ¬P(B).So P(Bob) must be False .

P(C) = True . Do we care about P(C)?Yes. P(C) =⇒ Q(C) means Q(C) must be true.

Q(D) = True . Do we care about P(D)?No. P(D) =⇒ Q(D) holds whatever P(D) is when Q(D) is true.

Only have to turn over cards for Bob and Charlie.

Page 292: 70: Discrete Math and Probability.

Back to: Wason’s experiment:1Theory:“If a person travels to Chicago, he/she flies.”

Suppose you see that Alice went to Baltimore, Bob drove, Charliewent to Chicago, and Donna flew.

Which cards do you need to flip to test the theory?

P(x) = “Person x went to Chicago.” Q(x) = “Person x flew”

Statement/theory: ∀x ∈ {A,B,C,D},P(x) =⇒ Q(x)

P(A) = False . Do we care about Q(A)?No. P(A) =⇒ Q(A), when P(A) is False , Q(A) can be anything.

Q(B) = False . Do we care about P(B)?Yes. P(B) =⇒ Q(B) ≡ ¬Q(B) =⇒ ¬P(B).

So P(Bob) must be False .

P(C) = True . Do we care about P(C)?Yes. P(C) =⇒ Q(C) means Q(C) must be true.

Q(D) = True . Do we care about P(D)?No. P(D) =⇒ Q(D) holds whatever P(D) is when Q(D) is true.

Only have to turn over cards for Bob and Charlie.

Page 293: 70: Discrete Math and Probability.

Back to: Wason’s experiment:1Theory:“If a person travels to Chicago, he/she flies.”

Suppose you see that Alice went to Baltimore, Bob drove, Charliewent to Chicago, and Donna flew.

Which cards do you need to flip to test the theory?

P(x) = “Person x went to Chicago.” Q(x) = “Person x flew”

Statement/theory: ∀x ∈ {A,B,C,D},P(x) =⇒ Q(x)

P(A) = False . Do we care about Q(A)?No. P(A) =⇒ Q(A), when P(A) is False , Q(A) can be anything.

Q(B) = False . Do we care about P(B)?Yes. P(B) =⇒ Q(B) ≡ ¬Q(B) =⇒ ¬P(B).So P(Bob) must be False .

P(C) = True . Do we care about P(C)?Yes. P(C) =⇒ Q(C) means Q(C) must be true.

Q(D) = True . Do we care about P(D)?No. P(D) =⇒ Q(D) holds whatever P(D) is when Q(D) is true.

Only have to turn over cards for Bob and Charlie.

Page 294: 70: Discrete Math and Probability.

Back to: Wason’s experiment:1Theory:“If a person travels to Chicago, he/she flies.”

Suppose you see that Alice went to Baltimore, Bob drove, Charliewent to Chicago, and Donna flew.

Which cards do you need to flip to test the theory?

P(x) = “Person x went to Chicago.” Q(x) = “Person x flew”

Statement/theory: ∀x ∈ {A,B,C,D},P(x) =⇒ Q(x)

P(A) = False . Do we care about Q(A)?No. P(A) =⇒ Q(A), when P(A) is False , Q(A) can be anything.

Q(B) = False . Do we care about P(B)?Yes. P(B) =⇒ Q(B) ≡ ¬Q(B) =⇒ ¬P(B).So P(Bob) must be False .

P(C) = True .

Do we care about P(C)?Yes. P(C) =⇒ Q(C) means Q(C) must be true.

Q(D) = True . Do we care about P(D)?No. P(D) =⇒ Q(D) holds whatever P(D) is when Q(D) is true.

Only have to turn over cards for Bob and Charlie.

Page 295: 70: Discrete Math and Probability.

Back to: Wason’s experiment:1Theory:“If a person travels to Chicago, he/she flies.”

Suppose you see that Alice went to Baltimore, Bob drove, Charliewent to Chicago, and Donna flew.

Which cards do you need to flip to test the theory?

P(x) = “Person x went to Chicago.” Q(x) = “Person x flew”

Statement/theory: ∀x ∈ {A,B,C,D},P(x) =⇒ Q(x)

P(A) = False . Do we care about Q(A)?No. P(A) =⇒ Q(A), when P(A) is False , Q(A) can be anything.

Q(B) = False . Do we care about P(B)?Yes. P(B) =⇒ Q(B) ≡ ¬Q(B) =⇒ ¬P(B).So P(Bob) must be False .

P(C) = True . Do we care about P(C)?

Yes. P(C) =⇒ Q(C) means Q(C) must be true.

Q(D) = True . Do we care about P(D)?No. P(D) =⇒ Q(D) holds whatever P(D) is when Q(D) is true.

Only have to turn over cards for Bob and Charlie.

Page 296: 70: Discrete Math and Probability.

Back to: Wason’s experiment:1Theory:“If a person travels to Chicago, he/she flies.”

Suppose you see that Alice went to Baltimore, Bob drove, Charliewent to Chicago, and Donna flew.

Which cards do you need to flip to test the theory?

P(x) = “Person x went to Chicago.” Q(x) = “Person x flew”

Statement/theory: ∀x ∈ {A,B,C,D},P(x) =⇒ Q(x)

P(A) = False . Do we care about Q(A)?No. P(A) =⇒ Q(A), when P(A) is False , Q(A) can be anything.

Q(B) = False . Do we care about P(B)?Yes. P(B) =⇒ Q(B) ≡ ¬Q(B) =⇒ ¬P(B).So P(Bob) must be False .

P(C) = True . Do we care about P(C)?Yes.

P(C) =⇒ Q(C) means Q(C) must be true.

Q(D) = True . Do we care about P(D)?No. P(D) =⇒ Q(D) holds whatever P(D) is when Q(D) is true.

Only have to turn over cards for Bob and Charlie.

Page 297: 70: Discrete Math and Probability.

Back to: Wason’s experiment:1Theory:“If a person travels to Chicago, he/she flies.”

Suppose you see that Alice went to Baltimore, Bob drove, Charliewent to Chicago, and Donna flew.

Which cards do you need to flip to test the theory?

P(x) = “Person x went to Chicago.” Q(x) = “Person x flew”

Statement/theory: ∀x ∈ {A,B,C,D},P(x) =⇒ Q(x)

P(A) = False . Do we care about Q(A)?No. P(A) =⇒ Q(A), when P(A) is False , Q(A) can be anything.

Q(B) = False . Do we care about P(B)?Yes. P(B) =⇒ Q(B) ≡ ¬Q(B) =⇒ ¬P(B).So P(Bob) must be False .

P(C) = True . Do we care about P(C)?Yes. P(C) =⇒ Q(C) means Q(C) must be true.

Q(D) = True . Do we care about P(D)?No. P(D) =⇒ Q(D) holds whatever P(D) is when Q(D) is true.

Only have to turn over cards for Bob and Charlie.

Page 298: 70: Discrete Math and Probability.

Back to: Wason’s experiment:1Theory:“If a person travels to Chicago, he/she flies.”

Suppose you see that Alice went to Baltimore, Bob drove, Charliewent to Chicago, and Donna flew.

Which cards do you need to flip to test the theory?

P(x) = “Person x went to Chicago.” Q(x) = “Person x flew”

Statement/theory: ∀x ∈ {A,B,C,D},P(x) =⇒ Q(x)

P(A) = False . Do we care about Q(A)?No. P(A) =⇒ Q(A), when P(A) is False , Q(A) can be anything.

Q(B) = False . Do we care about P(B)?Yes. P(B) =⇒ Q(B) ≡ ¬Q(B) =⇒ ¬P(B).So P(Bob) must be False .

P(C) = True . Do we care about P(C)?Yes. P(C) =⇒ Q(C) means Q(C) must be true.

Q(D) = True .

Do we care about P(D)?No. P(D) =⇒ Q(D) holds whatever P(D) is when Q(D) is true.

Only have to turn over cards for Bob and Charlie.

Page 299: 70: Discrete Math and Probability.

Back to: Wason’s experiment:1Theory:“If a person travels to Chicago, he/she flies.”

Suppose you see that Alice went to Baltimore, Bob drove, Charliewent to Chicago, and Donna flew.

Which cards do you need to flip to test the theory?

P(x) = “Person x went to Chicago.” Q(x) = “Person x flew”

Statement/theory: ∀x ∈ {A,B,C,D},P(x) =⇒ Q(x)

P(A) = False . Do we care about Q(A)?No. P(A) =⇒ Q(A), when P(A) is False , Q(A) can be anything.

Q(B) = False . Do we care about P(B)?Yes. P(B) =⇒ Q(B) ≡ ¬Q(B) =⇒ ¬P(B).So P(Bob) must be False .

P(C) = True . Do we care about P(C)?Yes. P(C) =⇒ Q(C) means Q(C) must be true.

Q(D) = True . Do we care about P(D)?

No. P(D) =⇒ Q(D) holds whatever P(D) is when Q(D) is true.

Only have to turn over cards for Bob and Charlie.

Page 300: 70: Discrete Math and Probability.

Back to: Wason’s experiment:1Theory:“If a person travels to Chicago, he/she flies.”

Suppose you see that Alice went to Baltimore, Bob drove, Charliewent to Chicago, and Donna flew.

Which cards do you need to flip to test the theory?

P(x) = “Person x went to Chicago.” Q(x) = “Person x flew”

Statement/theory: ∀x ∈ {A,B,C,D},P(x) =⇒ Q(x)

P(A) = False . Do we care about Q(A)?No. P(A) =⇒ Q(A), when P(A) is False , Q(A) can be anything.

Q(B) = False . Do we care about P(B)?Yes. P(B) =⇒ Q(B) ≡ ¬Q(B) =⇒ ¬P(B).So P(Bob) must be False .

P(C) = True . Do we care about P(C)?Yes. P(C) =⇒ Q(C) means Q(C) must be true.

Q(D) = True . Do we care about P(D)?No.

P(D) =⇒ Q(D) holds whatever P(D) is when Q(D) is true.

Only have to turn over cards for Bob and Charlie.

Page 301: 70: Discrete Math and Probability.

Back to: Wason’s experiment:1Theory:“If a person travels to Chicago, he/she flies.”

Suppose you see that Alice went to Baltimore, Bob drove, Charliewent to Chicago, and Donna flew.

Which cards do you need to flip to test the theory?

P(x) = “Person x went to Chicago.” Q(x) = “Person x flew”

Statement/theory: ∀x ∈ {A,B,C,D},P(x) =⇒ Q(x)

P(A) = False . Do we care about Q(A)?No. P(A) =⇒ Q(A), when P(A) is False , Q(A) can be anything.

Q(B) = False . Do we care about P(B)?Yes. P(B) =⇒ Q(B) ≡ ¬Q(B) =⇒ ¬P(B).So P(Bob) must be False .

P(C) = True . Do we care about P(C)?Yes. P(C) =⇒ Q(C) means Q(C) must be true.

Q(D) = True . Do we care about P(D)?No. P(D) =⇒ Q(D) holds whatever P(D) is when Q(D) is true.

Only have to turn over cards for Bob and Charlie.

Page 302: 70: Discrete Math and Probability.

Back to: Wason’s experiment:1Theory:“If a person travels to Chicago, he/she flies.”

Suppose you see that Alice went to Baltimore, Bob drove, Charliewent to Chicago, and Donna flew.

Which cards do you need to flip to test the theory?

P(x) = “Person x went to Chicago.” Q(x) = “Person x flew”

Statement/theory: ∀x ∈ {A,B,C,D},P(x) =⇒ Q(x)

P(A) = False . Do we care about Q(A)?No. P(A) =⇒ Q(A), when P(A) is False , Q(A) can be anything.

Q(B) = False . Do we care about P(B)?Yes. P(B) =⇒ Q(B) ≡ ¬Q(B) =⇒ ¬P(B).So P(Bob) must be False .

P(C) = True . Do we care about P(C)?Yes. P(C) =⇒ Q(C) means Q(C) must be true.

Q(D) = True . Do we care about P(D)?No. P(D) =⇒ Q(D) holds whatever P(D) is when Q(D) is true.

Only have to turn over cards for Bob and Charlie.

Page 303: 70: Discrete Math and Probability.

More for all quantifiers examples.

I “doubling a number always makes it larger”

(∀x ∈ N) (2x > x) False Consider x = 0

Can fix statement...

(∀x ∈ N) (2x ≥x) True

I “Square of any natural number greater than 5 is greater than 25.”

(∀x ∈ N)(x > 5 =⇒ x2 > 25).

Idea alert: Restrict domain using implication.

Note that we may omit universe if clear from context.

Page 304: 70: Discrete Math and Probability.

More for all quantifiers examples.

I “doubling a number always makes it larger”

(∀x ∈ N) (2x > x) False Consider x = 0

Can fix statement...

(∀x ∈ N) (2x≥x) True

I “Square of any natural number greater than 5 is greater than 25.”

(∀x ∈ N)(x > 5 =⇒ x2 > 25).

Idea alert: Restrict domain using implication.

Note that we may omit universe if clear from context.

Page 305: 70: Discrete Math and Probability.

More for all quantifiers examples.

I “doubling a number always makes it larger”

(∀x ∈ N) (2x > x)

False Consider x = 0

Can fix statement...

(∀x ∈ N) (2x≥x) True

I “Square of any natural number greater than 5 is greater than 25.”

(∀x ∈ N)(x > 5 =⇒ x2 > 25).

Idea alert: Restrict domain using implication.

Note that we may omit universe if clear from context.

Page 306: 70: Discrete Math and Probability.

More for all quantifiers examples.

I “doubling a number always makes it larger”

(∀x ∈ N) (2x > x) False

Consider x = 0

Can fix statement...

(∀x ∈ N) (2x≥x) True

I “Square of any natural number greater than 5 is greater than 25.”

(∀x ∈ N)(x > 5 =⇒ x2 > 25).

Idea alert: Restrict domain using implication.

Note that we may omit universe if clear from context.

Page 307: 70: Discrete Math and Probability.

More for all quantifiers examples.

I “doubling a number always makes it larger”

(∀x ∈ N) (2x > x) False Consider x = 0

Can fix statement...

(∀x ∈ N) (2x≥x) True

I “Square of any natural number greater than 5 is greater than 25.”

(∀x ∈ N)(x > 5 =⇒ x2 > 25).

Idea alert: Restrict domain using implication.

Note that we may omit universe if clear from context.

Page 308: 70: Discrete Math and Probability.

More for all quantifiers examples.

I “doubling a number always makes it larger”

(∀x ∈ N) (2x > x) False Consider x = 0

Can fix statement...

(∀x ∈ N) (2x≥x) True

I “Square of any natural number greater than 5 is greater than 25.”

(∀x ∈ N)(x > 5 =⇒ x2 > 25).

Idea alert: Restrict domain using implication.

Note that we may omit universe if clear from context.

Page 309: 70: Discrete Math and Probability.

More for all quantifiers examples.

I “doubling a number always makes it larger”

(∀x ∈ N) (2x > x) False Consider x = 0

Can fix statement...

(∀x ∈ N) (2x≥x)

True

I “Square of any natural number greater than 5 is greater than 25.”

(∀x ∈ N)(x > 5 =⇒ x2 > 25).

Idea alert: Restrict domain using implication.

Note that we may omit universe if clear from context.

Page 310: 70: Discrete Math and Probability.

More for all quantifiers examples.

I “doubling a number always makes it larger”

(∀x ∈ N) (2x > x) False Consider x = 0

Can fix statement...

(∀x ∈ N) (2x≥x) True

I “Square of any natural number greater than 5 is greater than 25.”

(∀x ∈ N)(x > 5 =⇒ x2 > 25).

Idea alert: Restrict domain using implication.

Note that we may omit universe if clear from context.

Page 311: 70: Discrete Math and Probability.

More for all quantifiers examples.

I “doubling a number always makes it larger”

(∀x ∈ N) (2x > x) False Consider x = 0

Can fix statement...

(∀x ∈ N) (2x≥x) True

I “Square of any natural number greater than 5 is greater than 25.”

(∀x ∈ N)(x > 5 =⇒ x2 > 25).

Idea alert: Restrict domain using implication.

Note that we may omit universe if clear from context.

Page 312: 70: Discrete Math and Probability.

More for all quantifiers examples.

I “doubling a number always makes it larger”

(∀x ∈ N) (2x > x) False Consider x = 0

Can fix statement...

(∀x ∈ N) (2x≥x) True

I “Square of any natural number greater than 5 is greater than 25.”

(∀x ∈ N)

(x > 5 =⇒ x2 > 25).

Idea alert: Restrict domain using implication.

Note that we may omit universe if clear from context.

Page 313: 70: Discrete Math and Probability.

More for all quantifiers examples.

I “doubling a number always makes it larger”

(∀x ∈ N) (2x > x) False Consider x = 0

Can fix statement...

(∀x ∈ N) (2x≥x) True

I “Square of any natural number greater than 5 is greater than 25.”

(∀x ∈ N)(x > 5

=⇒ x2 > 25).

Idea alert: Restrict domain using implication.

Note that we may omit universe if clear from context.

Page 314: 70: Discrete Math and Probability.

More for all quantifiers examples.

I “doubling a number always makes it larger”

(∀x ∈ N) (2x > x) False Consider x = 0

Can fix statement...

(∀x ∈ N) (2x≥x) True

I “Square of any natural number greater than 5 is greater than 25.”

(∀x ∈ N)(x > 5 =⇒

x2 > 25).

Idea alert: Restrict domain using implication.

Note that we may omit universe if clear from context.

Page 315: 70: Discrete Math and Probability.

More for all quantifiers examples.

I “doubling a number always makes it larger”

(∀x ∈ N) (2x > x) False Consider x = 0

Can fix statement...

(∀x ∈ N) (2x≥x) True

I “Square of any natural number greater than 5 is greater than 25.”

(∀x ∈ N)(x > 5 =⇒ x2 > 25).

Idea alert: Restrict domain using implication.

Note that we may omit universe if clear from context.

Page 316: 70: Discrete Math and Probability.

More for all quantifiers examples.

I “doubling a number always makes it larger”

(∀x ∈ N) (2x > x) False Consider x = 0

Can fix statement...

(∀x ∈ N) (2x≥x) True

I “Square of any natural number greater than 5 is greater than 25.”

(∀x ∈ N)(x > 5 =⇒ x2 > 25).

Idea alert:

Restrict domain using implication.

Note that we may omit universe if clear from context.

Page 317: 70: Discrete Math and Probability.

More for all quantifiers examples.

I “doubling a number always makes it larger”

(∀x ∈ N) (2x > x) False Consider x = 0

Can fix statement...

(∀x ∈ N) (2x≥x) True

I “Square of any natural number greater than 5 is greater than 25.”

(∀x ∈ N)(x > 5 =⇒ x2 > 25).

Idea alert: Restrict domain using implication.

Note that we may omit universe if clear from context.

Page 318: 70: Discrete Math and Probability.

More for all quantifiers examples.

I “doubling a number always makes it larger”

(∀x ∈ N) (2x > x) False Consider x = 0

Can fix statement...

(∀x ∈ N) (2x≥x) True

I “Square of any natural number greater than 5 is greater than 25.”

(∀x ∈ N)(x > 5 =⇒ x2 > 25).

Idea alert: Restrict domain using implication.

Note that we may omit universe if clear from context.

Page 319: 70: Discrete Math and Probability.

Quantifiers..not commutative.

I In English: “there is a natural number that is the square of everynatural number”.

(∃y ∈ N) (∀x ∈ N) (y = x2) False

I In English: “the square of every natural number is a naturalnumber.”

(∀x ∈ N)(∃y ∈ N) (y = x2) True

Page 320: 70: Discrete Math and Probability.

Quantifiers..not commutative.

I In English: “there is a natural number that is the square of everynatural number”.

(∃y ∈ N)

(∀x ∈ N) (y = x2) False

I In English: “the square of every natural number is a naturalnumber.”

(∀x ∈ N)(∃y ∈ N) (y = x2) True

Page 321: 70: Discrete Math and Probability.

Quantifiers..not commutative.

I In English: “there is a natural number that is the square of everynatural number”.

(∃y ∈ N) (∀x ∈ N)

(y = x2) False

I In English: “the square of every natural number is a naturalnumber.”

(∀x ∈ N)(∃y ∈ N) (y = x2) True

Page 322: 70: Discrete Math and Probability.

Quantifiers..not commutative.

I In English: “there is a natural number that is the square of everynatural number”.

(∃y ∈ N) (∀x ∈ N) (y = x2)

False

I In English: “the square of every natural number is a naturalnumber.”

(∀x ∈ N)(∃y ∈ N) (y = x2) True

Page 323: 70: Discrete Math and Probability.

Quantifiers..not commutative.

I In English: “there is a natural number that is the square of everynatural number”.

(∃y ∈ N) (∀x ∈ N) (y = x2) False

I In English: “the square of every natural number is a naturalnumber.”

(∀x ∈ N)(∃y ∈ N) (y = x2) True

Page 324: 70: Discrete Math and Probability.

Quantifiers..not commutative.

I In English: “there is a natural number that is the square of everynatural number”.

(∃y ∈ N) (∀x ∈ N) (y = x2) False

I In English: “the square of every natural number is a naturalnumber.”

(∀x ∈ N)(∃y ∈ N) (y = x2) True

Page 325: 70: Discrete Math and Probability.

Quantifiers..not commutative.

I In English: “there is a natural number that is the square of everynatural number”.

(∃y ∈ N) (∀x ∈ N) (y = x2) False

I In English: “the square of every natural number is a naturalnumber.”

(∀x ∈ N)

(∃y ∈ N) (y = x2) True

Page 326: 70: Discrete Math and Probability.

Quantifiers..not commutative.

I In English: “there is a natural number that is the square of everynatural number”.

(∃y ∈ N) (∀x ∈ N) (y = x2) False

I In English: “the square of every natural number is a naturalnumber.”

(∀x ∈ N)(∃y ∈ N)

(y = x2) True

Page 327: 70: Discrete Math and Probability.

Quantifiers..not commutative.

I In English: “there is a natural number that is the square of everynatural number”.

(∃y ∈ N) (∀x ∈ N) (y = x2) False

I In English: “the square of every natural number is a naturalnumber.”

(∀x ∈ N)(∃y ∈ N) (y = x2)

True

Page 328: 70: Discrete Math and Probability.

Quantifiers..not commutative.

I In English: “there is a natural number that is the square of everynatural number”.

(∃y ∈ N) (∀x ∈ N) (y = x2) False

I In English: “the square of every natural number is a naturalnumber.”

(∀x ∈ N)(∃y ∈ N) (y = x2) True

Page 329: 70: Discrete Math and Probability.

Quantifiers..not commutative.

I In English: “there is a natural number that is the square of everynatural number”.

(∃y ∈ N) (∀x ∈ N) (y = x2) False

I In English: “the square of every natural number is a naturalnumber.”

(∀x ∈ N)(∃y ∈ N) (y = x2) True

Page 330: 70: Discrete Math and Probability.

Quantifiers....negation...DeMorgan again.

Consider¬(∀x ∈ S)(P(x)),

English: there is an x in S where P(x) does not hold.

That is,¬(∀x ∈ S)(P(x)) ⇐⇒ ∃(x ∈ S)(¬P(x)).

What we do in this course! We consider claims.

Claim: (∀x) P(x) “For all inputs x the program works.”For False , find x , where ¬P(x).

Counterexample.Bad input.Case that illustrates bug.

For True : prove claim. Next lectures...

Page 331: 70: Discrete Math and Probability.

Quantifiers....negation...DeMorgan again.

Consider¬(∀x ∈ S)(P(x)),

English: there is an x in S where P(x) does not hold.

That is,¬(∀x ∈ S)(P(x)) ⇐⇒ ∃(x ∈ S)(¬P(x)).

What we do in this course! We consider claims.

Claim: (∀x) P(x) “For all inputs x the program works.”For False , find x , where ¬P(x).

Counterexample.Bad input.Case that illustrates bug.

For True : prove claim. Next lectures...

Page 332: 70: Discrete Math and Probability.

Quantifiers....negation...DeMorgan again.

Consider¬(∀x ∈ S)(P(x)),

English: there is an x in S where P(x) does not hold.

That is,

¬(∀x ∈ S)(P(x)) ⇐⇒ ∃(x ∈ S)(¬P(x)).

What we do in this course! We consider claims.

Claim: (∀x) P(x) “For all inputs x the program works.”For False , find x , where ¬P(x).

Counterexample.Bad input.Case that illustrates bug.

For True : prove claim. Next lectures...

Page 333: 70: Discrete Math and Probability.

Quantifiers....negation...DeMorgan again.

Consider¬(∀x ∈ S)(P(x)),

English: there is an x in S where P(x) does not hold.

That is,¬(∀x ∈ S)(P(x)) ⇐⇒ ∃(x ∈ S)(¬P(x)).

What we do in this course! We consider claims.

Claim: (∀x) P(x) “For all inputs x the program works.”For False , find x , where ¬P(x).

Counterexample.Bad input.Case that illustrates bug.

For True : prove claim. Next lectures...

Page 334: 70: Discrete Math and Probability.

Quantifiers....negation...DeMorgan again.

Consider¬(∀x ∈ S)(P(x)),

English: there is an x in S where P(x) does not hold.

That is,¬(∀x ∈ S)(P(x)) ⇐⇒ ∃(x ∈ S)(¬P(x)).

What we do in this course! We consider claims.

Claim: (∀x) P(x) “For all inputs x the program works.”For False , find x , where ¬P(x).

Counterexample.Bad input.Case that illustrates bug.

For True : prove claim. Next lectures...

Page 335: 70: Discrete Math and Probability.

Quantifiers....negation...DeMorgan again.

Consider¬(∀x ∈ S)(P(x)),

English: there is an x in S where P(x) does not hold.

That is,¬(∀x ∈ S)(P(x)) ⇐⇒ ∃(x ∈ S)(¬P(x)).

What we do in this course! We consider claims.

Claim: (∀x) P(x)

“For all inputs x the program works.”For False , find x , where ¬P(x).

Counterexample.Bad input.Case that illustrates bug.

For True : prove claim. Next lectures...

Page 336: 70: Discrete Math and Probability.

Quantifiers....negation...DeMorgan again.

Consider¬(∀x ∈ S)(P(x)),

English: there is an x in S where P(x) does not hold.

That is,¬(∀x ∈ S)(P(x)) ⇐⇒ ∃(x ∈ S)(¬P(x)).

What we do in this course! We consider claims.

Claim: (∀x) P(x) “For all inputs x the program works.”

For False , find x , where ¬P(x).Counterexample.Bad input.Case that illustrates bug.

For True : prove claim. Next lectures...

Page 337: 70: Discrete Math and Probability.

Quantifiers....negation...DeMorgan again.

Consider¬(∀x ∈ S)(P(x)),

English: there is an x in S where P(x) does not hold.

That is,¬(∀x ∈ S)(P(x)) ⇐⇒ ∃(x ∈ S)(¬P(x)).

What we do in this course! We consider claims.

Claim: (∀x) P(x) “For all inputs x the program works.”For False , find x , where ¬P(x).

Counterexample.Bad input.Case that illustrates bug.

For True : prove claim. Next lectures...

Page 338: 70: Discrete Math and Probability.

Quantifiers....negation...DeMorgan again.

Consider¬(∀x ∈ S)(P(x)),

English: there is an x in S where P(x) does not hold.

That is,¬(∀x ∈ S)(P(x)) ⇐⇒ ∃(x ∈ S)(¬P(x)).

What we do in this course! We consider claims.

Claim: (∀x) P(x) “For all inputs x the program works.”For False , find x , where ¬P(x).

Counterexample.

Bad input.Case that illustrates bug.

For True : prove claim. Next lectures...

Page 339: 70: Discrete Math and Probability.

Quantifiers....negation...DeMorgan again.

Consider¬(∀x ∈ S)(P(x)),

English: there is an x in S where P(x) does not hold.

That is,¬(∀x ∈ S)(P(x)) ⇐⇒ ∃(x ∈ S)(¬P(x)).

What we do in this course! We consider claims.

Claim: (∀x) P(x) “For all inputs x the program works.”For False , find x , where ¬P(x).

Counterexample.Bad input.

Case that illustrates bug.For True : prove claim. Next lectures...

Page 340: 70: Discrete Math and Probability.

Quantifiers....negation...DeMorgan again.

Consider¬(∀x ∈ S)(P(x)),

English: there is an x in S where P(x) does not hold.

That is,¬(∀x ∈ S)(P(x)) ⇐⇒ ∃(x ∈ S)(¬P(x)).

What we do in this course! We consider claims.

Claim: (∀x) P(x) “For all inputs x the program works.”For False , find x , where ¬P(x).

Counterexample.Bad input.Case that illustrates bug.

For True : prove claim. Next lectures...

Page 341: 70: Discrete Math and Probability.

Quantifiers....negation...DeMorgan again.

Consider¬(∀x ∈ S)(P(x)),

English: there is an x in S where P(x) does not hold.

That is,¬(∀x ∈ S)(P(x)) ⇐⇒ ∃(x ∈ S)(¬P(x)).

What we do in this course! We consider claims.

Claim: (∀x) P(x) “For all inputs x the program works.”For False , find x , where ¬P(x).

Counterexample.Bad input.Case that illustrates bug.

For True : prove claim.

Next lectures...

Page 342: 70: Discrete Math and Probability.

Quantifiers....negation...DeMorgan again.

Consider¬(∀x ∈ S)(P(x)),

English: there is an x in S where P(x) does not hold.

That is,¬(∀x ∈ S)(P(x)) ⇐⇒ ∃(x ∈ S)(¬P(x)).

What we do in this course! We consider claims.

Claim: (∀x) P(x) “For all inputs x the program works.”For False , find x , where ¬P(x).

Counterexample.Bad input.Case that illustrates bug.

For True : prove claim. Next lectures...

Page 343: 70: Discrete Math and Probability.

Negation of exists.

Consider

¬(∃x ∈ S)(P(x))

English: means that for all x in S , P(x) does not hold.

That is,¬(∃x ∈ S)(P(x)) ⇐⇒ ∀(x ∈ S)¬P(x).

Page 344: 70: Discrete Math and Probability.

Negation of exists.

Consider

¬(∃x ∈ S)(P(x))

English: means that for all x in S , P(x) does not hold.

That is,¬(∃x ∈ S)(P(x)) ⇐⇒ ∀(x ∈ S)¬P(x).

Page 345: 70: Discrete Math and Probability.

Negation of exists.

Consider

¬(∃x ∈ S)(P(x))

English: means that for all x in S , P(x) does not hold.

That is,¬(∃x ∈ S)(P(x)) ⇐⇒ ∀(x ∈ S)¬P(x).

Page 346: 70: Discrete Math and Probability.

Negation of exists.

Consider

¬(∃x ∈ S)(P(x))

English: means that for all x in S , P(x) does not hold.

That is,¬(∃x ∈ S)(P(x)) ⇐⇒ ∀(x ∈ S)¬P(x).

Page 347: 70: Discrete Math and Probability.

Which Theorem?

Theorem: (∀n ∈ N) ¬(∃a,b,c ∈ N) (n ≥ 3 =⇒ an +bn = cn)

Which Theorem?

Fermat’s Last Theorem!

Remember Special Triangles: for n = 2, we have 3,4,5 and 5,7, 12and ...

1637: Proof doesn’t fit in the margins.

1993: Wiles ...(based in part on Ribet’s Theorem)

DeMorgan Restatement:Theorem: ¬(∃n ∈ N) (∃a,b,c ∈ N) (n ≥ 3 =⇒ an +bn = cn)

Page 348: 70: Discrete Math and Probability.

Which Theorem?

Theorem: (∀n ∈ N) ¬(∃a,b,c ∈ N) (n ≥ 3 =⇒ an +bn = cn)

Which Theorem?

Fermat’s Last Theorem!

Remember Special Triangles: for n = 2, we have 3,4,5 and 5,7, 12and ...

1637: Proof doesn’t fit in the margins.

1993: Wiles ...(based in part on Ribet’s Theorem)

DeMorgan Restatement:Theorem: ¬(∃n ∈ N) (∃a,b,c ∈ N) (n ≥ 3 =⇒ an +bn = cn)

Page 349: 70: Discrete Math and Probability.

Which Theorem?

Theorem: (∀n ∈ N) ¬(∃a,b,c ∈ N) (n ≥ 3 =⇒ an +bn = cn)

Which Theorem?

Fermat’s Last Theorem!

Remember Special Triangles: for n = 2, we have 3,4,5 and 5,7, 12and ...

1637: Proof doesn’t fit in the margins.

1993: Wiles ...(based in part on Ribet’s Theorem)

DeMorgan Restatement:Theorem: ¬(∃n ∈ N) (∃a,b,c ∈ N) (n ≥ 3 =⇒ an +bn = cn)

Page 350: 70: Discrete Math and Probability.

Which Theorem?

Theorem: (∀n ∈ N) ¬(∃a,b,c ∈ N) (n ≥ 3 =⇒ an +bn = cn)

Which Theorem?

Fermat’s Last Theorem!

Remember Special Triangles: for n = 2, we have 3,4,5 and 5,7, 12and ...

1637: Proof doesn’t fit in the margins.

1993: Wiles ...(based in part on Ribet’s Theorem)

DeMorgan Restatement:Theorem: ¬(∃n ∈ N) (∃a,b,c ∈ N) (n ≥ 3 =⇒ an +bn = cn)

Page 351: 70: Discrete Math and Probability.

Which Theorem?

Theorem: (∀n ∈ N) ¬(∃a,b,c ∈ N) (n ≥ 3 =⇒ an +bn = cn)

Which Theorem?

Fermat’s Last Theorem!

Remember Special Triangles: for n = 2, we have 3,4,5 and 5,7, 12and ...

1637: Proof doesn’t fit in the margins.

1993: Wiles ...(based in part on Ribet’s Theorem)

DeMorgan Restatement:Theorem: ¬(∃n ∈ N) (∃a,b,c ∈ N) (n ≥ 3 =⇒ an +bn = cn)

Page 352: 70: Discrete Math and Probability.

Which Theorem?

Theorem: (∀n ∈ N) ¬(∃a,b,c ∈ N) (n ≥ 3 =⇒ an +bn = cn)

Which Theorem?

Fermat’s Last Theorem!

Remember Special Triangles: for n = 2, we have 3,4,5 and 5,7, 12and ...

1637: Proof doesn’t fit in the margins.

1993: Wiles ...(based in part on Ribet’s Theorem)

DeMorgan Restatement:Theorem: ¬(∃n ∈ N) (∃a,b,c ∈ N) (n ≥ 3 =⇒ an +bn = cn)

Page 353: 70: Discrete Math and Probability.

Which Theorem?

Theorem: (∀n ∈ N) ¬(∃a,b,c ∈ N) (n ≥ 3 =⇒ an +bn = cn)

Which Theorem?

Fermat’s Last Theorem!

Remember Special Triangles: for n = 2, we have 3,4,5 and 5,7, 12and ...

1637: Proof doesn’t fit in the margins.

1993: Wiles ...(based in part on Ribet’s Theorem)

DeMorgan Restatement:

Theorem: ¬(∃n ∈ N) (∃a,b,c ∈ N) (n ≥ 3 =⇒ an +bn = cn)

Page 354: 70: Discrete Math and Probability.

Which Theorem?

Theorem: (∀n ∈ N) ¬(∃a,b,c ∈ N) (n ≥ 3 =⇒ an +bn = cn)

Which Theorem?

Fermat’s Last Theorem!

Remember Special Triangles: for n = 2, we have 3,4,5 and 5,7, 12and ...

1637: Proof doesn’t fit in the margins.

1993: Wiles ...(based in part on Ribet’s Theorem)

DeMorgan Restatement:Theorem: ¬(∃n ∈ N) (∃a,b,c ∈ N) (n ≥ 3 =⇒ an +bn = cn)

Page 355: 70: Discrete Math and Probability.

Summary.Propositions are statements that are true or false.

Proprositional forms use ∧,∨,¬.

Propositional forms correspond to truth tables.

Logical equivalence of forms means same truth tables.

Implication: P =⇒ Q ⇐⇒ ¬P ∨Q.

Contrapositive: ¬Q =⇒ ¬PConverse: Q =⇒ P

Predicates: Statements with “free” variables.

Quantifiers: ∀x P(x), ∃y Q(y)

Now can state theorems! And disprove false ones!

DeMorgans Laws: “Flip and Distribute negation”¬(P ∨Q) ⇐⇒ (¬P ∧¬Q)¬∀x P(x) ⇐⇒ ∃x ¬P(x).

Next Time: proofs!

Page 356: 70: Discrete Math and Probability.

Summary.Propositions are statements that are true or false.

Proprositional forms use ∧,∨,¬.

Propositional forms correspond to truth tables.

Logical equivalence of forms means same truth tables.

Implication: P =⇒ Q ⇐⇒ ¬P ∨Q.

Contrapositive: ¬Q =⇒ ¬PConverse: Q =⇒ P

Predicates: Statements with “free” variables.

Quantifiers: ∀x P(x), ∃y Q(y)

Now can state theorems! And disprove false ones!

DeMorgans Laws: “Flip and Distribute negation”¬(P ∨Q) ⇐⇒ (¬P ∧¬Q)¬∀x P(x) ⇐⇒ ∃x ¬P(x).

Next Time: proofs!

Page 357: 70: Discrete Math and Probability.

Summary.Propositions are statements that are true or false.

Proprositional forms use ∧,∨,¬.

Propositional forms correspond to truth tables.

Logical equivalence of forms means same truth tables.

Implication: P =⇒ Q ⇐⇒ ¬P ∨Q.

Contrapositive: ¬Q =⇒ ¬PConverse: Q =⇒ P

Predicates: Statements with “free” variables.

Quantifiers: ∀x P(x), ∃y Q(y)

Now can state theorems! And disprove false ones!

DeMorgans Laws: “Flip and Distribute negation”¬(P ∨Q) ⇐⇒ (¬P ∧¬Q)¬∀x P(x) ⇐⇒ ∃x ¬P(x).

Next Time: proofs!

Page 358: 70: Discrete Math and Probability.

Summary.Propositions are statements that are true or false.

Proprositional forms use ∧,∨,¬.

Propositional forms correspond to truth tables.

Logical equivalence of forms means same truth tables.

Implication: P =⇒ Q ⇐⇒ ¬P ∨Q.

Contrapositive: ¬Q =⇒ ¬PConverse: Q =⇒ P

Predicates: Statements with “free” variables.

Quantifiers: ∀x P(x), ∃y Q(y)

Now can state theorems! And disprove false ones!

DeMorgans Laws: “Flip and Distribute negation”¬(P ∨Q) ⇐⇒ (¬P ∧¬Q)¬∀x P(x) ⇐⇒ ∃x ¬P(x).

Next Time: proofs!

Page 359: 70: Discrete Math and Probability.

Summary.Propositions are statements that are true or false.

Proprositional forms use ∧,∨,¬.

Propositional forms correspond to truth tables.

Logical equivalence of forms means same truth tables.

Implication: P =⇒ Q

⇐⇒ ¬P ∨Q.

Contrapositive: ¬Q =⇒ ¬PConverse: Q =⇒ P

Predicates: Statements with “free” variables.

Quantifiers: ∀x P(x), ∃y Q(y)

Now can state theorems! And disprove false ones!

DeMorgans Laws: “Flip and Distribute negation”¬(P ∨Q) ⇐⇒ (¬P ∧¬Q)¬∀x P(x) ⇐⇒ ∃x ¬P(x).

Next Time: proofs!

Page 360: 70: Discrete Math and Probability.

Summary.Propositions are statements that are true or false.

Proprositional forms use ∧,∨,¬.

Propositional forms correspond to truth tables.

Logical equivalence of forms means same truth tables.

Implication: P =⇒ Q ⇐⇒ ¬P ∨Q.

Contrapositive: ¬Q =⇒ ¬PConverse: Q =⇒ P

Predicates: Statements with “free” variables.

Quantifiers: ∀x P(x), ∃y Q(y)

Now can state theorems! And disprove false ones!

DeMorgans Laws: “Flip and Distribute negation”¬(P ∨Q) ⇐⇒ (¬P ∧¬Q)¬∀x P(x) ⇐⇒ ∃x ¬P(x).

Next Time: proofs!

Page 361: 70: Discrete Math and Probability.

Summary.Propositions are statements that are true or false.

Proprositional forms use ∧,∨,¬.

Propositional forms correspond to truth tables.

Logical equivalence of forms means same truth tables.

Implication: P =⇒ Q ⇐⇒ ¬P ∨Q.

Contrapositive: ¬Q =⇒ ¬P

Converse: Q =⇒ P

Predicates: Statements with “free” variables.

Quantifiers: ∀x P(x), ∃y Q(y)

Now can state theorems! And disprove false ones!

DeMorgans Laws: “Flip and Distribute negation”¬(P ∨Q) ⇐⇒ (¬P ∧¬Q)¬∀x P(x) ⇐⇒ ∃x ¬P(x).

Next Time: proofs!

Page 362: 70: Discrete Math and Probability.

Summary.Propositions are statements that are true or false.

Proprositional forms use ∧,∨,¬.

Propositional forms correspond to truth tables.

Logical equivalence of forms means same truth tables.

Implication: P =⇒ Q ⇐⇒ ¬P ∨Q.

Contrapositive: ¬Q =⇒ ¬PConverse: Q =⇒ P

Predicates: Statements with “free” variables.

Quantifiers: ∀x P(x), ∃y Q(y)

Now can state theorems! And disprove false ones!

DeMorgans Laws: “Flip and Distribute negation”¬(P ∨Q) ⇐⇒ (¬P ∧¬Q)¬∀x P(x) ⇐⇒ ∃x ¬P(x).

Next Time: proofs!

Page 363: 70: Discrete Math and Probability.

Summary.Propositions are statements that are true or false.

Proprositional forms use ∧,∨,¬.

Propositional forms correspond to truth tables.

Logical equivalence of forms means same truth tables.

Implication: P =⇒ Q ⇐⇒ ¬P ∨Q.

Contrapositive: ¬Q =⇒ ¬PConverse: Q =⇒ P

Predicates: Statements with “free” variables.

Quantifiers: ∀x P(x), ∃y Q(y)

Now can state theorems! And disprove false ones!

DeMorgans Laws: “Flip and Distribute negation”¬(P ∨Q) ⇐⇒ (¬P ∧¬Q)¬∀x P(x) ⇐⇒ ∃x ¬P(x).

Next Time: proofs!

Page 364: 70: Discrete Math and Probability.

Summary.Propositions are statements that are true or false.

Proprositional forms use ∧,∨,¬.

Propositional forms correspond to truth tables.

Logical equivalence of forms means same truth tables.

Implication: P =⇒ Q ⇐⇒ ¬P ∨Q.

Contrapositive: ¬Q =⇒ ¬PConverse: Q =⇒ P

Predicates: Statements with “free” variables.

Quantifiers: ∀x P(x), ∃y Q(y)

Now can state theorems! And disprove false ones!

DeMorgans Laws: “Flip and Distribute negation”¬(P ∨Q) ⇐⇒ (¬P ∧¬Q)¬∀x P(x) ⇐⇒ ∃x ¬P(x).

Next Time: proofs!

Page 365: 70: Discrete Math and Probability.

Summary.Propositions are statements that are true or false.

Proprositional forms use ∧,∨,¬.

Propositional forms correspond to truth tables.

Logical equivalence of forms means same truth tables.

Implication: P =⇒ Q ⇐⇒ ¬P ∨Q.

Contrapositive: ¬Q =⇒ ¬PConverse: Q =⇒ P

Predicates: Statements with “free” variables.

Quantifiers: ∀x P(x), ∃y Q(y)

Now can state theorems!

And disprove false ones!

DeMorgans Laws: “Flip and Distribute negation”¬(P ∨Q) ⇐⇒ (¬P ∧¬Q)¬∀x P(x) ⇐⇒ ∃x ¬P(x).

Next Time: proofs!

Page 366: 70: Discrete Math and Probability.

Summary.Propositions are statements that are true or false.

Proprositional forms use ∧,∨,¬.

Propositional forms correspond to truth tables.

Logical equivalence of forms means same truth tables.

Implication: P =⇒ Q ⇐⇒ ¬P ∨Q.

Contrapositive: ¬Q =⇒ ¬PConverse: Q =⇒ P

Predicates: Statements with “free” variables.

Quantifiers: ∀x P(x), ∃y Q(y)

Now can state theorems! And disprove false ones!

DeMorgans Laws: “Flip and Distribute negation”¬(P ∨Q) ⇐⇒ (¬P ∧¬Q)¬∀x P(x) ⇐⇒ ∃x ¬P(x).

Next Time: proofs!

Page 367: 70: Discrete Math and Probability.

Summary.Propositions are statements that are true or false.

Proprositional forms use ∧,∨,¬.

Propositional forms correspond to truth tables.

Logical equivalence of forms means same truth tables.

Implication: P =⇒ Q ⇐⇒ ¬P ∨Q.

Contrapositive: ¬Q =⇒ ¬PConverse: Q =⇒ P

Predicates: Statements with “free” variables.

Quantifiers: ∀x P(x), ∃y Q(y)

Now can state theorems! And disprove false ones!

DeMorgans Laws: “Flip and Distribute negation”

¬(P ∨Q) ⇐⇒ (¬P ∧¬Q)¬∀x P(x) ⇐⇒ ∃x ¬P(x).

Next Time: proofs!

Page 368: 70: Discrete Math and Probability.

Summary.Propositions are statements that are true or false.

Proprositional forms use ∧,∨,¬.

Propositional forms correspond to truth tables.

Logical equivalence of forms means same truth tables.

Implication: P =⇒ Q ⇐⇒ ¬P ∨Q.

Contrapositive: ¬Q =⇒ ¬PConverse: Q =⇒ P

Predicates: Statements with “free” variables.

Quantifiers: ∀x P(x), ∃y Q(y)

Now can state theorems! And disprove false ones!

DeMorgans Laws: “Flip and Distribute negation”¬(P ∨Q) ⇐⇒

(¬P ∧¬Q)¬∀x P(x) ⇐⇒ ∃x ¬P(x).

Next Time: proofs!

Page 369: 70: Discrete Math and Probability.

Summary.Propositions are statements that are true or false.

Proprositional forms use ∧,∨,¬.

Propositional forms correspond to truth tables.

Logical equivalence of forms means same truth tables.

Implication: P =⇒ Q ⇐⇒ ¬P ∨Q.

Contrapositive: ¬Q =⇒ ¬PConverse: Q =⇒ P

Predicates: Statements with “free” variables.

Quantifiers: ∀x P(x), ∃y Q(y)

Now can state theorems! And disprove false ones!

DeMorgans Laws: “Flip and Distribute negation”¬(P ∨Q) ⇐⇒ (¬P ∧¬Q)¬∀x P(x) ⇐⇒

∃x ¬P(x).

Next Time: proofs!

Page 370: 70: Discrete Math and Probability.

Summary.Propositions are statements that are true or false.

Proprositional forms use ∧,∨,¬.

Propositional forms correspond to truth tables.

Logical equivalence of forms means same truth tables.

Implication: P =⇒ Q ⇐⇒ ¬P ∨Q.

Contrapositive: ¬Q =⇒ ¬PConverse: Q =⇒ P

Predicates: Statements with “free” variables.

Quantifiers: ∀x P(x), ∃y Q(y)

Now can state theorems! And disprove false ones!

DeMorgans Laws: “Flip and Distribute negation”¬(P ∨Q) ⇐⇒ (¬P ∧¬Q)¬∀x P(x) ⇐⇒ ∃x ¬P(x).

Next Time: proofs!

Page 371: 70: Discrete Math and Probability.

Summary.Propositions are statements that are true or false.

Proprositional forms use ∧,∨,¬.

Propositional forms correspond to truth tables.

Logical equivalence of forms means same truth tables.

Implication: P =⇒ Q ⇐⇒ ¬P ∨Q.

Contrapositive: ¬Q =⇒ ¬PConverse: Q =⇒ P

Predicates: Statements with “free” variables.

Quantifiers: ∀x P(x), ∃y Q(y)

Now can state theorems! And disprove false ones!

DeMorgans Laws: “Flip and Distribute negation”¬(P ∨Q) ⇐⇒ (¬P ∧¬Q)¬∀x P(x) ⇐⇒ ∃x ¬P(x).

Next Time: proofs!