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STAT 104: Section 3 21 Feb, 2008 TF: Daniel Moon
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Page 1: STAT 104: Section 3 21 Feb, 2008 TF: Daniel Moon.

STAT 104: Section 3

21 Feb, 2008

TF: Daniel Moon

Page 2: STAT 104: Section 3 21 Feb, 2008 TF: Daniel Moon.

TF

Office hour: Th. PM 1:00-2:00, 6th Floor, 601

Page 3: STAT 104: Section 3 21 Feb, 2008 TF: Daniel Moon.

Agenda of Today Review

Feedback from Hw #2 Examples from 1st Mid-term Exam

Principles of experimental design Control Randomization Replication

Improvements Blocking (Stratification) Placebo Effect

Page 4: STAT 104: Section 3 21 Feb, 2008 TF: Daniel Moon.

Linear Regression Assumptions

Residuals have en expected value 0. Residuals are uncorrelated. Residuals have the same variance. To check these assumptions:

Residual plot (residuals vs. X): constant variance

Normal Prob. Plot: Normality

Page 5: STAT 104: Section 3 21 Feb, 2008 TF: Daniel Moon.

Linear Regression Assumptions

<Residual Plot> <Normal Probability Plot>

<Source: Fall 2006 Midterm 1, Q4>

Page 6: STAT 104: Section 3 21 Feb, 2008 TF: Daniel Moon.

Feedback on HW #2 When writing regression equation, don't forget y_hat

, if you don't include error term.

(Right: "y = a + bx + error" or "y_hat = a + bx") R is unit-free Problem 4.e. What is r^2?  (Y_hat = aX + b)

One unit increase in X leads to "a" unit increase in Y.

(not clear answer: for every $1 of revenue, the value of team goes up $2.59)

(--> (It's better say) for every $1 revenue increase, ")

Page 7: STAT 104: Section 3 21 Feb, 2008 TF: Daniel Moon.

Examples (2007 Midterm 1)

Page 8: STAT 104: Section 3 21 Feb, 2008 TF: Daniel Moon.

Examples (2007 Midterm 1)

Page 9: STAT 104: Section 3 21 Feb, 2008 TF: Daniel Moon.

Examples (2007 Midterm 1)

Page 10: STAT 104: Section 3 21 Feb, 2008 TF: Daniel Moon.

Review Feedback from Hw #2 Examples from 1st Mid-term Exam

Principles of experimental design Control Randomization Replication

Improvements Blocking (Stratification) Placebo Effect

Page 11: STAT 104: Section 3 21 Feb, 2008 TF: Daniel Moon.

Sources of Data

Anecdotal Information Available Data Observational Studies Controlled Experiments Randomized Controlled Experiments

Page 12: STAT 104: Section 3 21 Feb, 2008 TF: Daniel Moon.

Elements of Designing an Experiment

Subjects to be tested Observational or Randomized

Experimental? How many individuals? How will the individuals selected? What kind of variables measured?

Page 13: STAT 104: Section 3 21 Feb, 2008 TF: Daniel Moon.

Principles of experimental design

Page 14: STAT 104: Section 3 21 Feb, 2008 TF: Daniel Moon.

Control

Page 15: STAT 104: Section 3 21 Feb, 2008 TF: Daniel Moon.

Randomization

Page 16: STAT 104: Section 3 21 Feb, 2008 TF: Daniel Moon.

Randomization Example

Page 17: STAT 104: Section 3 21 Feb, 2008 TF: Daniel Moon.

Review Feedback from Hw #2 Examples from 1st Mid-term Exam

Principles of experimental design Control Randomization Replication

Improvements Blocking (Stratification) Placebo Effect

Page 18: STAT 104: Section 3 21 Feb, 2008 TF: Daniel Moon.

Blocking

Page 19: STAT 104: Section 3 21 Feb, 2008 TF: Daniel Moon.

Blocking Example

Page 20: STAT 104: Section 3 21 Feb, 2008 TF: Daniel Moon.

Examples (2007 Midterm 1)

Physical Activity Breast Cancer

Page 21: STAT 104: Section 3 21 Feb, 2008 TF: Daniel Moon.

Examples (2007 Midterm 1)

Observational Study

Lurking Variables

Other good lifestyle habits

Page 22: STAT 104: Section 3 21 Feb, 2008 TF: Daniel Moon.

Examples (2007 Midterm 1)

Page 23: STAT 104: Section 3 21 Feb, 2008 TF: Daniel Moon.

Examples (2007 Midterm 1)

Page 24: STAT 104: Section 3 21 Feb, 2008 TF: Daniel Moon.

Factors that we should consider

Sample Size Individuals Experimental Design Individuals Selected Response Variable

Page 25: STAT 104: Section 3 21 Feb, 2008 TF: Daniel Moon.

Examples (2006 Midterm 1)

Breakfast Performance

Page 26: STAT 104: Section 3 21 Feb, 2008 TF: Daniel Moon.

Examples (2006 Midterm 1)

Page 27: STAT 104: Section 3 21 Feb, 2008 TF: Daniel Moon.

Examples (2006 Midterm 1)

Page 28: STAT 104: Section 3 21 Feb, 2008 TF: Daniel Moon.

Examples (2006 Midterm 1)