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1 Objective Compare of two matched-paired means using two samples from each population. Hypothesis Tests and Confidence Intervals of two dependent means use the t-distribution Section 9.4 Inferences About Two Means (Matched Pairs)
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Section 9.4 Inferences About Two Means (Matched Pairs)

Dec 31, 2015

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Section 9.4 Inferences About Two Means (Matched Pairs). Objective Compare of two matched-paired means using two samples from each population. Hypothesis Tests and Confidence Intervals of two dependent means use the t -distribution. Definition. - PowerPoint PPT Presentation
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Page 1: Section 9.4 Inferences About Two Means (Matched Pairs)

1

Objective

Compare of two matched-paired means using two samples from each population.

Hypothesis Tests and Confidence Intervals of two dependent means use the t-distribution

Section 9.4Inferences About Two Means

(Matched Pairs)

Page 2: Section 9.4 Inferences About Two Means (Matched Pairs)

2

Definition

Two samples are dependent if there is some relationship between the two samples so that each value in one sample is paired with a corresponding value in the other sample.

Two samples can be treated as the matched pairs of values.

Page 3: Section 9.4 Inferences About Two Means (Matched Pairs)

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Examples

• Blood pressure of patients before they are given medicine and after they take it.

• Predicted temperature (by Weather Forecast) and the actual temperature.

• Heights of selected people in the morning and their heights by night time.

• Test scores of selected students in Calculus-I and their scores in Calculus-II.

Page 4: Section 9.4 Inferences About Two Means (Matched Pairs)

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Example 1

First sample: weights of 5 students in April

Second sample: their weights in September

These weights make 5 matched pairs

Third line: differences between April weights and September weights (net change in weight for each student, separately)

In our calculations we only use differences (d), not the values in the two samples.

Page 5: Section 9.4 Inferences About Two Means (Matched Pairs)

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Notation

d Individual difference between two matched paired values

μd Population mean for the difference of the two values.

n Number of paired values in sample

d Mean value of the differences in sample

sd Standard deviation of differences in sample

Page 6: Section 9.4 Inferences About Two Means (Matched Pairs)

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(1) The sample data are dependent (i.e. they make matched pairs)

(2) Either or both the following holds:

The number of matched pairs is large (n>30) orThe differences have a normal distribution

Requirements

All requirements must be satisfied to make a Hypothesis Test or to find a Confidence Interval

Page 7: Section 9.4 Inferences About Two Means (Matched Pairs)

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Tests for Two Dependent Means

Goal: Compare the mean of the differences

H0 : μd =

0

H1 : μd ≠ 0

Two tailed Left tailed Right tailed

H0 : μd =

0

H1 : μd < 0

H0 : μd =

0

H1 : μd > 0

Page 8: Section 9.4 Inferences About Two Means (Matched Pairs)

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t = d – µdsdn

degrees of freedom: df = n – 1

Note: d

= 0 according to H0

Finding the Test Statistic

Page 9: Section 9.4 Inferences About Two Means (Matched Pairs)

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Test Statistic

Note: Hypothesis Tests are done in same way as in Ch.8-5

Degrees of freedom df = n – 1

Page 10: Section 9.4 Inferences About Two Means (Matched Pairs)

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Steps for Performing a Hypothesis Test on Two Independent Means

• Write what we know

• State H0 and H1

• Draw a diagram

• Calculate the Sample Stats

• Find the Test Statistic

• Find the Critical Value(s)

• State the Initial Conclusion and Final Conclusion

Note: Same process as in Chapter 8

Page 11: Section 9.4 Inferences About Two Means (Matched Pairs)

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Example 1

Assume the differences in weight form a normal distribution.

Use a 0.05 significance level to test the claim that for the population of students, the mean change in weight from September to April is 0 kg (i.e. on average, there is no change)

Claim: μd = 0 using α = 0.05

Page 12: Section 9.4 Inferences About Two Means (Matched Pairs)

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H0 : µd = 0

H1 : µd ≠ 0t = 0.186

tα/2 = 2.78

t-dist.df = 4

Test Statistic

Critical Value

Initial Conclusion: Since t is not in the critical region, accept H0

Final Conclusion: We accept the claim that mean change in weight from

September to April is 0 kg.

-tα/2 = -2.78

Example 1

tα/2 = t0.025 = 2.78 (Using StatCrunch, df = 4)

d Data: -1 -1 4 -2 1

Sample Stats

n = 5 d = 0.2 sd = 2.387

Use StatCrunch: Stat – Summary Stats – Columns

Two-TailedH0 = Claim

Page 13: Section 9.4 Inferences About Two Means (Matched Pairs)

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H0 : µd = 0

H1 : µd ≠ 0Two-TailedH0 = Claim

Initial Conclusion: Since P-value is greater than α (0.05), accept H0

Final Conclusion: We accept the claim that mean change in weight from

September to April is 0 kg.

Example 1 d Data: -1 -1 4 -2 1Sample Stats

n = 5 d = 0.2 sd = 2.387

Use StatCrunch: Stat – Summary Stats – Columns

Null: proportion=

Alternative

Sample mean:

Sample std. dev.:

Sample size:

● Hypothesis Test0.2

2.387

5

0

P-value = 0.8605

Stat → T statistics→ One sample → With summary

Page 14: Section 9.4 Inferences About Two Means (Matched Pairs)

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Confidence Interval Estimate

We can observe how the two proportions relate by looking at the Confidence Interval Estimate of μ1–μ2

CI = ( d – E, d + E )

Page 15: Section 9.4 Inferences About Two Means (Matched Pairs)

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Example 2 Find the 95% Confidence Interval Estimate of μd from the data in Example 1

Sample Stats

n = 5 d = 0.2 sd = 2.387

CI = (-2.8, 3.2)

tα/2 = t0.025 = 2.78 (Using StatCrunch, df = 4)

Page 16: Section 9.4 Inferences About Two Means (Matched Pairs)

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Example 2 Find the 95% Confidence Interval Estimate of μd from the data in Example 1

Sample Stats

n = 5 d = 0.2 sd = 2.387

Level:Sample mean:

Sample std. dev.:

Sample size:

● Confidence Interval0.2

2.387

5

0.95

Stat → T statistics→ One sample → With summary

CI = (-2.8, 3.2)