A graduate student is interested in how viewing different types of scenes affects working memory. For his study, he selects a random sample of 36 adults. The subjects complete a series of working memory tests before and after walking in an urban setting. Before the walk, the mean score on the test of working memory was 9.1. After the walk, the mean score was 1.4 higher. The graduate student has no presupposed assumptions about how viewing different types of scenes affects working memory, so he formulates the null and alternative hypotheses as: H00 : μDD = 0 H11 : μDD ≠ 0 Assume that the data satisfy all of the required assumptions for a repeated-measures t test. The graduate student calculates the following statistics for his hypothesis test: Mean difference (MDD) 1.4 Estimated population standard deviation of the differences (s) 1.6 Estimated standard error of the mean differences (sMDMD) 0.2667 Degrees of freedom (df) 35 The t statistic 5.25 The critical values of t when α = .05 ±2.030 Notice that since the t statistic (5.25) is in the critical region (t < –2.030 or t > 2.030), the hypothesis test is significant when α = .05. A 95% confidence interval for the mean difference is . Use Cohen’s d to calculate the effect size. The absolute value of the estimated d is . Using Cohen’s criteria, this is a effect size. Use r² to calculate the effect size. The r² is . This value of r² means that, on average, % of the variability in the working memory is explained by whether it was measured before or after the walk. (Round to the nearest percent.)

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Description: A graduate student is interested in how viewing different types of scenes affects working memory. For his study, he selects a random sample of 36 adults. The subjects complete a series of working memory tests before and after walking in an urban sett

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter10: Statistics

Section10.4: Distributions Of Data

Problem 19PFA

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A graduate student is interested in how viewing different types of scenes affects working memory. For his study, he selects a random sample of 36 adults. The subjects complete a series of working memory tests before and after walking in an urban setting.

Before the walk, the mean score on the test of working memory was 9.1. After the walk, the mean score was 1.4 higher.

The graduate student has no presupposed assumptions about how viewing different types of scenes affects working memory, so he formulates the null and alternative hypotheses as:

H00 : μDD = 0

H11 : μDD ≠ 0

Assume that the data satisfy all of the required assumptions for a repeated-measures t test. The graduate student calculates the following statistics for his hypothesis test:

Mean difference (MDD)	1.4
Estimated population standard deviation of the differences (s)	1.6
Estimated standard error of the mean differences (sMDMD)	0.2667
Degrees of freedom (df)	35
The t statistic	5.25
The critical values of t when α = .05	±2.030

Notice that since the t statistic (5.25) is in the critical region (t < –2.030 or t > 2.030), the hypothesis test is significant when α = .05.

A 95% confidence interval for the mean difference is .

Use Cohen’s d to calculate the effect size. The absolute value of the estimated d is . Using Cohen’s criteria, this is a effect size.

Use r² to calculate the effect size. The r² is . This value of r² means that, on average,

% of the variability in the working memory is explained by whether it was measured before or after the walk. (Round to the nearest percent.)

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