Hi Expert, I need help. Thanks A graduate student is interested in how viewing different kinds of scenery affects working memory. For her study, she selects a random sample of 49 adults. The subjects complete a test of working memory before and after walking in a nature setting. Before the walk, the mean score on the test of working memory was 9.4. After the walk, the mean score was 0.9 lower. The graduate student has no presupposed assumptions about how viewing different kinds of scenery affects working memory, so she 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 her hypothesis test: Mean difference (MDD) -0.9 Estimated population standard deviation of the differences (s) 1.8 Estimated standard error of the mean differences (sMDMD) 0.2571 Degrees of freedom (df) 48 The t statistic -3.50 The critical values of t when α = .05 ±2.011 Notice that since the t statistic (-3.50) is in the critical region (t < –2.011 or t > 2.011), 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.)

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Hi Expert, I need help. Thanks

A graduate student is interested in how viewing different kinds of scenery affects working memory. For her study, she selects a random sample of 49 adults. The subjects complete a test of working memory before and after walking in a nature setting.

Before the walk, the mean score on the test of working memory was 9.4. After the walk, the mean score was 0.9 lower.

The graduate student has no presupposed assumptions about how viewing different kinds of scenery affects working memory, so she 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 her hypothesis test:

Mean difference (MDD)	-0.9
Estimated population standard deviation of the differences (s)	1.8
Estimated standard error of the mean differences (sMDMD)	0.2571
Degrees of freedom (df)	48
The t statistic	-3.50
The critical values of t when α = .05	±2.011

Notice that since the t statistic (-3.50) is in the critical region (t < –2.011 or t > 2.011), 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.)

Definition Definition Measure of central tendency that is the average of a given data set. The mean value is evaluated as the quotient of the sum of all observations by the sample size. The mean, in contrast to a median, is affected by extreme values. Very large or very small values can distract the mean from the center of the data. Arithmetic mean: The most common type of mean is the arithmetic mean. It is evaluated using the formula: μ = 1 N ∑ i = 1 N x i Other types of means are the geometric mean, logarithmic mean, and harmonic mean. Geometric mean: The nth root of the product of n observations from a data set is defined as the geometric mean of the set: G = x 1 x 2 ... x n n Logarithmic mean: The difference of the natural logarithms of the two numbers, divided by the difference between the numbers is the logarithmic mean of the two numbers. The logarithmic mean is used particularly in heat transfer and mass transfer. ln x 2 − ln x 1 x 2 − x 1 Harmonic mean: The inverse of the arithmetic mean of the inverses of all the numbers in a data set is the harmonic mean of the data. 1 1 x 1 + 1 x 2 + ...

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