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: Họ : Pp-0 H : PD - 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 (Mp) 1.4 Estimated population standard deviation of the differences (s) 1.6 Estimated standard error of the mean differences (SMD) 0.2667 Degrees of freedom (df) 35 The t statistic 5.25 The critical values of t whena.05 2.030 Notice that since the t statistic (5.25) is in the critical region (t < -2.030 or:> 2.030), the hypothesis test is significant when a-.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 v effect size. Use r to calculate the effect size. The r is . This value of r 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.)