# If you have access to SPSS, compute the Shapiro-Wilk test of normality for the variable age (as demonstrated in ). If you do not have

If you have access to SPSS, compute the Shapiro-Wilk test of normality for the variable age (as demonstrated in ). If you do not have access to SPSS, plot the frequency distributions by hand. What do the results indicate? The distribution significantly deviated from normality. The distribution did not significant from normality. Answer: State the null hypothesis where age at enrollment is used to predict the time for completion of an RN to BSN program. Answer: What is as computed by hand (or using SPSS)? 0.027 0.037 0.047 0.057 Answer: What is as computed by hand (or using SPSS)? 10.76 11.76 12.76 13.76 Answer: Write the new regression equation. = 0.037 10.76 = 0.047 11.76 Answer: How would you characterize the magnitude of the obtained value? Provide a rationale for your answer. value is very low. value is very high. Answer: How much variance in months to RN to BSN program completion is explained by knowing the student’s enrollment age? 1.2% 2.4% 12% 24% Answer: What was the correlation between the actual values and the predicted values using the new regression equation in the example? 0.11 0.155 0.346 0.49 Answer: Write your interpretation of the results as you would in an APA-formatted journal. Given the results of your analyses, would you use the calculated regression equation to predict future students’ program completion time by using enrollment age as ? Provide a rationale for your answer. Student age (x) did significantly predict months to completion (y). Therefore, the equation will accurately predict future values of y. Student age (x) did not significantly predict months to completion (y). Therefore, the equation will not accurately predict future values of y. Answer: Do the example data in Table 35-2 meet the assumptions for the Pearson χ test? Provide a rationale for your answer. Yes, the data meet the 2 assumptions. No, the data do not meet the 2 assumptions. Yes, the data meet the 3 assumptions. No, the data do not meet the 3 assumptions. Answer: Compute the χ test. What is the χ value? Answer: Is the χ2 significant at α = 0.05? Specify how you arrived at your answer. Yes, by comparing it with the critical value. No, by comparing it with the critical value. Answer: If using SPSS, what is the exact likelihood of obtaining the χ value at least as extreme as or as close to the one that was actually observed, assuming that the null hypothesis is true? 0.1% 0.5% 1% 5% Answer: Using the numbers in the contingency table, calculate the percentage of antibiotic users who tested positive for candiduria. 15.5% 47.6% 0% Answer: Using the numbers in the contingency table, calculate the percentage of non-antibiotic users who tested positive for candiduria. 15.5% 47.6% 0% Answer: Using the numbers in the contingency table, calculate the percentage of veterans with candiduria who had a history of antibiotic use. 0% 15% 100% Answer: Using the numbers in the contingency table, calculate the percentage of veterans with candiduria who had no history of antibiotic use. 0% 15% 100% Answer: Write your interpretation of the results as you would in an APA-formatted journal. Was the sample size adequate to detect differences between the two groups in this example? Provide a rationale for your answer. Answer: