# statistics 45

Statistics for Nursing Research: A Workbook for Evidence-Based Practice 2nd Edition
Exercise 29: Calculating Simple Linear Regression
The following questions refer to the section called Data for Additional Computational Practice in Exercise 29 of Grove & Cipher 2017.
1. If you have access to SPSS compute the Shapiro-Wilk test of normality for the variable age (as demonstrated in Exercise 26). If you do not have access to SPSS plot the frequency distributions by hand. What do the results indicate?
A. The distribution significantly deviated from normality.
B. The distribution did not significant from normality.
2. State the null hypothesis where age at enrollment is used to predict the time for completion of an RN to BSN program.
A. Age at enrollment predicts the number of months until completion of an RN to BSN program.
B. Age at enrollment does not predict the number of months until completion of an RN to BSN program.
3. What is b as computed by hand (or using SPSS)?
A. 0.027
B. 0.037
C. 0.047
D. 0.057
4. What is a as computed by hand (or using SPSS)?
A. 10.76
B. 11.76
C. 12.76
D. 13.76
5. Write the new regression equation.
A. = 0.027x + 10.76
B. = 0.037x + 10.76
C. = 0.047x + 11.76
D. = 0.057x + 11.76
6. How would you characterize the magnitude of the obtained R2 value? Provide a rationale for your answer.
A. R2 value is very low.
B. R2 value is very high.
7. How much variance in months to RN to BSN program completion is explained by knowing the students enrollment age?
A. 1.2%
B. 2.4%
C. 12%
D. 24%
8. What was the correlation between the actual y values and the predicted y values using the new regression equation in the example?
A. 0.11
B. 0.155
C. 0.346
D. 0.49
9. Write your interpretation of the results as you would in an APA-formatted journal.
10. 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 x? Provide a rationale for your answer.
A. Student age (x) did significantly predict months to completion (y). Therefore the equation will accurately predict future values of y.
B. Student age (x) did not significantly predict months to completion (y). Therefore the equation will not accurately predict future values of y.
Exercise 35: Calculating Pearson Chi-Square
The following questions refer to the section called Data for Additional Computational Practice in Exercise 35 of Grove & Cipher 2017.
1. Do the example data in Table 35-2 meet the assumptions for the Pearson 2 test? Provide a rationale for your answer.
A. Yes the data meet the 2 assumptions.
B. No the data do not meet the 2 assumptions.
C. Yes the data meet the 3 assumptions.
D. No the data do not meet the 3 assumptions.
2. Compute the 2 test. What is the 2 value?
A. 11.93
B. 12.93
C. 13.93
D. 14.93
3. Is the 2 significant at = 0.05? Specify how you arrived at your answer.
A. Yes by comparing it with the critical value.
B. No by comparing it with the critical value.
4. If using SPSS what is the exact likelihood of obtaining the 2 value at least as extreme as or as close to the one that was actually observed assuming that the null hypothesis is true?
A. 0.1%
B. 0.5%
C. 1%
D. 5%
5. Using the numbers in the contingency table calculate the percentage of antibiotic users who tested positive for candiduria.
A. 15.5%
B. 25.9%.
C. 47.6%
D. 0%
6. Using the numbers in the contingency table calculate the percentage of non-antibiotic users who tested positive for candiduria.
A. 15.5%
B. 25.9%.
C. 47.6%
D. 0%
7. Using the numbers in the contingency table calculate the percentage of veterans with candiduria who had a history of antibiotic use.
A. 0%
B. 10%.
C. 15%
D. 100%
8. Using the numbers in the contingency table calculate the percentage of veterans with candiduria who had no history of antibiotic use.
A. 0%
B. 10%.
C. 15%
D. 100%