# statistics

**Statistics for Nursing Research: A Workbook for Evidence-Based Practice, 2nd Edition**

**Exercise 29: Calculating Simple Linear Regression**

**Questions to Be Graded**

### Save your time - order a paper!

Get your paper written from scratch within the tight deadline. Our service is a reliable solution to all your troubles. Place an order on any task and we will take care of it. You won’t have to worry about the quality and deadlines

Order Paper Now**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.

Answer: Choose an item.

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.

Answer: Choose an item.

3. What is b as computed by hand (or using SPSS)?

A. 0.027

B. 0.037

C. 0.047

D. 0.057

Answer: Choose an item.

4. What is a as computed by hand (or using SPSS)?

A. 10.76

B. 11.76

C. 12.76

D. 13.76

Answer: Choose an item.

5. Write the new regression equation.

A. ŷ = 0.027x + 10.76

B. ŷ = 0.037x + 10.76

C. ŷ = 0.047x + 11.76

D. ŷ = 0.057x + 11.76

Answer: Choose an item.

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.

Answer: Choose an item.

7. How much variance in months to RN to BSN program completion is explained by knowing the student’s enrollment age?

A. 1.2%

B. 2.4%

C. 12%

D. 24%

Answer: Choose an item.

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

Answer: Choose an item.

9. Write your interpretation of the results as you would in an APA-formatted journal.

Enter your answer here and select the “Completed” choice in the dropdown menu. If more space is needed, please hit the Enter key from your computer.

Answer: Choose an item.

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.

Answer: Choose an item.

**Exercise 35: Calculating Pearson Chi-Square**

**Questions to Be Graded**

**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 χ2 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.

Answer: Choose an item.

2. Compute the χ2 test. What is the χ2 value?

A. 11.93

B. 12.93

C. 13.93

D. 14.93

Answer: Choose an item.

3. Is the χ2 significant at α = 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.

Answer: Choose an item.

4. If using SPSS, what is the exact likelihood of obtaining the χ2 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%

Answer: Choose an item.

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%

Answer: Choose an item.

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%

Answer: Choose an item.

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%

Answer: Choose an item.

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%

Answer: Choose an item.

9. Write your interpretation of the results as you would in an APA-formatted journal.

Enter your answer here and select the “Completed” choice in the dropdown menu. If more space is needed, please hit the Enter key from your computer.

Answer: Choose an item.

10. Was the sample size adequate to detect differences between the two groups in this example? Provide a rationale for your answer.

A. The sample size was adequate to detect differences between the two groups because a significant difference was found, p = 0.001.

B. The sample size was not adequate to detect differences between the two groups because no significant difference was found, p >0.05.

Answer: Choose an item.

Grading

Please do not add or delete a row or column for the following grading table. If you have a problem with the drop-down lists, then you can enter your answers in the second column. If you use the drop-down lists, the instructor will update your answers in the table. Thanks