HLT 362 Exercise 29

EXERCISE 29 

The computation of Shapiro Wilk test of normality for the variable age indicates the statistic is =0.949 and the p value is = 0.357. Because the p value is more than 0.05, the result indicates that frequency distribution did not deviate from the normality.

  1. If you have access to SPSS, compute the Shapiro-Wilk test of normality for the variable age (as demonstrated in Exercise 26Exercise 26). If you do not have access to SPSS, plot the frequency distributions by hand. What do the results indicate?

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

  1. State the null hypothesis where age at enrollment is used to predict the time for completion of an RN to BSN program.
  2. Student age at enrollment does not predict the number of months until completion of an RN to BSN program.

The B value is .047

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

The a value is 11.763

5. Write the new regression equation.

The new regression equation is Y=bx+a, so for my data set it would be Y=0.047x+11.763

6. How would you characterize the magnitude of the obtained R2 value? Provide a rationale for your answer.

The magnitude of the obtained R square value is = 0.012. The R square represents the percentage of variance. Because the magnitude of R square value is 0.012, it would be considered a small effect size.

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

The variance in months to RN to BSN program completion by knowing the student’s age is: R^2×100=0.012×100=1.2%, indicates that 1.2% of the variance in months to RN to BSN program completion is explained by knowing the student’s enrollment age.

8. What was the correlation between the actual y values and the predicted y values using the new regression equation in the example?

The correlation between the actual y values and the predicted y values using the regression equation in the example is 0.108, which is also known as multiple R value.

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

Simple linear regression was performed with student’s age at RN to BSN program enrollment as the independent variable and the number of months it took for students to complete the program as the dependent variable. The age of the students did not significantly predict months to completion among students in an RN to BSN program, using the regression equation y=0.047x + 11.763, with p value =0.651and the R^2=1.2%. Th p value is greater than 0.05 and the R^2=1.2% indicates the variance of the months, this indicates that the students age at enrollment does not significantly predict the program completion time.

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.

Student age (x) did not significantly predict months to completion (y). Therefore, the equation will not accurately predict future values of y.

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