Name: Amarbir Kaur Gill.
Class: HLT-362V Applied Statistics for Health Care
Professionals.
Date: June 16, 2017
EXERCISE 29: Questions to be graded
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?
Tests of Normality
Kolmogorov-Smirnova Shapiro-Wilk
Statistic df Sig. Statistic df Sig.
AGE .140 20 .200 *
.949 20 .357
*. This is a lower bound of the true significance.
a. Lilliefors Significance Correction
The frequency distributor was not significant as the Shapiro-Wilk p value was 0.357 indicating
that the frequency distribution did not significantly deviate 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.
The null hypothesis is: “Student 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)?
b=.047
Coefficientsa
Standardized
Unstandardized Coefficients Coefficients
Model B Std. Error Beta t Sig.
1 (Constant) 11.763 3.536 3.326 .004
AGE .047 .102 .108 .459 .651
a. Dependent Variable: Months_to_Complete
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, 4. What is a as computed by hand (or using SPSS)?
a = 11.763
Coefficientsa
Standardized
Unstandardized Coefficients Coefficients
Model B Std. Error Beta t Sig.
1 (Constant) 11.763 3.536 3.326 .004
AGE .047 .102 .108 .459 .651
a. Dependent Variable: Months_to_Complete
5. Write the new regression equation.
y = bx + a
y = 0.047 (age) + 11.763
6. How would you characterize the magnitude of the obtained R 2 value? Provide a
rationale for your answer.
R2 value is 0.012, indicating that 1.2% of the variance in months to program
completion can be explained by knowing the student's age at enrollment. Magnitude is not lar
ge. The magnitude of the obtained R2 value would be a small one per the description
offered in the lesson.
Model Summary
Adjusted R Std. Error of the
Model R R Square Square Estimate
1 .108 a
.012 -.043 3.368
a. Predictors: (Constant), AGE
7. How much variance in months to RN to BSN program completion is explained by
knowing the student ’s enrollment age?
From answer to question above 1.2% of the variance in months to program completion can
be explained by knowing the student's age at enrollment. Knowing the student’s enrollment
age, explains the variance in months of RN to BSN program completion, which is 1.2%.
8. What was the correlation between the actual y values and the predicted y values using
the new regression equation in the example?
This study source was downloaded by 100000850699744 from CourseHero.com on 08-20-2022 04:57:38 GMT -05:00
https://www.coursehero.com/file/25563443/Assignment-Exercise-29-and-35docx/
Class: HLT-362V Applied Statistics for Health Care
Professionals.
Date: June 16, 2017
EXERCISE 29: Questions to be graded
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?
Tests of Normality
Kolmogorov-Smirnova Shapiro-Wilk
Statistic df Sig. Statistic df Sig.
AGE .140 20 .200 *
.949 20 .357
*. This is a lower bound of the true significance.
a. Lilliefors Significance Correction
The frequency distributor was not significant as the Shapiro-Wilk p value was 0.357 indicating
that the frequency distribution did not significantly deviate 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.
The null hypothesis is: “Student 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)?
b=.047
Coefficientsa
Standardized
Unstandardized Coefficients Coefficients
Model B Std. Error Beta t Sig.
1 (Constant) 11.763 3.536 3.326 .004
AGE .047 .102 .108 .459 .651
a. Dependent Variable: Months_to_Complete
This study source was downloaded by 100000850699744 from CourseHero.com on 08-20-2022 04:57:38 GMT -05:00
https://www.coursehero.com/file/25563443/Assignment-Exercise-29-and-35docx/
, 4. What is a as computed by hand (or using SPSS)?
a = 11.763
Coefficientsa
Standardized
Unstandardized Coefficients Coefficients
Model B Std. Error Beta t Sig.
1 (Constant) 11.763 3.536 3.326 .004
AGE .047 .102 .108 .459 .651
a. Dependent Variable: Months_to_Complete
5. Write the new regression equation.
y = bx + a
y = 0.047 (age) + 11.763
6. How would you characterize the magnitude of the obtained R 2 value? Provide a
rationale for your answer.
R2 value is 0.012, indicating that 1.2% of the variance in months to program
completion can be explained by knowing the student's age at enrollment. Magnitude is not lar
ge. The magnitude of the obtained R2 value would be a small one per the description
offered in the lesson.
Model Summary
Adjusted R Std. Error of the
Model R R Square Square Estimate
1 .108 a
.012 -.043 3.368
a. Predictors: (Constant), AGE
7. How much variance in months to RN to BSN program completion is explained by
knowing the student ’s enrollment age?
From answer to question above 1.2% of the variance in months to program completion can
be explained by knowing the student's age at enrollment. Knowing the student’s enrollment
age, explains the variance in months of RN to BSN program completion, which is 1.2%.
8. What was the correlation between the actual y values and the predicted y values using
the new regression equation in the example?
This study source was downloaded by 100000850699744 from CourseHero.com on 08-20-2022 04:57:38 GMT -05:00
https://www.coursehero.com/file/25563443/Assignment-Exercise-29-and-35docx/
