Exercise 30: Calculating Multiple Linear Regression
1. Write the newly computed regression equation, predicting months to RN to BSN program
completion.
a. Y hat= -4.235x1 + -1.630x2 +17.309
2. Why have the values in the equation changed slightly from the first analysis?
a. Actual values create variance in the equation
3. Using SPSS, create a scatterplot of predicted values and residuals that assists us in identifying
heteroscedasticity. Do the data meet the homoscedasticity assumptions? Provide a rationale
for your answer.
a. Yes, as homoscedasticity reflects equal variance of both variables. For every x value, the
distribution of y values is equally variable.
4. Using the new regression equation, compute the predicated months to RN to BSN program
completion if a student’s number of degrees in 1 and is enrolled in the online program. Show
your calculations.
a. Y hat= -4.235x1 + -1.630x2 +17.309= -4.235(1) + -1.630(1) +17.309= 11.444
5. Using the new regression equation, compute the predicted months to RN to BSN program
completion if a student’s number of degrees is 2 and is enrolled in the in-seat program. Show
your calculations.
a. Y hat= -4.235x1 + -1.630x2 +17.309= -4.235(2) + -1.630(2) +17.309= 29.039
1. Write the newly computed regression equation, predicting months to RN to BSN program
completion.
a. Y hat= -4.235x1 + -1.630x2 +17.309
2. Why have the values in the equation changed slightly from the first analysis?
a. Actual values create variance in the equation
3. Using SPSS, create a scatterplot of predicted values and residuals that assists us in identifying
heteroscedasticity. Do the data meet the homoscedasticity assumptions? Provide a rationale
for your answer.
a. Yes, as homoscedasticity reflects equal variance of both variables. For every x value, the
distribution of y values is equally variable.
4. Using the new regression equation, compute the predicated months to RN to BSN program
completion if a student’s number of degrees in 1 and is enrolled in the online program. Show
your calculations.
a. Y hat= -4.235x1 + -1.630x2 +17.309= -4.235(1) + -1.630(1) +17.309= 11.444
5. Using the new regression equation, compute the predicted months to RN to BSN program
completion if a student’s number of degrees is 2 and is enrolled in the in-seat program. Show
your calculations.
a. Y hat= -4.235x1 + -1.630x2 +17.309= -4.235(2) + -1.630(2) +17.309= 29.039
