What is the residual for the prediction in the above problem?.

In SPSS, create a correlation table and find the correlation of BMIChange with each of the other 4 variables. Organize the variables (name, r) in the chart below from the strongest to the weakest correlation with BMIChange. Put a star (*) above all the variables which have significant correlations with BMIChange. You do not need to attach the correlation table as part of your outputs.

Variable

Correlation (r)

strongest weakest

) Perform a regression of BMIChange using the 4 explanatory variables.

a) Complete the table below regarding the regression line:

R2 Model Standard error ANOVA table F- test statistic P-value for F-test

b) Complete the table below regarding the coefficients information:

Explanatory variables in model Is the variable significant? Answer yes or no. List the correspondent p-value.

PhysicalActivity

DailyCalories

AvgSleepHours

Age

3. (3 points) Now drop the least significant variable and re-run the regression.

a) Complete the tables below:

R2 Model Standard error ANOVA table F- test statistic P-value for F-test

Explanatory variables in model Is the variable significant? Answer yes or no. List the correspondent p-value.

b) Was dropping that variable a good change? Explain why or why not.

4. Based on your answer to question 3b, state the regression equation for the “better” model. Do not forget the proper notation.

5. Using the regression equation from question 4, predict the BMIChange for the 22 year old male (Subject_ID=59) with PhysicalActivity of 44 minutes, DailyCalories of 3100 calories and AveSleepHours of 5.9 hours. Show your work.

6. What is the residual for the prediction in the above problem? Show your work.

7. Even if the model used in question 4 was a “better” model, does the model still have some variables that are not significant? If so, which ones?

What is the residual for the prediction in the above problem?