Scenario: A fashion design professor was interested in developing a
regression model to predict the salary of the models. The data file name
is Supermodel.sav. There were 231 models included in the data
collection. The questions asked to each one of the models were current
income per day (Salary), age (Age), their years of experience modeling
(Years), an attractiveness rating (Beauty).
1. Assumptions of the multiple linear regression analysis.
a. What are the assumptions of the multiple linear regression
analysis? In one or two sentences briefly describe each one
of them.
b. What is multicollinearity?
c. How could the researcher examine for multicollinearity?
2. Open the Supermodel.sav file and conduct a multiple linear
regression.
a. Which is your dependent or predicted variable?
b. Which are your independent or predictor variables?
c. Conduct a correlation analysis including all of the predictors
(independent variables).
d. What are the correlations between each pair of correlations?
e. Can you determine if all of the variables should be included
in the regression analysis? (Hint: Examine for
multicollinearity.)
f. Which variables would you include in the regression
analysis?
g. Please conduct a multiple regression analysis.
h. What is the R – value of the model?
i. What are the R
2
– values?
