A fashion design

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?