Week 4 Assignment Worksheet:

Examine Relationship and Predictions Among Multiple Variables

Scenario

A fashion designer 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).

I.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?

II.Please 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.Please 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 R² values?

j.What is the meaning of the R² value in a regression analysis?

k.Was the model significant? How could you determine significance in a regression model?

l.Which predictor(s) were significant in the model?

m.Now conduct another regression analysis, changing the variables that were highly correlated with one another.

n.Were the results the same? Which one is a better predictor? (Hint: R² of the model).

o.Develop the regression equation using the values from the coefficients table in SPSS.

p.Based on your equation, what would be the salary of a model who is 30.5 years old?