Multicollinearity

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 R2 – values?

j. What is the meaning of the R2 – value in 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 for your regression model, what

would be the salary of someone who is 30.5 years old?

Length: Complete responses to all parts of both questions. Please

include the question prompts along with your responses in your

assignment submission. In addition to a WORD (.doc) file with the

answers to the assignment questions, also include the output (.spv) file.

(NOTE: SPSS automatically generates the .spv file as you work in

SPSS. When you close your SPSS main window, SPSS will ask you if

you want to save the output file. Click ‘yes’, then save to your computer

and upload with your assignment.)

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