Multiple regression

1.You are interested in examining whether the variables shown here in brackets

[ years of age (age), hours worked per week (hrs1), years of education (educ), years of education for mother (maeduc), and years of education for father (paeduc) ]

are predictors of individual income (rimcmdol). Complete the following steps to conduct this analysis:

a.Using profile-a.sav, conduct a preliminary regression to calculate Mahalanobis distance. Identify the critical value for chi-square. Conduct Explore to identify outliers. Which cases should be removed from further analysis?

For all subsequent analyses, use profile-b.sav. Make sure that only cases where MAH_1 22.458 are selected.

b. scatterplot matrix. Can you assume linearity and normality?

c.Conduct a preliminary regression to residual plot. Can you assume normality and homoscedasticity?

d.Conduct multiple regression using the Enter method. Evaluate the tolerance statistics. Is multicollinearity a problem?

e.Does the model significantly predict rincmdol? Explain.

f.Which variables significantly predict rincmdol? Which variable is the best predictor of the DV?

g.What percentage of variance in rincmdol is explained by the model?

h.Write the regression equation for the standardized variables.