Before you start with the actual regression analysis, generate a scatterplot matrix and correlations for all variables and describe the observed patterns.
Find the best regression model that explains most of the variability associated with wages, but make sure that the model only includes variables that are significant at the 0.05 level. Report the equation for your regression model as well as the coefficients, p-values, and partial regression plots.
Test for multicollinearity and comment on the results.
Test for outliers by analyzing standardized residuals, leverage, Cook’s distance, and Dfbetas. Generate the appropriate graphs and comment on the results.
Analyze if the assumptions of normality and homoscedasticity are met for the residuals and comment on it.
Discuss and explain the results of the regression model and all diagnostic analyses.
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