and are checked. Click to return to the Estimates, Model fit Collinearity Diagnostics Continue Linear Regression dialogue box. To create residual plots to check the homogeneity of variance and normality assumptions, click Plots just below Statistics to open the Linear Regression: Plots dialogue box. Click and move to the Y box. Click and move to the X box. Check “*ZRESID” “*ZPRED” “Histogram”. Click Continue to return to the Linear Regression dialogue box. To check for outliers and influential cases, click Save just below Plots to open the Linear Regression: Save dialogue box. Under Residuals, check Under Influence “Standardized”. Statistics, check DfBetas”. Click Continue to return to the Linear Regression “Standardized dialogue box. Click OK at the bottom of the Linear Regression dialogue box to run the multiple linear regression analysis. We now examine the output, including findings with regard to multicollinearity, whether the should be trimmed (i.e., removing insignificant predictors), violation of homogeneity of variance and normality assumptions, and outliers and influential cases. The R2 is .225. This means that the independent variables explain 22.5% of the variation in the dependent variable.
Estimates, Model fit and Collinearity Diagnostics are checked. Click Continue to return to the Linear Regression dialogue box. To create residual plots to check the homogeneity of variance and normality assumptions, click Plots just below Statistics to open the Linear Regression: Plots dialogue box. Click and move “*ZRESID” to the Y box. Click and move “*ZPRED” to the X box. Check “Histogram”. Click Continue to return to the Linear Regression dialogue box. To check for outliers and influential cases, click Save just below Plots to open the Linear Regression: Save dialogue box. Under Residuals, check “Standardized”. Under Influence Statistics, check “Standardized DfBetas”. Click Continue to return to the Linear Regression dialogue box. Click OK at the bottom of the Linear Regression dialogue box to run the multiple linear regression analysis. We now examine the output, including findings with regard to multicollinearity, whether the should be trimmed (i.e., removing insignificant predictors), violation of homogeneity of variance and normality assumptions, and outliers and influential cases. The R2 is .225. This means that the independent variables explain 22.5% of the variation in the dependent variable.
Multiple_Linear_Regression_Instructions_Rev.docx