In regard to the variables used ERIratio was not enteredas apredictor in the first model summary. The researcher was concerned that the inclusion of this variable may have violated the assumption of multicollinearity.

The analysis was run again to determine if these variables below including ERIratio violate the assumption of multicollinearity when predicting sIgA.

a) State whether these variables below violated the assumption of multicollinearity.

b) State the difference of incorporating ERIratio in the second model summary.

c)Explain why this regression result, with four predictors, is better or worse, in terms of the F test and effect size.

Model Summary Change Statistics R Adjusted R Std. Error of the R Square F Sig. F Model R Square Square Estimate Change Change df1 df2 Change 470a .221 .183 6.01015 .221 5.851 3 62 001 a. Predictors: (Constant), REWARD, EFFORT, OC Model Summary Change Statistics R Adjusted R Std. Error of the R Square Sig. F Model R Square Square Estimate Change Change df1 df2 Change 470a 221 17 6.05790 221 4.326 4 61 004 a. Predictors: (Constant), ERIratio. OC, REWARD, EFFORT Coefficientsa Unstandardized Standardized Collinearity Coefficients Coefficients Correlations Statistics Zero- Model B Std. Error Beta Sig order Partial Part Tolerance VIF 1 (Constant) 21.983 4.365 5.036 .000 OC -.364 213 .205 093 -.222 -.213 886 1.129 1.706 193 EFFORT -.411 .206 .267 .050 -.355 -.248 - 715 1.399 .99 22 REWARD 231 102 272 2.271 027 .280 279 .257 393 1.120 ERIratio -.165 1.011 .022 -.163 .871 -.208 -.021 695 1.439 .018 a. Dependent Variable: slgA