Question: Model Summaryb Change Statistics Model R R Square Adjusted R Square Std. Error of the Estimate R Square Change F Change df1 1 .426

Model Summaryb Change Statistics Model R R Square Adjusted R Square Std. Error of the Estimate R Square Change F Change df1 1 .426 .181 .154 5.721 .181 6.650 3 df2 90 Sig. F Change .000 a. Predictors: (Constant), Competence, Relatedness, Autonomy b. Dependent Variable: Concealing ANOVA* Model 1 Sum of Squares df Mean Square F Regression Residual Total 653.056 3 2946.178 90 217.685 32.735 6.650 Sig. .000 3599.234 93 a. Dependent Variable: Concealing b. Predictors: (Constant), Competence, Relatedness, Autonomy Coefficients Unstandardized Coefficients Standardized Coefficients 95.0% Confidence Interval for B Model 1 B Std. Error Betal Sig. Lower Bound Upper Bound Collinearity Statistics Tolerance VIF (Constant) 43.697 3.602 12.133 .000 36.542 Autonomy -2.385 .999 Relatedness Competence -.980 .451 1.120 -.129 -.367 -2.387 -.875 .019 -4.369 50.853 -.400 .384 2.605 .384 -3.205 1.246 .415 2.408 .903 .068 .500 .618 -1.342 2.245 .493 2.030 a. Dependent Variable: Concealing If this is your multiple regression output, what do you know? this set of predictors allows us to predict the dependent variable significantly better than we could without them. you've probably made a Type I error this set of predictors does not significantly improve our prediction of the dependent variable, compared to chance levels. autonomy is not a significant predictor in this model.

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