17 multiple-linear regression

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Running & Reporting a Multiple Linear Regression in SPSS - Example The Study: Are the number of cyberbullying incidents a student experiences and there absenteeism significant predictors of self-esteem scores? Decision Path: Inferential / Relationship / Predictive / Three Variables = Multiple- Linear Regression The Hypothesis: GPA and years in academic clubs are significant predictors of ACT scores. The Null-hypothesis: GPA and years in academic clubs are NOT significant predictors of ACT scores. Question: Do we have enough evidence to reject the null hypothesis? The Decision rule: If the probability that we are wrong is .05 or 5 out of 100 times we will reject the null-hypothesis or in other words, accept the hypothesis.

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1. Running & Reporting a Multiple Linear Regression in SPSS - Example The Study: Are the number of cyberbullying incidents a student experiences and there absenteeism significant predictors of self-esteem scores? Decision Path: Inferential / Relationship / Predictive / Three Variables= Multiple-Linear Regression The Hypothesis: GPA and years in academic clubs are significant predictors of ACT scores. The Null-hypothesis: GPA and years in academic clubs are NOT significant predictors of ACT scores. Question: Do we have enough evidence to reject the null hypothesis? The Decisionrule: If the probability that we are wrong is .05 or 5 out of 100 times we will reject the null-hypothesis or in other words, accept the hypothesis. 2. Results ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 25.272 2 12.636 58.669 .000b Residual 38.122 177 .215 Total 63.394 179 a. DependentVariable:Absenteeism b. Predictors:(Constant),Self_Esteem_Index, Cyberbullying_incidents Report: Based on the results of the study, the number of cyberbullying incidents experienced by students and their self-esteem index scores are statistically significant predictors of absenteeism F(2) = 58.669 p = .000. 3. Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig.B Std. Error Beta 1 (Constant) -.712 .267 -2.673 .008 Cyberbullying_incidents .167 .030 .398 5.550 .000 Self_Esteem_Index .024 .006 .311 4.341 .000 a. Dependent Variable:Absenteeism The undstandardized coefficients in the regression equation were each statistically significant: constant p = .008, regression coefficient for cyberbullying p = .000, and regression coefficient for self-esteem index scores p = .000. Therefore, students predicted absenteeism are equal to -.712 + .167 (incidents of cyberbullying) + .024 (self-esteem). Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .631a .399 .392 .464 a. Predictors:(Constant), Self_Esteem_Index,Cyberbullying_incidents 39.2% of the variance in absenteeism are explained by the variance in cyberbullying incidents and self-esteem scores. 4. Therefore, here are the final results: Based on the results of the study, the number of cyberbullying incidents experienced by students and their self-esteem index scores are statistically significant predictors of absenteeism F(2) = 58.669 p = .000. The undstandardized coefficients in the regression equation were each statistically significant: constant p = .008, regression coefficient for cyberbullying p = .000, and regression coefficient for self-esteem index scores p = .000. Therefore, students predicted absenteeism are equal to -.712 + .167 (incidents of cyberbullying) + .024 (self-esteem). 39.2% of the variance in absenteeism are explained by the variance in cyberbullying incidents and self-esteem scores.