Chrispinus Asembo Opuli

Regression assignment To test the significance of B1, B2, and B3 towards employee retention where B1 is the years of employee education, B2 is the age of the employees and B3 is the college GPA of the employee. H0: B1 = B2 = B3 = 0 H1: At least one of the Bs is greater than zero SUMMARY OUTPUT Regression Statistics Multiple R 0.38756 R Square- Adjusted R Square- Standard Error- Observations 40 ANOVA   df SS MS F Significance F Regression- Residual- Total-         Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept - - - - YrsEducation - - - - Age- College GPA- - - The regression model is; Employee retention = - 2.73711 – 0.06705* (YrsEducation) -* (Age) -* (College GPA) From the analysis, Age of the employee (t =-, p =-) =- was found to have significant contribution towards employee retention at 0.05% level of significance while Years of employee education (t = - 0.1888, p =-) = - 0.06705 and college grade point (t =-, p =-) =- were found to have insignificant contribution towards Employee Retention at 0.05% Level of significance. Therefore, the predictor for employee retention is the age of the employee since it was found to be significant while college grade point and years of employee’s education were found to be insignificant from the fitted multiple linear regression model. We fit a model for the significant variable only SUMMARY OUTPUT Regression Statistics Multiple R- R Square- Adjusted R Square- Standard Error- Observations 40 ANOVA   df SS MS F Significance F Regression- Residual- Total-         Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept - - - - Age- The model is; Employee retention = - - *(Age) From the model, Age of the employees (t = -, p =-) =- indicates that age is significant in predicting employee retention at 0.05% level of significance, this is because the p – value obtained- is less than the level of significance.