Multiple Predictors of Salary
Multiple Predictors of Salary Jacob Otto MSC 1201 8am Section Professor Zangenah Monday, August 11, 2003 Introduction In this report, our goal was to determine the significant predictor variables in determining an employee’s annual salary. ... It gives information on each employee’s annual salary, gender, age, experience level, and training level. This report examines each variable’s effect on annual salary, and the significance of its effect. Analyses and Methods As is exhibited in appendix 1, the multiple regression analysis was done in MiniTab on all k predictors of salary, and it shows this regression equation for the data set: Salary = 6109 + 8425 (gender) + 326 (age) + 1168 (experience) + 7769 (training level) The first question that we ask is that of co-linearity. As shown in appendix 1, the VIF values for all relevant predictors (C3-C6) are all less than five. ... This is done to eliminate insignificant predictors one at a time, to judge their significance to the model. ... Through the above tests, we have confirmed that all the variables included in the stated model equation are significant to determining salary. ... Summary and Conclusion In conclusion, the model that was produced was a significant, valid, and reliable way in which to predict a person’s annual salary.