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Exploring Additional Variable Impact on Salary Prediction using Multiple Linear Regression Analysis

This study delves into the influence of adding a second independent variable, Years Ranked (YRSRANK), on salary prediction. The analysis examines the variation in salary explained by the Rank and YRSRANK combination, emphasizing the calculated R-Square values and the importance of conducting a partial F-test to ascertain the significance of the additional contribution. Insights into the relationship between the F-test and t-test in Simple Linear Regression are also discussed for a comprehensive understanding of the analysis techniques utilized.

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Exploring Additional Variable Impact on Salary Prediction using Multiple Linear Regression Analysis

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  1. Multiple Linear Regression (MLR) Testing the additional contribution made by adding an independent variable.

  2. Predicting SALARY using RANK

  3. Predicting SALARY using RANK SST = SSY = variation in SALARY

  4. Predicting SALARY using RANK SST = SSY = variation in SALARY

  5. Predicting SALARY using RANK SST = SSY = variation in SALARY variation in SALARY= 1,009,361,697

  6. Predicting SALARY using RANK SST = SSY = variation in SALARY variation in SALARY= 1,009,361,697 SSR = variation explained by regression

  7. Predicting SALARY using RANK SST = SSY = variation in SALARY variation in SALARY= 1,009,361,697 SSR = variation explained by regression SSR = 647,750,075

  8. Predicting SALARY using RANK SST = SSY = variation in SALARY variation in SALARY= 1,009,361,697 SSR = variation explained by regression SSR = 647,750,075 R Square = SSR/SST ≈ .6417 or 64.17%

  9. Predicting SALARY using RANK SST = SSY = variation in SALARY variation in SALARY= 1,009,361,697 SSR = variation explained by regression SSR = 647,750,075 R Square = SSR/SST ≈ .6417 or 64.17%

  10. Predicting SALARY using RANK Adding YRSRANK as a second independent variable will explain more of the variation in SALARY, but will it be a significant amount? SST = SSY = variation in SALARY variation in SALARY= 1,009,361,697 SSR = variation explained by regression SSR = 647,750,075 R Square = SSR/SST ≈ .6417 or 64.17%

  11. Predicting SALARY using RANK and YRSRANK

  12. Predicting SALARY using RANK and YRSRANK

  13. Predicting SALARY using RANK and YRSRANK SST = SSY = variation in SALARY

  14. Predicting SALARY using RANK and YRSRANK SST = SSY = variation in SALARY

  15. Predicting SALARY using RANK and YRSRANK SST = SSY = variation in SALARY variation in SALARY= 1,009,361,697

  16. Predicting SALARY using RANK and YRSRANK SST = SSY = variation in SALARY variation in SALARY= 1,009,361,697 SSR = variation explained by regression

  17. Predicting SALARY using RANK and YRSRANK SST = SSY = variation in SALARY variation in SALARY= 1,009,361,697 SSR = variation explained by regression SSR(RANK and YRSRANK) = 683,715,472.1

  18. Predicting SALARY using RANK and YRSRANK SST = SSY = variation in SALARY variation in SALARY= 1,009,361,697 SSR = variation explained by regression SSR(RANK and YRSRANK) = 683,715,472.1 R Square = SSR/SST ≈ .6774 or 67.74%

  19. Predicting SALARY using RANK and YRSRANK SST = SSY = variation in SALARY variation in SALARY= 1,009,361,697 SSR = variation explained by regression SSR(RANK and YRSRANK) = 683,715,472.1 R Square = SSR/SST ≈ .6774 or 67.74%

  20. Predicting SALARY using RANK Adding YRSRANK as a second independent variable will explain more of the variation in SALARY, but will it be a significant amount? SST = SSY = variation in SALARY variation in SALARY= 1,009,361,697 SSR = variation explained by regression SSR = 647,750,075 R Square = SSR/SST ≈ .6417 or 64.17%

  21. Predicting SALARY using RANK and YRSRANK Adding YRSRANK as a second independent variable will explain more of the variation in SALARY, but will it be a significant amount? SST = SSY = variation in SALARY variation in SALARY= 1,009,361,697 SSR = variation explained by regression SSR(RANK and YRSRANK) = 683,715,472.1 R Square = SSR/SST ≈ .6774 or 67.74% SSR = variation explained by regression SSR = 647,750,075 R Square = SSR/SST ≈ .6417 or 64.17%

  22. Predicting SALARY using RANK and YRSRANK SST = SSY = variation in SALARY variation in SALARY= 1,009,361,697 We may determine if this additional contribution is significant by performing a partial F-test. Additionalcontribution made by adding YRSRANK = SSR(YRSRANK | RANK) = 683,715,472.1 - 647,750,075 = 35,965,397.1 SSR = variation explained by regression SSR = 647,750,075 R Square = SSR/SST ≈ .6417 or 64.17%

  23. Partial F-test (α = .05) Additionalcontribution made by adding YRSRANK = SSR(YRSRANK | RANK) = 683,715,472.1 - 647,750,075 = 35,965,397.1, the numerator.

  24. Predicting SALARY using RANK and YRSRANK

  25. Predicting SALARY using RANK and YRSRANK

  26. Partial F-test (α = .05) In Simple Linear Regression, what was the relationship between the F-test and the t-test? The square root of the F ≈ 2.419398, the t value for YRSRANK.

  27. Predicting SALARY using RANK and YRSRANK The partial F-test and the t-test are equivalent, provided that one is examining the additional contribution of a single independent variable.

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