SlideServe
Télécharger Obtenir Pro
Menu
Accueil Pro
Préférences
ApparenceLight Aide et commentaires
© SlideServe · v2026

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

tansy avatar
tansy
27 diapositives · 441 Views · Jul 27, 2014

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.

#exploring additional variable impact on salary prediction using multiple linear regression analysis #exploring additional variable impact on salary prediction using multiple linear regression analysis ppt #powerpoint #presentation
Exploring Additional Variable Impact on Salary Prediction using Multiple Linear Regression Analysis - slide 1
01 / 27Exploring Additional Variable Impact on Salary Prediction using Multiple Linear Regression Analysis
1 / 17

Télécharger la présentation

Exploring Additional Variable Impact on Salary Prediction using Multiple Linear Regression Analysis
Une image / un lien ci-dessous est fourni (tel quel) pour télécharger la présentation

Politique de téléchargement : le contenu du site vous est fourni TEL QUEL pour votre information et votre usage personnel ; il ne peut être vendu, concédé sous licence ni partagé sur d’autres sites sans l’accord de son auteur.Le contenu vous est fourni TEL QUEL, pour votre information et votre usage personnel uniquement. Téléchargez la présentation en cliquant sur ce lien. Lors du téléchargement, si vous ne parvenez pas à télécharger une présentation, l’éditeur a peut-être supprimé le fichier de son serveur.Pendant le téléchargement, si vous ne parvenez pas à obtenir une présentation, le fichier a peut-être été supprimé par l’éditeur.

F I N

Plus de contenu associé

Transcription de la présentation

  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.