Multiple Linear Regression

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Mathematics

Description

Refer to the Excel file Salary (attached to the assignment) to address all the questions below:

  1. Develop a model to predict the salary based on (Gender, Age, Performance Rating, and Degree).
  2. State the multiple regression equation.
  3. Interpret the meaning of the slopes in this equation.
  4. Perform a residual analysis for the results and determine if the regression assumptions are valid.
  5. Is there a significant relation between the salary and the independent variables at the 0.05 level.
  6. Determine the p-value in (5) and interpret its meaning.
  7. Interpret the meaning of the coefficient of multiple determination in this problem.
  8. Determine the adjusted R-Squared
  9. At the 0.05 level of significance, determine whether each independent variable makes a significant contribution to the regression model. Indicate the most appropriate regression model for this data set.
  10. Determine the p-values in 9 (above) and interpret their meaning.

Unformatted Attachment Preview

ID Salary Compa Midpoint Age 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 62,9 27,2 34,9 64,6 49,2 77,7 41,2 22 73,4 23,9 21,8 60,8 41,8 23,3 24,6 47,4 66,6 34,8 24,5 35,9 77,1 58,9 23,8 55,1 24,6 24,1 45,3 75,2 78,7 48,3 24,2 27,6 61,2 27 23,8 24,4 23,5 61,3 34,4 24,7 1,104 57 31 31 57 48 67 40 23 67 23 23 57 40 23 23 40 57 31 23 31 67 48 23 48 23 23 40 67 67 48 23 31 57 31 23 23 23 57 31 23 34 52 30 42 36 36 32 32 49 30 41 52 30 32 32 44 27 31 32 44 43 48 36 30 41 22 35 44 52 45 29 25 35 26 23 27 22 45 27 24 0,879 1,127 1,134 1,026 1,160 1,029 0,956 1,095 1,039 0,947 1,066 1,044 1,012 1,070 1,186 1,169 1,124 1,065 1,159 1,150 1,228 1,036 1,147 1,069 1,047 1,133 1,122 1,175 1,006 1,050 0,889 1,074 0,872 1,036 1,060 1,023 1,075 1,109 1,076 Performance Service Gende Raise Rating r 85 80 75 100 90 70 100 90 100 80 100 95 100 90 80 90 55 80 85 70 95 65 65 75 70 95 80 95 95 90 60 95 90 80 90 75 95 95 90 90 8 7 5 16 16 12 8 9 10 7 19 22 2 12 8 4 3 11 1 16 13 6 6 9 4 2 7 9 5 18 4 4 9 2 4 3 2 11 6 2 0 0 1 0 0 0 1 1 0 1 1 0 1 1 1 0 1 1 0 1 0 1 1 1 0 1 0 1 0 0 1 0 0 0 1 1 1 0 1 0 5,7 3,9 3,6 5,5 5,7 4,5 5,7 5,8 4 4,7 4,8 4,5 4,7 6 4,9 5,7 3 5,6 4,6 4,8 6,3 3,8 3,3 3,8 4 6,2 3,9 4,4 5,4 4,3 3,9 5,6 5,5 4,9 5,3 4,3 6,2 4,5 5,5 6,3 41 42 43 44 45 46 47 48 49 50 45,8 22,4 75,6 61,3 59,7 55,1 63,8 62,8 64 66,3 1,145 0,975 1,129 1,075 1,244 0,967 1,119 1,101 1,122 1,163 40 23 67 57 48 57 57 57 57 57 25 32 42 45 36 39 37 34 41 38 80 100 95 90 95 75 95 90 95 80 5 8 20 16 8 20 5 11 21 12 0 1 1 0 1 0 0 1 0 0 4,3 5,7 5,5 5,2 5,2 3,9 5,5 5,3 6,6 4,6 Degree Gender 1 0 0 1 1 1 1 1 1 1 1 1 0 0 1 1 0 1 0 1 0 1 1 0 0 0 0 1 0 0 0 1 0 1 1 0 0 0 0 0 0 M M F M M M F F M F F M F F F M F F M F M F F F M F M F M M F M M M F F F M F M Gr E B B E D F C A F A A E C A A C E B A B F D A D A A C F F D A B E B A A A E B A The column labels in the table mean: ID – Employee sample number Salary – Salary in thousands Age – Age in years Performance Rating - Appraisal rating (em Service – Years of service (rounded) Gender – 0 = male, 1 = female Midpoint – salary grade midpoint Raise – percent of last raise Grade – job/pay grade Degree (0= BS\BA 1 = MS) Gender1 (Male or Female) Compa - salary divided by midpoint 0 1 0 1 1 1 1 1 0 0 M F F M F M M F M M C A F E D E E E E E ance Rating - Appraisal rating (employee evaluation score)
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Awesome! Perfect study aid.

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