Question

A survey conducted by a research team was to investigate how the education level, tenure in current employment, and age are related to annual income. A sample of 20 employees is selected and the data are given below. Education (No. of years) Length of tenure in current employment (No. of years) Age (No. of years) Annual income ($) 17 8 40 124,000 12 12 41 30,000 20 9 44 193,000 14 4 42 88,000 12 1 19 27,000 14 9 28 43,000 12 8 43 96,000 18 10 37 110,000 16 12 36 88,000 11 7 39 36,000 16 14 36 81,000 12 4 22 38,000 16 17 45 140,000 13 7 42 11,000 11 6 18 21,000 20 4 40 151,000 19 7 35 124,000 16 12 38 48,000 12 2 19 26,000 10 6 44 124,000 a. Determine the estimated multiple linear regression equation that can be used to predict the annual income given number of years school completed (Education), length of tenure in current employment, and age.

Answer 1

Answer:

y = - 143481.1924 + 10011.9212x1 - 2193.8838x2 + 2689.2405x3

Step-by-step explanation:

Given the data below:

Education (No. of years) Length of tenure in current employment (No. of years) Age (No. of years) Annual income ($) 17 8 40 124,000 12 12 41 30,000 20 9 44 193,000 14 4 42 88,000 12 1 19 27,000 14 9 28 43,000 12 8 43 96,000 18 10 37 110,000 16 12 36 88,000 11 7 39 36,000 16 14 36 81,000 12 4 22 38,000 16 17 45 140,000 13 7 42 11,000 11 6 18 21,000 20 4 40 151,000 19 7 35 124,000 16 12 38 48,000 12 2 19 26,000 10 6 44 124,000

The estimated multiple linear regression equation that can be used to predict the annual income using number of years school completed (Education), length of tenure in current employment, and age using an online multiple regression calculator is :

y = - 143481.1924 + 10011.9212x1 - 2193.8838x2 + 2689.2405x3

According to the general form of a multiple linear regression model:

-143481.1924 = intercept(c) ; where regression line crosses the origin

10011.9212x1 - 2193.8838x2 + 2689.2405x3 are weight coefficients of the three predictor variables ; x1, x2, and x3

y = predicted variable