The personnel director for Electronics Associates developed the following estimated regression equation relating an employee's score on a job satisfaction test to his or her length of service and wage rate. = 14.4 - 8.69x1 + 13.5x2
where
x1 = length of service (years)
x2 = wage rate (dollars)
y = job satisfaction test score (higher scores indicate greater job satisfaction)
a. Interpret the coefficients in this estimated regression equation.
b. Predict the job satisfaction test score for an employee who has four years of service and makes $13.00 per hour.

a-1. An increase in the length of service (years) by one year will lead to a reduction in the job satisfaction test score by 8.69.

a-2. An increase in the wage rate (dollars) by $1 will lead to an increase in the job satisfaction test score by 13.5.

b. The predicted score of the job satisfaction test for the employee is 155.14.

Explanation:

a. Interpret the coefficients in this estimated regression equation.

a-1. Interpretation of the coefficient of x1

From the estimated regression equation, the coefficient of x1 is -8.69. Since the coefficient of x1 is negative, it implies that an increase in the length of service (years) by one year will lead to a reduction in the job satisfaction test score by 8.69.

a-2. Interpretation of the coefficient of x2

From the estimated regression equation, the coefficient of x2 is 13.5. Since the coefficient of x2 is positive, it implies that an increase in the wage rate (dollars) by $1 will lead to an increase in the job satisfaction test score by 13.5.

b. Predict the job satisfaction test score for an employee who has four years of service and makes $13.00 per hour.

Given:

y = 14.4 - 8.69x1 + 13.5x2 ………………… (1)

where

x1 = length of service (years) = 4

x2 = wage rate (dollars) = $13

y = job satisfaction test score = ?

Substituting the values into equation (1), we have:

y = 14.4 - (8.69 * 4) + (13.5 * 13)

y = 155.14

Therefore, the predicted score of the job satisfaction test for the employee is 155.14.

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