Question: Recall that in exercise 50 the personnel director for Electronics Associates developed the following estimated regression equation relating an employee’s score on a job satisfaction test to length of service and wage rate.

where

= length of service (years)

= wage rate (dollars)

y = job satisfaction test score (higher scores indicate greater job satisfaction)

A portion of the Minitab computer output follows.

The regression equation is

Y = 14.4 – 8. 69 X1 + 13.52 X2

Predictor Coef SE Coef T

Constant 14.448 8.191 1.76

X1 __ -8.69 ____ 1.555 __ -5.5884 ___

X2 13.517 2.085 __ 6.4830 ___

S = 3.773 R–sq = __ 0.901152778 ____% R–sq( adj ) = ___ 0.861614 ___%

Analysis of Variance

SOURCE DF SS MS F

Regression 2 _ 648.83 ____ _ 324.415_ ___ _ 22.79 __

Residual Error ___ 5 __ 71.17 __ 14.234 ___

Total 7 720.0

1. Compute the missing entries in this output.
2. Use the F test and α = .05 to see whether a significant relationship is present.
3. Use the t test and α= .05 to test : = 0 and : = 0
4. Did the estimated regression equation provide a good fit to the data? Explain.

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