# Southern Textiles Inc Wishes To Predict Employee Wages By Using Employee S Experience

Southern Textiles Inc. wishes to predict employee wages by using employee’s experience (months of service) and the employee’s education (0=no college degree, 1=college degree). A sample of 20 employees is selected at random.

Y = WAGES (in \$1,000s)
X1= EXP (experience in months)
X2= EDUC (dummy variable 0=no college, 1=college)

The data is given below (in MINITAB).

WAGES

EXP

EDUC

PEXP

PEDUC

37.1

47

1

48

1

30.1

40

0

48

0

35.1

37

1

32.3

45

0

35.2

42

1

37.4

46

1

23.8

17

1

21.0

29

0

32.4

31

1

40.3

60

0

38.5

48

1

36.7

55

0

31.9

43

0

32.1

47

0

28.7

40

0

31.8

37

1

21.8

20

0

24.1

31

0

33.1

37

1

40.8

50

1

Correlations: WAGES, EXP, EDUC

WAGES EXP
EXP  0.851
0.000

EDUC 0.403  -0.086
0.078 0.719

Cell Contents: Pearson correlation
P-Value

Regression Analysis: WAGES versus EXP, EDUC

The regression equation is
WAGES = 9.94 + 0.487 EXP + 5.50 EDUC.

Predictor  Coef  SE Coef  T  P
Constant 9.938  1.263  7.87  0.000
EXP 0.48688  0.02897  16.80  0.000
EDUC 5.4964  0.6077   9.04  0.000

S = 1.35387  R-Sq = 95.2%  R-Sq(adj) = 94.7%

Analysis of Variance

Source  DF  SS  MS F  P
Regression  2  624.36  312.18  170.31  0.000
Residual Error  17  31.16 1.83
Total  19  655.52

Predicted Values for New Observations

New Obs Fit SE Fit 95% CI 95% PI
1  38.805  0.498  (37.753, 39.856)  (35.761, 41.848)
2  33.308  0.474  (32.309, 34.308)  (30.282, 36.334)

Values of Predictors for New Observations

New Obs  EXP EDUC
1  48.0  1.00
2  48.0  1.00

a. Analyze the above output to determine the multiple regression equation.
b. Find and interpret the multiple index of determination (R-Sq).
c. Perform the t-tests on  (use two tailed test with (a = .05). Interpret your results.
d. Predict the wage for an individual having 48 months of experience and a college degree. Use both a point estimate and the appropriate interval estimate