Fred G. Hire is the manager of human resources at Crescent Tool and Die, Inc. As part of his yearly report to the CEO, he is required to present an analysis of the salaried employees. Because there are over 1,000 employees, he does not have the staff to gather information on each salaried employed, so he selects a random sample of 30 (Fred G. Hire worksheet). For each employee, he records monthly salary; service at Crescent, in months; gender (1 = male, 0 = female); and whether the employee has a technical or clerical job. Those working technical jobs are coded 1 and those who are clerical 0. Fred uses excel to fit a multiple regression model using salary as the dependent or Y variable and the other four variables as independent or X variables. The output appears below. Use this output for question 36) below. 36) The regression model above suggests that for two employees (one male and one female) who both have 10 years of service, work technical jobs, and are the same age: A) The male employee makes $651.86 more than the female employee. B) The male employee makes $205.65 more than the female employee C) The model predicts the same salary for both employees. D) The female employee makes $205.65 more than the male employee E) The female employee makes $651.86 more than the male employee. As part of this analysis Fred uses excel to fit a multiple regression model using salary as the dependent or Y variable and Service and Gender as independent or X variables. The output appears below. Use this output for question 37). 37) A sketch of the regression model in the salary and service plane would have what structure A) Three parallel lines B) Two lines with the same intercept and different slopes C) Two parallel lines D) Two lines with the different intercepts and different slopes E) Three lines with the different intercepts and different slopes Fred would like to use the Partial F-test with a = 0.05 to determine if both Age and Job can be removed from the regression model. Use the output on this page above, along with the output on the last page, for question 38). 38) What is the value of Fo, the test statistic of the Partial F-test. A) 1.65 B) 9.19 C) 0.61 D) 4.77 E) 2.27

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Use the following scenario and data when answering questions 36) -> 38):


Fred G. Hire is the manager of human resources at Crescent Tool and Die, Inc. As part of
his yearly report to the CEO, he is required to present an analysis of the salaried
employees. Because there are over 1,000 employees, he does not have the staff to gather
information on each salaried employed, so he selects a random sample of 30 (Fred G. Hire
worksheet). For each employee, he records monthly salary; service at Crescent, in
months; gender (1 = male, 0 = female); and whether the employee has a technical or
clerical job. Those working technical jobs are coded 1 and those who are clerical 0.
Fred uses excel to fit a multiple regression model using salary as the dependent or Y
variable and the other four variables as independent or X variables. The output appears
below. Use this output for question 36) below.

36) The regression model above suggests that for two employees (one male and one
female) who both have 10 years of service, work technical jobs, and are the same age:
A) The male employee makes $651.86 more than the female employee.
B) The male employee makes $205.65 more than the female employee
C) The model predicts the same salary for both employees.
D) The female employee makes $205.65 more than the male employee
E) The female employee makes $651.86 more than the male employee.

As part of this analysis Fred uses excel to fit a multiple regression model using salary as
the dependent or Y variable and Service and Gender as independent or X variables. The
output appears below. Use this output for question 37).

37) A sketch of the regression model in the salary and service plane would have what
structure
A) Three parallel lines
B) Two lines with the same intercept and different slopes
C) Two parallel lines
D) Two lines with the different intercepts and different slopes
E) Three lines with the different intercepts and different slopes

Fred would like to use the Partial F-test with a = 0.05 to determine if both Age and Job
can be removed from the regression model. Use the output on this page above, along with
the output on the last page, for question 38).
38) What is the value of Fo, the test statistic of the Partial F-test.
A) 1.65
B) 9.19
C) 0.61
D) 4.77
E) 2.27

 

 

 

Regression Statistics
Multiple R
R Square
Adjusted R Square
Standard Error
Observations
ANOVA
Regression
Residual
Total
Intercept
Service
Age
Gender
Job
0.6578
0.4327
0.3419
236.53
30
df
4
25
29
Coefficients
651.86
13.42
-6.71
205.65
-33.45
SS
1066830.39
1398650.98
2465481.37
Standard Error
345.30
5.13
6.35
90.27
89.55
MS
266707.60
55946.04
t Stat
1.89
2.62
-1.06
2.28
-0.37
F
4.77
P-value
0.0707
0.0148
0.3007
0.0315
0.7119
Significance F
0.0054
Transcribed Image Text:Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept Service Age Gender Job 0.6578 0.4327 0.3419 236.53 30 df 4 25 29 Coefficients 651.86 13.42 -6.71 205.65 -33.45 SS 1066830.39 1398650.98 2465481.37 Standard Error 345.30 5.13 6.35 90.27 89.55 MS 266707.60 55946.04 t Stat 1.89 2.62 -1.06 2.28 -0.37 F 4.77 P-value 0.0707 0.0148 0.3007 0.0315 0.7119 Significance F 0.0054
Multiple R
R Square
Adjusted R Square
Standard Error
Observations
ANOVA
Regression Statistics
Regression
Residual
Total
Intercept
Service
Gender
0.6385
0.4051
0.3610
233.07
30
2
27
29
Coefficients
784.19
9.02
224.41
SS
998778.67
1466702.70
2465481.37
Standard Error
316.82
3.11
87.35
MS
499389.33
54322.32
t Stat
2.48
2.90
2.57
F
9.19
P-value
0.0199
0.0073
0.0160
Significance F
0.0009
Transcribed Image Text:Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Statistics Regression Residual Total Intercept Service Gender 0.6385 0.4051 0.3610 233.07 30 2 27 29 Coefficients 784.19 9.02 224.41 SS 998778.67 1466702.70 2465481.37 Standard Error 316.82 3.11 87.35 MS 499389.33 54322.32 t Stat 2.48 2.90 2.57 F 9.19 P-value 0.0199 0.0073 0.0160 Significance F 0.0009
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