Need stats/excel homework help Create a single ression equation for starting sal
ID: 3060323 • Letter: N
Question
Need stats/excel homework help
Create a single ression equation for starting salaries using the independant variable that has the closest relationships with the salaries. And the reason why you chose that variable.
Student School_Ranking GPA Experience Salary 1 78 2.92 3 73,590 2 56 3.84 9 87,000 3 23 3.04 6 76,970 4 67 3.20 6 79,320 5 56 3.61 7 79,530 6 78 2.99 5 71,040 7 68 3.78 8 82,050 8 89 3.20 5 78,890 9 37 3.42 7 82,170 10 67 3.05 5 76,120 11 48 3.12 4 77,500 12 78 3.56 7 83,920 13 56 3.01 5 71,800 14 25 3.15 6 77,000 15 68 3.05 7 79,000 16 36 3.24 5 77,800 17 76 3.25 6 80,600 18 78 3.78 9 87,000 19 67 3.12 4 78,450 20 67 3.24 8 80,600 21 15 2.98 5 74,900 22 29 3.24 6 79,200 23 49 3.08 4 77,000 24 67 3.00 6 77,900 25 39 2.95 4 76,950 26 81 3.01 5 76,800 27 54 3.23 7 79,300 28 72 3.01 2 72,120 29 73 3.45 7 83,900 30 78 3.85 8 85,200 31 51 3.00 5 77,300 32 86 3.23 6 83,500 33 76 3.80 7 77,000 34 30 3.08 5 75,000 35 58 3.15 7 79,200 36 86 3.35 7 80,400 37 34 3.09 7 80,200 38 72 3.35 9 84,800 39 38 3.16 3 72,800 40 89 2.76 7 75,000Explanation / Answer
First of all we model the equation using all the three independent variables i.e. School_Ranking and Experience. Please see below the excel output: -
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.840
R Square
0.705
Adjusted R Square
0.681
Standard Error
2242.363
Observations
40
ANOVA
df
SS
MS
F
Significance F
Regression
3
433447942.730
144482647.577
28.735
0.000
Residual
36
181014847.270
5028190.202
Total
39
614462790.000
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept
53086.914
4627.483
11.472
0.000
43701.944
62471.884
School_Ranking
9.738
18.157
0.536
0.595
-27.086
46.561
GPA
5449.971
1721.276
3.166
0.003
1959.061
8940.880
Experience
1243.192
286.806
4.335
0.000
661.523
1824.861
As we can see based on the p-value, School_Ranking is not a significant variable. Hence, we would remove that and rerun and this would be our final model
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.84
R Square
0.70
Adjusted R Square
0.69
Standard Error
2220.67
Observations
40
ANOVA
df
SS
MS
F
Significance F
Regression
2
432001745.96
216000872.98
43.80
0.00
Residual
37
182461044.04
4931379.57
Total
39
614462790.00
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept
53295.45
4566.51
11.67
0.00
44042.82
62548.08
GPA
5539.80
1696.54
3.27
0.00
2102.29
8977.30
Experience
1257.26
282.84
4.45
0.00
684.17
1830.35
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.840
R Square
0.705
Adjusted R Square
0.681
Standard Error
2242.363
Observations
40
ANOVA
df
SS
MS
F
Significance F
Regression
3
433447942.730
144482647.577
28.735
0.000
Residual
36
181014847.270
5028190.202
Total
39
614462790.000
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept
53086.914
4627.483
11.472
0.000
43701.944
62471.884
School_Ranking
9.738
18.157
0.536
0.595
-27.086
46.561
GPA
5449.971
1721.276
3.166
0.003
1959.061
8940.880
Experience
1243.192
286.806
4.335
0.000
661.523
1824.861
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