Consider the following computer output of a multiple regression analysis relating annual salary to years of education and years of work experience. Regression Statistics Multiple R 0.7352 R Square 0.5405 Adjusted R Square 0.5205 Standard Error 2131.1820 Observations 49 ANOVA df Regression 2 245,793,126.4218 SS MS F Significance F 122,896,563.2109 27.0582 1.7E-08 Residual Total 46 208,929,085.5374 48 454,722,211.9592 4,541,936.6421 Coefficients Standard Error t Stat P-value Lower 95 % Upper 95 % Intercept Education (Years) 14268.68236 2352.2698 2,521.0844 5.6597 0.000000934 9194.0027 19,343.3621 337.1115 6.9777 0.00000001 1673.6995 3030.8401 Experience (Years) 832.2096 391.3987 2.1262 0.03888471 44.3649 1620.0543 Step 2 of 2: How much would you expect your salary to increase if you stayed at the company for another year?
Consider the following computer output of a multiple regression analysis relating annual salary to years of education and years of work experience. Regression Statistics Multiple R 0.7352 R Square 0.5405 Adjusted R Square 0.5205 Standard Error 2131.1820 Observations 49 ANOVA df Regression 2 245,793,126.4218 SS MS F Significance F 122,896,563.2109 27.0582 1.7E-08 Residual Total 46 208,929,085.5374 48 454,722,211.9592 4,541,936.6421 Coefficients Standard Error t Stat P-value Lower 95 % Upper 95 % Intercept Education (Years) 14268.68236 2352.2698 2,521.0844 5.6597 0.000000934 9194.0027 19,343.3621 337.1115 6.9777 0.00000001 1673.6995 3030.8401 Experience (Years) 832.2096 391.3987 2.1262 0.03888471 44.3649 1620.0543 Step 2 of 2: How much would you expect your salary to increase if you stayed at the company for another year?
Calculus For The Life Sciences
2nd Edition
ISBN:9780321964038
Author:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Publisher:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Chapter1: Functions
Section1.2: The Least Square Line
Problem 7E
Question
Transcribed Image Text:Consider the following computer output of a multiple regression analysis relating annual salary to years of education and years of work experience.
Regression Statistics
Multiple R
0.7352
R Square
0.5405
Adjusted R Square
0.5205
Standard Error
2131.1820
Observations
49
ANOVA
df
Regression 2 245,793,126.4218
SS
MS
F
Significance F
122,896,563.2109 27.0582
1.7E-08
Residual
Total
46 208,929,085.5374
48 454,722,211.9592
4,541,936.6421
Coefficients Standard Error
t Stat
P-value
Lower 95 %
Upper 95 %
Intercept
Education (Years)
14268.68236
2352.2698
2,521.0844
5.6597
0.000000934
9194.0027
19,343.3621
337.1115
6.9777 0.00000001
1673.6995
3030.8401
Experience (Years)
832.2096
391.3987
2.1262
0.03888471
44.3649
1620.0543
Step 2 of 2: How much would you expect your salary to increase if you stayed at the company for another year?
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