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 0.7357 R Square 0.5412 Adjusted R Square 0.5213 Standard Error 2128.8575 Observations 49 ANOVA df SS MS F Significance F 122,948,379.2778 27.1287 1.7E-08 Regression 2 245,896,758.5555 Total Residual 46 46 208,473,570.9955 48 454,370,329.5510 4,532,034.1521 Coefficients Standard Error t Stat P-value Lower 95 % Upper 95 % Intercept Education (Years) 14265.71682 2352.8476 2,518.3346 Experience (Years) 832.0973 336.7438 390.9718 5.6647 0.000000918 6.9871 0.00000001 2.1283 0.038700983 9196.5722 19,334.8615 1675.0175 3030.6777 45.1119 1619.0827 Step 2 of 2: How much would you expect your salary to increase if you had one more year of education?
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 0.7357 R Square 0.5412 Adjusted R Square 0.5213 Standard Error 2128.8575 Observations 49 ANOVA df SS MS F Significance F 122,948,379.2778 27.1287 1.7E-08 Regression 2 245,896,758.5555 Total Residual 46 46 208,473,570.9955 48 454,370,329.5510 4,532,034.1521 Coefficients Standard Error t Stat P-value Lower 95 % Upper 95 % Intercept Education (Years) 14265.71682 2352.8476 2,518.3346 Experience (Years) 832.0973 336.7438 390.9718 5.6647 0.000000918 6.9871 0.00000001 2.1283 0.038700983 9196.5722 19,334.8615 1675.0175 3030.6777 45.1119 1619.0827 Step 2 of 2: How much would you expect your salary to increase if you had one more year of education?
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
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
0.7357
R Square
0.5412
Adjusted R Square
0.5213
Standard Error
2128.8575
Observations
49
ANOVA
df
SS
MS
F
Significance F
122,948,379.2778 27.1287
1.7E-08
Regression 2 245,896,758.5555
Total
Residual 46
46 208,473,570.9955
48 454,370,329.5510
4,532,034.1521
Coefficients Standard Error t Stat
P-value
Lower 95 %
Upper 95 %
Intercept
Education (Years)
14265.71682
2352.8476
2,518.3346
Experience (Years)
832.0973
336.7438
390.9718
5.6647 0.000000918
6.9871 0.00000001
2.1283 0.038700983
9196.5722
19,334.8615
1675.0175
3030.6777
45.1119
1619.0827
Step 2 of 2: How much would you expect your salary to increase if you had one more year of education?
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