Consider the following computer output of a multiple regression analysis relating annual salary to years of education and years of work experience. Multiple R Regression Statistics 0.7357 R Square 0.5412 Adjusted R Square 0.5213 Standard Error 2128.8575 Observations 49 ANOVA df SS Regression 2 245,896,758.5555 MS 122,948,379.2778 27.1287 F Significance F 1.7E-08 Total Residual 46 208,473,570.9955 48 454,370,329.5510 4,532,034.1521 Coefficients Standard Error t Stat Intercept Education (Years) 14265.71682 2352.8476 Experience (Years) 832.0973 2,518.3346 336.7438 390.9718 P-value 5.6647 0.000000918 6.9871 0.00000001 2.1283 0.038700983 Lower 95% Upper 95 % 9196.5722 19,334.8615 1675.0175 3030.6777 45.1119 1619.0827 Step 1 of 2: What would be your expected salary with no education and no experience?
Consider the following computer output of a multiple regression analysis relating annual salary to years of education and years of work experience. Multiple R Regression Statistics 0.7357 R Square 0.5412 Adjusted R Square 0.5213 Standard Error 2128.8575 Observations 49 ANOVA df SS Regression 2 245,896,758.5555 MS 122,948,379.2778 27.1287 F Significance F 1.7E-08 Total Residual 46 208,473,570.9955 48 454,370,329.5510 4,532,034.1521 Coefficients Standard Error t Stat Intercept Education (Years) 14265.71682 2352.8476 Experience (Years) 832.0973 2,518.3346 336.7438 390.9718 P-value 5.6647 0.000000918 6.9871 0.00000001 2.1283 0.038700983 Lower 95% Upper 95 % 9196.5722 19,334.8615 1675.0175 3030.6777 45.1119 1619.0827 Step 1 of 2: What would be your expected salary with no education and no experience?
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 5E
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