Question: Regression Statistics Multiple R 0.70995212 R Square 0.50403202 Adjusted R Square 0.50086627 Standard Error 12063.8519 Observations 474 ANOVA df SS MS F Significance F Regression

Regression Statistics
Multiple R 0.70995212
R Square 0.50403202
Adjusted R Square 0.50086627
Standard Error 12063.8519
Observations 474
ANOVA
df SS MS F Significance F
Regression 3 6.9514E+10 2.3171E+10 159.21394 3.3298E-71
Residual 470 6.8402E+10 145536523
Total 473 1.3792E+11
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept -13313.269 2763.35782 -4.8177869 1.9618E-06 -18743.334 -7883.2043 -18743.334 -7883.2043
gender 9022.21202 1201.22739 7.51082778 2.9827E-13 6661.77117 11382.6529 6661.77117 11382.6529
minority -5116.84 1362.97752 -3.7541631 0.00019571 -7795.1237 -2438.5562 -7795.1237 -2438.5562
education 3257.19864 208.853422 15.5956201 1.708E-44 2846.79661 3667.60066 2846.79661 3667.60066

  1. A measure on how well the independent variables gender, minority, and education are able to explain the variation in average salary is the adjusted R Squared. What percentage of the variation in average salaries is described by these variables?
  2. The t Stat is a measure of how an individual independent variable explains variation in the dependent variable average salary. An absolute value greater than 2 is generally considered a significant value in explaining variation. What do the t Stats tell us about the ability of the variables gender, minority, and education to explain average salary?

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