Model Selection & Functional Form: Female (takes a value for 1 if female, 0 if not female) and Non-White (and value of 1 if NonWhite, 0 if white) are dummy variables. Age is measured continuously in years, and Age Squared is the square of Age. Education is measured in years, Earnings measured in dollars, and Log Earnings are the log transformation of earnings. Are the predictor variables in Models A & B statistically significant at the 5% significance level? Carefully interpret and explain the coefficients for Female, Non-White, Age Squared and Education in models A and B. Compare Model A to Model B. Which model would use for purposes of prediction and why? In your preferred model, should we add Age Squared? Is your chosen model a good model? Explain why or why not.
Model Selection & Functional Form: Female (takes a value for 1 if female, 0 if not female) and Non-White (and value of 1 if NonWhite, 0 if white) are dummy variables. Age is measured continuously in years, and Age Squared is the square of Age. Education is measured in years, Earnings measured in dollars, and Log Earnings are the log transformation of earnings. Are the predictor variables in Models A & B statistically significant at the 5% significance level? Carefully interpret and explain the coefficients for Female, Non-White, Age Squared and Education in models A and B. Compare Model A to Model B. Which model would use for purposes of prediction and why? In your preferred model, should we add Age Squared? Is your chosen model a good model? Explain why or why not.
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
- Model Selection &
Functional Form:
Female (takes a value for 1 if female, 0 if not female) and Non-White (and value of 1 if NonWhite, 0 if white) are dummy variables. Age is measured continuously in years, and Age Squared is the square of Age. Education is measured in years, Earnings measured in dollars, and Log Earnings are the log transformation of earnings.
- Are the predictor variables in Models A & B statistically significant at the 5% significance level?
- Carefully interpret and explain the coefficients for Female, Non-White, Age Squared and Education in models A and B.
- Compare Model A to Model B. Which model would use for purposes of prediction and why? In your preferred model, should we add Age Squared? Is your chosen model a good model? Explain why or why not.
Transcribed Image Text:Regression Model B (from R)
Coefficients:
(Intercept)
Estimate Std. Error t value Pr(>|t|)
8.6956410 0.5913211 14.705 < 2e-16 ***
0.1455393 -3.931 0.000088 ***
2.082 0.0375 *
Female
-0.3720635
Non-White
0.3627136
0.2222849
Female *Age 0.0854627 0.0524483 1.629
Age
0.0540141
Age Squared -0.0003535
0.0200741 2.691
0.0001883 -1.877
0.0170229 2.346
0.0399288
0.1034
0.0072 **
0.0606
0.0191 *
.
Education
Residual standard error: 1.733 on 1755 degrees of freedom
(802 observations deleted due to missingness)
Multiple R-squared: 0.03378, Adjusted R-squared: 0.03048
F-statistic: 10.23 on 6 and 1755 DF, p-value: 3.779e-11
Transcribed Image Text:Regression Model A (from R)
Coefficients:
Estimate Std. Error t value
Pr (>|t|)
8943.25
2.701
0.00697 **
2397.93 -10.065
< 2e-16 ***
0.17503
3629.86 -1.357
857.37
2.190
Age
0.02864 *
336.42 5.109 0.0000003570 ***
3.17 -5.718 0.0000000126 ***
263.590 14.648
< 2e-16 ***
Residual standard error: 1.735 on 1758 degrees of freedom
(800 observations deleted due to missingness)
Age Squared
Education
Multiple R-squared: 0.03073, Adjusted R-squared: 0.02797
F-statistic: 11.15 on 5 and 1758 DF, p-value: 1.361e-10
(Intercept) 24156.63
-24134.88
Female
Non-White
-4924.83
Female Age 1877.77
1718.91
-18.13
3860.943
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