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Regression Model A: OLS (from R) Coefficients: (Intercept) age educ2 sex smoke Estimate Std. Error t value Pr (>[t]) www <2e-16 *** 0.136269 0.011681 11.666 0.007031 0.000379 18.549 <2e-16 *** 0.003327 0.588 0.557 0.001956 -0.031296 0.003737 -8.375 <2e-16 *** 0.003399 0.004624 0.735 0.462 Signif. codes: 0 ***** 0.001 **** 0.01 ¹* 0.05 '.' 0.1 ' 1 Residual standard error: 0.364 on 33531 degrees of freedom_ (257770 observations deleted due to missingness) Multiple R-squared: 0.01282, Adjusted R-squared: 0.0127 F-statistic: 108.8 on 4 and 33531 DF, p-value: < 2.2e-16

Regression Model A: OLS (from R) Coefficients: (Intercept) age educ2 sex smoke Estimate Std. Error t value Pr (>[t]) www <2e-16 *** 0.136269 0.011681 11.666 0.007031 0.000379 18.549 <2e-16 *** 0.003327 0.588 0.557 0.001956 -0.031296 0.003737 -8.375 <2e-16 *** 0.003399 0.004624 0.735 0.462 Signif. codes: 0 ***** 0.001 **** 0.01 ¹* 0.05 '.' 0.1 ' 1 Residual standard error: 0.364 on 33531 degrees of freedom_ (257770 observations deleted due to missingness) Multiple R-squared: 0.01282, Adjusted R-squared: 0.0127 F-statistic: 108.8 on 4 and 33531 DF, p-value: < 2.2e-16

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
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Female and Smoke are binary indicator variables ( 0 and 1). Age and Education is measured continuously in years. Loan Default is a binary indicator variable (0 = no default, 1=default).

  1. Are the predictor variables in Models A statistically significant at the 5% significance level?
  2. Carefully interpret the coefficients for Female and Education in Models A
  3. Generate a forecast from all 3 models when Age=10, Educ=10, Sex=1, Smoke=0
  4. You are unsure if education has a linear effect on loan default. What are possible transformations you can do to check for non-linearity?

 

**Regression Model A: OLS (from R)** **Coefficients:** | | Estimate | Std. Error | t value | Pr(>|t|) | |--------------|----------|------------|--------|-----------| | (Intercept) | 0.136269 | 0.011681 | 11.666 | <2e-16 ***| | age | 0.007031 | 0.000379 | 18.549 | <2e-16 ***| | educ2 | 0.001956 | 0.003327 | 0.588 | 0.557 | | sex | -0.031296| 0.003737 | -8.375 | <2e-16 ***| | smoke | 0.003399 | 0.004624 | 0.735 | 0.462 | - **Significance codes:** 0 ‘***’, 0.001 ‘**’, 0.01 ‘*’, 0.05 ‘.’, 0.1 ‘ ’, 1 **Residual standard error:** 0.364 on 33531 degrees of freedom *(257770 observations deleted due to missingness)* - **Multiple R-squared:** 0.01282, **Adjusted R-squared:** 0.0127 - **F-statistic:** 108.8 on 4 and 33531 DF, **p-value:** < 2.2e-16 --- This output displays the results of an Ordinary Least Squares (OLS) regression analysis. The key variables along with their estimated coefficients, standard errors, t-values, and p-values are outlined above. Significant predictors at conventional levels are marked with asterisks. The residual standard error, R-squared values, and F-statistic provide additional insights into the model's explanatory power.
Transcribed Image Text:**Regression Model A: OLS (from R)** **Coefficients:** | | Estimate | Std. Error | t value | Pr(>|t|) | |--------------|----------|------------|--------|-----------| | (Intercept) | 0.136269 | 0.011681 | 11.666 | <2e-16 ***| | age | 0.007031 | 0.000379 | 18.549 | <2e-16 ***| | educ2 | 0.001956 | 0.003327 | 0.588 | 0.557 | | sex | -0.031296| 0.003737 | -8.375 | <2e-16 ***| | smoke | 0.003399 | 0.004624 | 0.735 | 0.462 | - **Significance codes:** 0 ‘***’, 0.001 ‘**’, 0.01 ‘*’, 0.05 ‘.’, 0.1 ‘ ’, 1 **Residual standard error:** 0.364 on 33531 degrees of freedom *(257770 observations deleted due to missingness)* - **Multiple R-squared:** 0.01282, **Adjusted R-squared:** 0.0127 - **F-statistic:** 108.8 on 4 and 33531 DF, **p-value:** < 2.2e-16 --- This output displays the results of an Ordinary Least Squares (OLS) regression analysis. The key variables along with their estimated coefficients, standard errors, t-values, and p-values are outlined above. Significant predictors at conventional levels are marked with asterisks. The residual standard error, R-squared values, and F-statistic provide additional insights into the model's explanatory power.
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