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10. Fill in the blank 1. z-value. 2. p-value (please write down the formula), and 3 significance code.

10. Fill in the blank 1. z-value. 2. p-value (please write down the formula), and 3 significance code.

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
Problem 1P
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10. Fill in the blank 1. z-value. 2. p-value (please write down the formula), and 3. significance code. 11. Predicting the likelihood that a 750 credit score customer repays the loan. 12. In a logistic regression, the slope coefficient estimate can be interpreted as the average change in the dependent variable as the independent variable increases by one unit. (If you choose False, please correct the sentence or write down the reason. If you choose True, you do not need to provide any reasons. ) A. True B. False For questions 13-14, refer to the below confusion matrix. Predicted = default Predicted reрay default 3730 961 repay 572 4315 13. What is the false negative rate? 14. What is the overall accuracy of the model?
Transcribed Image Text:10. Fill in the blank 1. z-value. 2. p-value (please write down the formula), and 3. significance code. 11. Predicting the likelihood that a 750 credit score customer repays the loan. 12. In a logistic regression, the slope coefficient estimate can be interpreted as the average change in the dependent variable as the independent variable increases by one unit. (If you choose False, please correct the sentence or write down the reason. If you choose True, you do not need to provide any reasons. ) A. True B. False For questions 13-14, refer to the below confusion matrix. Predicted = default Predicted reрay default 3730 961 repay 572 4315 13. What is the false negative rate? 14. What is the overall accuracy of the model?
Question 2: Logistic Regression For questions 9 - 11, use the model below where the data contains the following information on the n = 9578 peer-to-peer loans. repay: 1 if customer repaid the loan, 0 else. fico: Credit score on 0 - 850 scale. Call: glm(formula repay fico, family = binomial, data = Lending) %3D Deviance Residuals: Min 1Q Median 3Q Маx -2.4863 0.4185 0.5538 0.6518 0.9544 Coefficients: Estimate Std. Error z value Pr(>Izl) (Intercept) -6.7188776 0.5768348 .-11.65 <2e-16 *** 3 fico 0.0118775 0.0008231 1 Signif. codes: 0 ' 0.1 ' 1 (***) 0.001 (**> 0.01 *> 0.05 (Dispersion parameter for binomial family taken to be 1) Null deviance: 8424.0 Residual deviance: 8196.1 on 9576 degrees of freedom on 9577 degrees of freedom AIC: 8200.1 Number of Fisher Scoring iterations: 4 Figure 3: Logistic Regression Table 9. What is the estimated regression model?
Transcribed Image Text:Question 2: Logistic Regression For questions 9 - 11, use the model below where the data contains the following information on the n = 9578 peer-to-peer loans. repay: 1 if customer repaid the loan, 0 else. fico: Credit score on 0 - 850 scale. Call: glm(formula repay fico, family = binomial, data = Lending) %3D Deviance Residuals: Min 1Q Median 3Q Маx -2.4863 0.4185 0.5538 0.6518 0.9544 Coefficients: Estimate Std. Error z value Pr(>Izl) (Intercept) -6.7188776 0.5768348 .-11.65 <2e-16 *** 3 fico 0.0118775 0.0008231 1 Signif. codes: 0 ' 0.1 ' 1 (***) 0.001 (**> 0.01 *> 0.05 (Dispersion parameter for binomial family taken to be 1) Null deviance: 8424.0 Residual deviance: 8196.1 on 9576 degrees of freedom on 9577 degrees of freedom AIC: 8200.1 Number of Fisher Scoring iterations: 4 Figure 3: Logistic Regression Table 9. What is the estimated regression model?
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