How do I explain why this is a good outcome?

A First Course in Probability (10th Edition)
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Chapter1: Combinatorial Analysis
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Problem 1.1P: a. How many different 7-place license plates are possible if the first 2 places are for letters and...
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How do I explain why this is a good outcome?

 

 

### Logistic Regression Model Summary

**Deviance Residuals:**
- Min: -2.9114
- 1Q: -0.1921
- Median: -0.1226
- 3Q: -0.0792
- Max: 3.7793

**Coefficients:**

| Variable                | Estimate | Std. Error | z value | Pr(>|z|)  |
|-------------------------|----------|------------|---------|----------|
| (Intercept)             | -7.025e+00 | 1.763e-01  | -39.848 | < 2e-16  |
| Child6-12               | 1.656e-02 | 1.213e-03  | 13.648  | < 2e-16  |
| Child13-18              | -1.451e-02 | 1.243e-03  | -11.672 | < 2e-16  |
| Income                  | 3.733e-05 | 9.518e-07  | 39.229  | < 2e-16  |
| PaidDirectMailOrders    | 8.908e-02 | 2.690e-02  | 3.311   | 0.000929 |
| DollarsPerIssue         | 1.923e+00 | 8.073e-02  | 23.814  | < 2e-16  |
| MonthsSinceIncOrder     | 7.391e-03 | 4.291e-04  | 17.228  | < 2e-16  |
| MonthsSinceLastOrder    | -1.891e-02 | 1.308e-03  | -14.459 | < 2e-16  |

**Significance Codes:**
- '***' 0
- '**' 0.001
- '*' 0.01
- '.' 0.05
- ' ' 0.1

**Dispersion parameter for binomial family taken to be 1**

- Null deviance: 6261.9 on 31495 degrees of freedom
- Residual deviance: 5042.8 on 31492 degrees of freedom
- AIC: 5058.8
Transcribed Image Text:### Logistic Regression Model Summary **Deviance Residuals:** - Min: -2.9114 - 1Q: -0.1921 - Median: -0.1226 - 3Q: -0.0792 - Max: 3.7793 **Coefficients:** | Variable | Estimate | Std. Error | z value | Pr(>|z|) | |-------------------------|----------|------------|---------|----------| | (Intercept) | -7.025e+00 | 1.763e-01 | -39.848 | < 2e-16 | | Child6-12 | 1.656e-02 | 1.213e-03 | 13.648 | < 2e-16 | | Child13-18 | -1.451e-02 | 1.243e-03 | -11.672 | < 2e-16 | | Income | 3.733e-05 | 9.518e-07 | 39.229 | < 2e-16 | | PaidDirectMailOrders | 8.908e-02 | 2.690e-02 | 3.311 | 0.000929 | | DollarsPerIssue | 1.923e+00 | 8.073e-02 | 23.814 | < 2e-16 | | MonthsSinceIncOrder | 7.391e-03 | 4.291e-04 | 17.228 | < 2e-16 | | MonthsSinceLastOrder | -1.891e-02 | 1.308e-03 | -14.459 | < 2e-16 | **Significance Codes:** - '***' 0 - '**' 0.001 - '*' 0.01 - '.' 0.05 - ' ' 0.1 **Dispersion parameter for binomial family taken to be 1** - Null deviance: 6261.9 on 31495 degrees of freedom - Residual deviance: 5042.8 on 31492 degrees of freedom - AIC: 5058.8
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Multiple linear regression model:

A multiple linear regression model is given as y = b0 + b1x1 + b2x2 + …+ bkxk  where y is the predicted value of response variable, and x1, x2,…, xk  are the k predictor variables. The quantities b1, b2,…, bk  are the estimated slopes corresponding to x1, x2,…, xk   respectively and b0 is the estimated intercept of the line, from the sample data.

A multiple regression equation describes the combined effect of all the predictors in the model. Even when the effect of a particular predictor is being studied from a multiple regression equation, it assumes that the effects of all the other predictors are accounted for.

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