How do I explain why this is a good outcome?
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](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fcb457854-bac3-4449-9cfb-ac619b2a6ab2%2Fc47a1996-446e-4668-8112-daa303998949%2Fslga32j_processed.jpeg&w=3840&q=75)
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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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