[R] Interpreting Logistic Regression

[Madhavi Bhave]

Hi !

This is Madhavi from Mumbai, India. Incidently this is my first post.

I am working on Credit Scoring Model and using R, I have run the logistic 
regression. I have received following Output.

I have two questions

(a) What is the significance of "family = binomial(link = logit)". Why do I 
have to mention Binomial? Is it because my dependent variable assumes only two 
values 0 and 1? Can I write name of some other Statistical distribution (say 
Poisson or Negative Binomial) in place of Binomial? How will it affect my 
results?

(b) How do I interpret the "R" result as given below? I know all the variables 
are significant. How do I get Log Likelihood ratio, Odds ratio etc.?

Please can anyone help me out.

With warm regards

Madhavi



R OUTPUT


Call:
glm(formula = Y ~ Age1 + Age2 + Sex + Education + Profession + SavingsAccount + 
    CurrentAccount, family = binomial(link = logit), data = ons)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-3.21142  -0.42556  -0.15911  -0.02954   3.02465  

Coefficients:
                  Estimate Std. Error z value Pr(>|z|)    
(Intercept)       2.627725   0.110752  23.726  < 2e-16 ***
Age1              0.692180   0.070410   9.831  < 2e-16 ***
Age2             -2.817883   0.080801 -34.874  < 2e-16 ***
Sex              -0.486132   0.049766  -9.768  < 2e-16 ***
Education        -0.682142   0..046507 -14.667  < 2e-16 ***
Profession       -0.690937   0.069032 -10.009  < 2e-16 ***
SavingsAccount   -1.891455   0.074906 -25.251  < 2e-16 ***
CurrentAccount   -1.367460   0.079604 -17.178  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ 
’ 1 

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 26932  on 24999  degrees of freedom
Residual deviance: 14615  on 24983  degrees of freedom
  (2 observations deleted due to missingness)
AIC: 14649

Number of Fisher Scoring iterations: 6




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