# how to interpret a glm output in r [closed]

I am very new to R but am trying to interpret each figure within my output. Could I please have some help. I understand that I have three statistically significant variables relating to my dependent variable but that is all.

this is the code in put in :

reg1 <- glm(Aviolever ~ Ahhinc5 + Aupbring +
+               Aedqual + Ah1mumg + Ah1dadg, data =youngoffenders1, family = binomial)
summary(reg1)


Here is the output I obtain:

Call:
glm(formula = Aviolever ~ Ahhinc5 + Aupbring + Aedqual + Ah1mumg +
Ah1dadg, family = binomial, data = youngoffenders1)

Deviance Residuals:
Min       1Q   Median       3Q      Max
-2.5193  -0.6641  -0.6380   0.5112   1.8789

Coefficients:
Estimate Std. Error z value Pr(>|z|)

(Intercept) -5.25986    0.51351 -10.243  < 2e-16 ***
Ahhinc5     -0.04450    0.03242  -1.373   0.1698
Aupbring     0.20600    0.09829   2.096   0.0361 *
Aedqual     -0.02748    0.05514  -0.498   0.6183
Ah1mumg      3.13435    0.19987  15.682  < 2e-16 ***
Ah1dadg      0.84593    0.21706   3.897 9.73e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 1933.2  on 1479  degrees of freedom
Residual deviance: 1391.6  on 1474  degrees of freedom
AIC: 1403.6

Number of Fisher Scoring iterations: 4

• Welcome to CV. Since you’re new here, you may want to take our tour, which has information for new users. Do you have any specific question? (Please read How do I ask a good question?) If not, it might be better to check an introductory text on GLMs (using R?) to understand the results. – T.E.G. - Reinstate Monica May 31 at 8:16
• Why was this put on hold? – David May 31 at 23:15

What you have here is simply a linaer regression model, but instead of predicting the "target", you are predicting the logarythm of its odds (i.e. $$\frac{\log(prob(Y=1))}{\log(prob(Y=0))}$$), so everything regarding coefficients is the same as in linear regression, but keeping that transformation in mind. "Intercept" gives you the "base" log odds (the log odds when all the variables are 0) and the coefficients that are associated to a variable give you how much that log odds goes up every time the corresponding varaible goes up by 1 unit.