So I have a data set, and I'd like to test the influence of a continuous variable (cont) on a categorical (binary) variable (cat) that can be 0 (yes) or 1 (no). I've looked on the internet and binary logistic regression seems to be a good choice. So I plugged this into R:
glm(cat~cont, family = binomial(``logit"))
and got the following results from the summary:
Deviance residuals: Min 1Q Median 3Q Max -1.0757 -0.9077 -0.7019 1.3583 1.9911 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.65847 0.58545 1.125 0.26070 cont -0.04297 0.01652 -2.602 0.00927 (Dispersion parameter for binomial family taken to be 1) Null deviance: 244.35 on 199 degrees of freedom Residual deviance: 237.11 on 198 degrees of freedom AIC: 241.11 Number of Fisher Scoring iterations: 4
I can see that the p-value is 0.00927, so I definitely have something significant. But how can I interpret this? I've looked on the internet but haven't found anything useful, but it's possible I just didn't understand it. From what I did understand though, the deviances are really high, but then why do I get something significant? If anyone could help me, or explain how logistic regression works, that'd be great!