I'm new to logistic regression. Can you help me understand how to read this?
Here's what I understand -
For every +1 of
continous_variable, the probability of the outcome goes up by 4.88%. If they have 0 for
continous variable, then the probability of the outcome is -314.49%.
Both of these are significant and that means that we can reject the null hypothesis that there is no relationship between
glm(formula = outcome ~ ., family = binomial(link = "logit"), data = dataframe) Deviance Residuals: Min 1Q Median 3Q Max -4.2917 -0.2904 -0.2904 -0.2904 2.5247 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -3.144947 0.095262 -33.01 <2e-16 *** continous_variable 0.048831 0.004774 10.23 <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: 1256.6 on 2817 degrees of freedom Residual deviance: 1065.2 on 2816 degrees of freedom AIC: 1069.2
Is that correct? Is there stuff that I'm missing?
continuous_variable is the number of a particular action that was taken by one person.
outcome is whether that person took a final action coded in 1 or 0. The dataset was analyzed in R and was a dataframe with two fields:
Also, for sample sizes, there are about 3,000 records and about 2,700 where outcome = 1.