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 outcome
and continous variable
.
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?
Thank you!
Context -
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: continuous_variable
and outcome
.
Also, for sample sizes, there are about 3,000 records and about 2,700 where outcome = 1.