I am using R to run a logistic regression to analyze how a categorical variable ("population") correlates with a binary variable ("response") and am having some trouble interpreting the results (shown below).
Only the p-value for the intercept is significant. As I understand it, the intercept deals with what would happen if all x=0. This seems important since one of my variables in binary. Doesn't it imply that there is a correlation between the variables because we're saying that if all were from the same population we'd be able to make some predictions about the response? But if this is true, how is it possible that the other p-value is not significant.
call: glm(formula = response ~ population, family = binomial, data = sfpa9) Deviance Residuals: Min 1Q Median 3Q Max -1.4929 -1.2388 0.8918 0.9482 1.1173 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.7167 0.2662 2.692 0.0071 ** populationSurface -0.5736 0.3777 -1.519 0.1289 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 160.68 on 119 degrees of freedom Residual deviance: 158.35 on 118 degrees of freedom AIC: 162.35 Number of Fisher Scoring iterations: 4` ```