0

I am trying to interpret the results from a binary logistic regression I have run, however I am struggling to wrap my head around the odds, probability and likelihood. This is the code and the results that I got:

logisticlm <- glm(Gov_adoption_binary ~ AC_5_Clusters, data = df2new, family = 'binomial')
summary(logisticlm)

eviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.4823  -1.2346  -0.6039   1.0293   1.8930  

Coefficients:
               Estimate Std. Error z value Pr(>|z|)   
(Intercept)      0.1335     0.2988   0.447  0.65496   
AC_5_Clusters2   0.4612     0.4316   1.069  0.28527   
AC_5_Clusters3  -1.7430     0.5379  -3.241  0.00119 **
AC_5_Clusters4  -0.1335     0.6995  -0.191  0.84861   
AC_5_Clusters5   0.5596     1.2607   0.444  0.65711

Does anyone know how to interpret the coefficients at all? any advice would go a long way

  • What are your `AC_5_Clusters` variables? It looks like you have five categories and you want to predict the probability of some outcome based on that category. Is that correct? – Dave Aug 10 '21 at 19:32

0 Answers0