I have conducted a logistic regression in R
> Model <- glm(A ~ B + C, family = "binomial", data = Data) > summary(Model) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -1.6138 678.6939 -0.002 0.9981 BPu 1.0003 0.5539 1.806 0.0709 . C.L 21.2450 2146.2181 0.010 0.9921 C.Q 1.2210 1813.8853 0.001 0.9995 C.C 9.8965 1073.1091 0.009 0.9926 C^4 -0.3275 405.5973 -0.001 0.9994 exp(coef(Model)) (Intercept) BPu C.L C.Q 1.991295e-01 2.719031e+00 1.684921e+09 3.390646e+00 C.C C^4 1.986151e+04 7.207529e-01
As I understand it when the independent variable, B, (a binary variable) changes to Pu this is associated with an increase in the log odds of a "success" in the dependent by 1.0003, or that odds of increasing the dependent variable are multiplied by 3.22 relative to the intercept, and this change is near significant.
Can I say a similar statement for the variable CR, a 5 level ordinal variable? I’ve found online that L, Q, C, ^4 represent linear quadratic, cubic … but I haven’t found an answer outlining what I can practically say about these coefficients or how to interpret them.
I understand that the influence is likely to be insignificant, but which P value do I use? What can I say about the other coefficients?