If you included one variable at time, the OR would be a crude odds ratio... Therefore in this video the OR is actually adjusted. And variables are actually adjusting for the effect in each other
That was a really nice, clear example, and I love that you provided an example writeup as well. That is going to make things so much easier when analysing my data. Sincerely, thanks!
Really nice video. Thank you! But i have a question, how do i do the Omnibus Test for a single variable, like, if i have 7 variabile and i want to start analysing the single omnibus test for single variables not by whole model. Hope you reply, thank you
great video, i have a question, i read that you have to check if your data meets the conditions/assumptions for logistic regression. Does SPSS do that automatically or have you done it before?
If the overal p value is not significant that means that the variable shoudnot included in the model. Even if some of the variable modalities have a significant p
Hey thank you for your video. I have one question. If I have ordinal data (Likert scale) as my independent variables, should I put it into categorical covariates?
I am sure that this is a common dataset you could find in kaggle, or it is also a preinstalled dataset in R. If there's a chance you don't find it, I have it so I can email it to you.
Hi, I have a question. Hopefully you reply. When overall p-value is significant but whole variables p-value are not significant. What will be the comment?
I did it and everything went well. But one thing pokes mi in the eye. I've done the regression for 6 discrete variables as a possible predictor of a binary outcome. For one variable I got a really large number in the table, above 1000 for the Exp (B) and 15000+ for the Upper limit of CI interval 95%
