What happens when one of your interactions (Variable by LN_Variable) doesn't show up in the table? I put in 5 variables and their interactions with the log-odds, but only 4 show up. It also doesn't show up when I just add that one.
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?
Ma'am when I checking my assumption of outlier and applying CDF formula in that case it is showing "the expression ends unexpectedly" In this case, what can i do? I have 7 independent variables and 3 binary dependent variables.
Thank you for a great video! I have a question: why is it important to centre the variables? Also, in my case, I want to look at the interaction effect of 'Incumbency' on the 'total amount of money' a candidate receives in an election when trying to predict 'vote share (%)' (DV). However, while money is a numerical value, I have coded 'incumbency' as a binary value of either 1 (they are an incumbent) or 0 (they are a challenger, or the seat is uncontested). Can I just multiply these together as you did in the video, even though my variables are different types of data? Thank you for your help!
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!
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%
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
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
Hi! thanks for the tutorial. I have one question tho: isn't the normality assumption of linear models supposed to be checked on the residuals of the model? i.e. on the pooled deviation between the values as predicted by the model and the actual data?