I am modeling usage of a particular app like this: predicting week 3 engagement (number of days of the week the product is used) based on prior engagement (week 1 and week 2) and usage of particular product features in week 2.
In the first step, I do an ordinary least squares regression. The model performs well with R^2 of 0.6 and 11 of the 21 variables are highly significant.
Since my dependent variable is actually bounded (by 7), I divide it by 7 and do a logistic regression with the same variables. Now, only 5 variables are significant.
I realise the interpretation of the coefficients is different, but why should 6 variables previously significant in OLS not be significant in the logistic regression? Could it be the multi-collinearity (usage of different features are highly correlated) that poses a problem in OLS but not in logistic regression? I can't find any article that talks about that.