Logistic regression F value I have a question that I’ve been struggling finding the correct answer to. I wonder if you can help?
I am performing a logistic regression with multiple variables,only one variable is statistically significant in the output but when I try to introduce other variables (that are not significant) the F value becomes non-significant. The question is: can I still present those variables in the results section of my paper, or is that forbidden because of the F not being significant?
Thank you in advance 
Corina
 A: You should report everything that you test in your paper be they significant or non-significant. If you don't, then it gives rise to selective reporting; also known as p-hacking. 
What is the goal behind the exercise? Is it just to see which variables are related and how they relate or do you want to predict? 
If it is the former then standard practice is to feed in all your variables (i.e. multiple linear regression) and then simply report p-values, effect size and degrees of freedom. This only works well if you have specific hypotheses you are wanting to test and typically you only want a handful to test at a given time. If you are more interested in exploring which variables are related generally to generate hypotheses then I would suggest you look into multivariate methods like principle component analysis (PCA), although this somewhat depends on your data.
If the goal is prediction then one can refine the model (and there a lot of different ways) down to significant predictors so that your model generalises well to new data.
If you provide more information I am happy to help more! 
A: Yes, you can report both significant and non-significant variables.
Your question is not limited to logistic regression, but can be generalized into linear model feature selection.
There are many discussions on this topic, people are asking if they should remove non-significant variables and fit the model again. Or how to pick "important variables" only to build the model.
You can try some experiments by yourself, by selecting different subset of the variables to fit the model, you will notice the significant variables will change. They are depending on the input variables.
