I am new to this forum so please be kind . Not sure if this belongs in 'R'.

I will describe my problem as good as I can. First of all I am using R commander for my Master Thesis.

My Thesis is to identify if there is an association between one predicting variable (categorical 'less than 1', '1-2' ... '10+') and one outcome (numerical 0-60).

Only for these two variables am I correct to use Generalized linear model, Family 'gaussian'?

However, I also would like to adjust this association by other variables. All other variables are categorical and differ between 'yes' and 'no', but also 93 different countries. Am I correct to use also here Generalized linear model, Family 'gaussian'?

Can I use the same model when one predicting variable is numerical? (Predictor = categorical + numerical; outcome = numerical)

To identify confounding factors I have to test each variable against the predictor and the outcome.

Am I correct to use the Pearson's Chi-squared test for Categorical <-> Categorical, even so both categories persist not only out of 'yes' and 'no'? Or would I use Generalized linear mode, Family 'binominal'?

To test an association between one predictor=numerical variable with one outcome=categorical variable, would I use Generalized linear model 'binominal'?

Thank you for you help!