I have one dependent variable name as "win ration" of the deal contested and more than 30 independent variables, all are categorical variable name as role of the customer, geo, region, and 27 competencies marks between 1 to 12 for each competency (this is like the performance parameter "1" is high and "12" is low rating) name as comp1,comp2,....,comp27
My question is that which is the best model or predictor model to filter out which all competencies and other variables are really affection the win rate.
for this i used beta regression but non of the competencies are coming out significant and when i perform the step wise for dim reduction, this method is not working on beta reg. In this case all competencies are treated as quantitative variable, the reason is that all are bound between 1 to 12 and if i will categorize this all 27 competencies will have 12 categories
Please help me to do this analysis