Here is the data I have:
- Response variable : It contains proportions and it takes discrete values 0, 0.2, 0.4, 0.6, 0.8, 1. But there are 109 possible discrete values
- Predictor variable.1: Discrete and ordinal. It contains these values 10, 20, 30, 40.
- Predictor variable.2: Discrete and non-ordinal. It contains these values 'a', 'b', 'c'.
Neither normality (checked with Kolmogorov-Smirnov and by looking at a qqplot) nor homoscedasticity (checked with Fligner and by looking at a plot) are respected.
Which model should I use in order to infer whether any of the two predictor variable influence my response variable?
What about a logistic regression? Would it work?