I have the wine data from here which consists of 11 numerical independent variables with a dependent rating associated with each entry with values between 0 and 10. This makes it a great dataset to use a regression model to investigate the relation between the variables and the associated rating. However, would linear regression be appropriate, or is it better to use multinomial/ordered logistic regression?
Logistic regression seems better given specific categories, i.e. not a continuous dependent variable but (1) there are 11 categories (a bit too many?) and (2) upon inspection, there's only data for 6-7 of those categories, i.e. the remaining 5-4 categories have no example in the dataset.
On the other hand, linear regression should linearly estimate a rating between 0-10 which seems closer to what I'm trying to find out; yet the dependent variable is not continuous in the dataset.
Which is the better approach? Note: I am using R for the analysis
Edit, addressing some of the points mentioned in the answers:
- There is no business goal as this is actually for a university course. The task is to analyze a dataset of choice whichever way I see fit.
- The distribution of the ratings looks normal (histogram/qq-plot). The actual values in the dataset are between 3-8 (even though technically 0-10).