# Which model to use to handle multiple levels(10-12) in many categorical independent variables and a continuous dependent variable?

I have a continuous dependent variable. 5 categorical independent variable with 7-12 levels in each. Converting into dummy variables and using regression doesn't sound good as there will be so many variables. Which analytical methods can be used here ?

• Is it a ' self study" question? If yes give some data and indicate the objective.
– user10619
Apr 28, 2016 at 12:22
• You really need to give us more details! How many observations, for instance. Or better: tell us about the applied problem, in the language of the application. Then maybe ... Sep 20, 2016 at 16:22
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• this is just one observation. All predictors are categorical, Dependent variable is click probability.We have more than 1 million observations. Sep 22, 2016 at 5:14
• Unless there is some kind of structure in the categorical predictors (maybe they are ordinal, or there is some kind of random effects you can exploit) I am afraid it doesn't look like a soluble problem. You essentially have about $10^5 = 100000$ unique groups and one million observations, thus roughly, your MLE estimate is going to be the average value from 10 observations.
– Alex
Aug 24, 2018 at 6:43

## 1 Answer

ANOVA is recommended when you have a continuous dependent variable, and a categorical independent variable.

Logistic regression could be used if you convert the dependent variable into a categorical one.