How to perform linear regression on very large categorical variable? I have to do linear regression on a dataset where variable "Brand" is categorical and had approx 120 different brands.
I conducted linear regression on it, but it creates a lot of dummies, and I'm more interested about the overall impact of this variable on the data, and not particularly interested in every each one of the brands.
Is there any way to group this variable as just 1 dummy Brand? What other analysis would you recommend for this type of problem?


Use something like car::Anova(fitted_model); that will estimate an overall effect (F-statistic/p-value) for each term, with Brand being treated as a single term.
@cdalitz comments that anova(fitted_model) or drop1(fitted_model, test="F") from base R will also work. I generally recommend car::Anova() because it can provide clearer results when you're working with a model with multiple terms, especially when there are interactions and correlations between terms.
