Using R to run multiple regression modeling when one of the categorical variables has several levels I am looking at a dataset that looks like this. Let's call it movies:

As you can see, the genre column has several values. So does the studio column. I am trying to determine what the multiple linear regression model is using R using these two variables and the avg_score column. How do I do this? I tried

But that spits out this data:

But that doesn't seem right? First off... if the movie is made from Studio A, that means it can't be made from Studio B. Also, if a movie is a horror movie, it's not a documentary. How do I capture that relationship using MLR and R? The output I generated seems wrong no?
For example, let's look at studio7th art. Is the way to interpret that row that it has a coefficient of 15.68 (If the studio is made by them, the avg_score on avg goes up 15.68), with a p-value of 0.44 (not significant), with a SE of 20? That data seems... worthless. Is there a way to do this that isn't idiotic?
 A: "if the movie is made from Studio A, that means it can't be made from Studio B." - That is implicit in how the variables were created. What internally happens is that R is creating dummy variables from your catgorical variable.
In any given record, only 1 of the dummy variables will have a value of 1, while all else will be 0.
"For example, let's look at studio7th art." - If the co-efficient in a regression shows a high p-value it could mean multiple different things. In your case, the biggest problem is that you have a large number of levels for studio and probably not enough observations in your dataset to characterize every level.
So rather than build a model with all studios, you could build dummy variables yourself. Something along the lines of "Studio-Category1" which includes some of the larger houses. "Studio-Category2" with smaller studios etc, which you would know from domain experience.
Subsequently build your model with these variables as inputs instead of the entire studio categories. 
