Okay, so now here my fit my linear model fit is fertility

as agriculture but now my Catholic variable.

My percent of the province that's Catholic that I've binarized.

I'm going to include it as a factor variable.

Because this variable CatholicBin is 01, remember back from the dummy variables

part of the lecture, I don't actually have to have this factor statement.

Because coding a variable of 0 versus 1 treats it as a factor.

However I like to always call things factor variables and the reason for

this is sometimes I have a variable that 012 for example for a 3 level variable.

And if I don't then call that a factor,

R is going to treat that as a continuous regressor.

It's going to say 2 is twice 1, even if 2 is just my numeric coding for

representing red hair color and 1 is for brown hair color.

Something like that,

where 2 really has nothing to do with being twice 1 in that case.

So I like to get in the habit of calling factor variables always

factor in my models just so I don't make that mistake.

Now when you print out your summary,

you would hopefully notice that there's only one coefficient.

And so you would hopefully notice that you've made that mistake.

But this is an easy way to avoid it.

I don't maybe always live up to this standard of

treating my factor variables always with the factor statement.

But I try.

Okay.

So there I fit it.

And let's first look at the coefficients.