So the next thing that we can do is fit a linear model with multiple variables in it

and so the idea here is, again, we're just

fitting lines, but now we're fitting more than one line.

And so the idea is, we have some intercept terms, so that's

just the baseline level of, wage that we might have, and then

we might have a, a relationship with the age of the person,

and then we might have relationship with what job class you're in.

So one way that we typically do that is, by fitting an indicator variable.

So an indicator variable is a variable

that's denoted like this in mathematical notation.

It just says, if the job class for the ith

person is equal to information, this variable's equal to one.

If the job class for the ith person is not equal to information, then

this information is equal to zero, and so this represents the difference in the

wages between the people with job class

equal to information versus job class equal

to not information, when you, fix all the other variables in the regression model.

You can also do this for education it's a little

bit more complicated road because there are multiple education levels.

So we create an indicator variable for, each of the different education

levels and so, here this is the sum of four indicator variables.

And so the, the variable's equal to one, if the education

for person I is equal to level K, that variables equal to one and zero otherwise.