00:01
Working with some basic linear regression concepts, let's define what a residual is.
00:07
So let's remember that when we have y hat, that this is our predicted value, and when we have just y, that's our actual value.
00:18
So we want to determine what exactly the difference is between these two, and so our residual is really just going to be our actual value of y, minus our predicted value of y.
00:28
And that will give us that residual.
00:30
That's going to be that whatever is left over between the two, the error in our estimate, essentially is what the residual is.
00:40
Now, working, continuing with the concept of a residual, we want to determine when exactly is our regression equation line, the best fit for all of our points on a scatter plot.
00:49
Let's take a look at the graph over here.
00:51
I think it's best depicted visually.
00:53
You can see we have just a basic x, y, coordinate graph of our first quadrant.
00:57
The yellow line here, let's suppose that that represents our regression equation.
01:01
How about? and let's just start plotting some points just to give you an idea.
01:06
So let's say that we have points that look something like this...