So, what is our residuals plot?

Since previously we looked at the residuals of Rhode

Island and District of Columbia, for their relationship between poverty

and high school graduation rate in the U.S., we'll stick

with those to exemplify how we build a residuals plot.

In Rhode Island, the observed high school graduation rate

is 81% and the observed rate of poverty is 10.3%.

Using our linear model, we can calculate what

the predicted of poverty would be for Rhone island.

So, for that we simply plug in 81% into our linear model.

And we see that the model predicts 14.46% for the poverty rate in Rhode island.

The difference between the observed and the predicted rates is r residual,

and that comes out to be negative four point sixteen percent.

This is basically the value that's shown in the residuals plot.

So the x-axis of the residuals plot here is once again high school graduation rate.

So if, again we have 81% for Rhode Island.

And on the y-axis we have the residuals.

And since Rhode Island has a negative residual,

the point associated with Rhode Island appears below the

zero line in the residuals plot and is

four point sixteen percent away from the zero line.

For DC, the observed high school graduation rate is

86% and the observed rate of poverty is 16.8%.

Using the same linear model and plugging

in 86%, we can actually calculate the predicted

poverty rate for DC, and we see the model predicts a poverty rate of 11.36%.

In this case, the residual can once again be calculated as the observed value minus

the predicted value, and that comes out to be positive five point forty-four percent.

And that's the same value that we're seeing on the

residuals plot, where on the x-axis we have 86% for DC.

And on the y-axis we have the associated residual.