The point being in this case you can test whether or not the inclusion

of the additional set of extra terms is necessary with the Nova functions.

So I do a Nova fit 1.

Fit three and fit five.

Okay, that's what I named them.

One, three, five.

And, then you see down here what you get is a listing of the models.

Model one, model two, model three.

And then it gives you the degrees of freedom.

That's the number of data points minus the number of parameters that it had to fit.

The residual sums of squares and

then the excess degrees of freedom of going Df is the excess degrees of freedom

going from model one to model two, and then model two to model three.

So, we added two parameters in going from model one to model two.

That's why that Df is two.

And, then we added two additional parameters going from model two to

model three.

So the 2 parameters we added from going from model one to model two

is we added examination and education their two regression coefficients.

Going from model two to model three we added catholic and

infant mortality there too were crashing coefficients.

Okay so with this residual sums of squares and the degrees of

freedom you can calculate so called F statistic, and thus get a P value.

This gives you the F statistic and the P value associated with each of them.

And then here it shows that yes, the inclusion of education

examination appears to be necessary over just looking at agriculture by itself.

Then when I look at the next one, it says yes.

The inclusion of Catholic and infant mortality appears to be necessary

beyond just including examination, education, and agriculture.