of rows of your data and look at the associated hatvalues.

One hatvalue per row in your model.

And if you see some that are extremely large not

only would they potentially impact your model, but you should look at that row to

ascertain whether there was a data entry error.

And then we get to how do we measure things like influence?

Actual influence, not just the potential for influence.

Well almost all of the influence measures follow along the following line.

Take out that data point, refit the model, and

then compare to whatever aspect of the model you're thinking about

what happened between having the data point in and having the data point out.

So dffits, for every data point, sees how much the fitted value

at that X value changes depending on whether or not that point was included.

The dfbetas look specifically at the slope coefficients.

How much do the slope coefficients change?

Whether or not that particular data point is included.

So for the dffits we get one dffit per data point for the dfbetas.

We get one, we get a dfbeta for every coefficient for every data points.

So if we have a model, a linear regression model, we have two coefficients,

the intercept and then the slope term.

So our dfbetas will be a matrix of two by the number of datapoints.

Cooks.distance is just an overall change in our coefficients.