The Q-Q plot is specifically designed to test normality or

not to test to evaluate normality of the error terms, okay?

This scale location plot is plotting the standardized residuals, remember we

talked about standardized residuals they're the ordinary residuals but

standardize, so they have a more kind of comparable scale across subject,

across experiments and the scale to try and make them to be like a TY statistic.

So again, this is a lot like the residual plot, you're applying them against

the fitted values but, now you just mostly, you've change the scale.

So that's potentially useful for looking at these across different experiments.

The final is plot of the Residuals vs Leverage.

So here's the standardized Residuals on this scale and

then here's the Leverage on that scale and in this plot

again you're trying to look at any sort of systematic pattern any reason why points

with higher leverage are having higher or particularly small residual values.

If you had an instance like where you have plots like this and

you have one very high leverage point and you get something like that.

That point might have a very small residual but

unnecessarily while it has happened to have very large leverage or

you might have an instance like this where even though it's really impacted

the regression line, it still has high leverage and also has a high residual.

So at any rate these are many, these are just a couple of examples of the kind of

plots you might want to look at in this data set none of them seem

to look too inherently bad but when you go through these things ideally you

would have something where you could click on the individual points and it would It

would describe the aspects of the point when you hover over it with your mouse.

Some of the other software systems can do that.

R can do that now, we'll talk in the data products

class how you can actually create those kinds of plots.