Residuals homework

The purpose of using studentized.residuals is to provide a standardization. Clearly, if a were known, then under ideal conditions (I.c., a I correct model and homogeneous variance), we have So the studentize,d residuals produce a set of statis.tics that behave in a standard way under Ideal conditions.

Build a basic understanding of what a residual is. We run into a problem in stats when we're trying to fit a line to data points in a scatter plot. The problem is this: It's hard to say for sure which line fits the data best. For example, imagine three scientists, , , and , are working with the same data set.

This vertical distance is known as a residual. For data points above the line, the residual is positive, and for data points below the line, the residual is negative. The closer a data point's residual is to , the better the fit. In this case, the line fits the point better than it fits the point .

For example, imagine three scientists, , , and , are working with the same data set. If each scientist draws a different line of fit, how do they decide which line is best? If only we had some way to measure how well each line fit each data point... Residuals to the rescue! A residual is a measure of how well a line fits an individual data point.