Howtosignalprocessing
What do residuals tell us?
A residual is a measure of how well a line fits an individual data point. 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 0, the better the fit.
How do you interpret a residual plot?
If the points show no pattern, that is, the points are randomly dispersed, we can conclude that a linear model is an appropriate model. If the points show a curved pattern, such as a U-shaped pattern, we can conclude that a linear model is not appropriate and that a non-linear model might fit better.
Why is residual analysis important?
Residual analysis is a useful class of techniques for the evaluation of the goodness of a fitted model. Checking the underlying assumptions is important since most linear regression estimators require a correctly specified regression function and independent and identically distributed errors to be consistent.
