“Why You Need to Check Your Residual Plots for Regression Analysis” by Minitab Blog Editor

Original article: https://blog.minitab.com/en/adventures-in-statistics-2/why-you-need-to-check-your-residual-plots-for-regression-analysis

Why? To start, let’s breakdown and define the 2 basic components of a valid regression model:

Response = (Constant + Predictors) + Error 

Another way we can say this is:

Response = Deterministic + Stochastic

The take-home message to me is that the residual represents the unpredictable error. By checking the residual plot, you can validate whether your predictors are missing some of the predictive information.

Residual plots can reveal unwanted residual patterns that indicate biased results more effectively than numbers.

The residuals should be centered on zero throughout the range of fitted values and normally distributed.

Now let’s look at a problematic residual plot. Keep in mind that the residuals should not contain any predictive information.

Reading more from minitab:

Regression Analysis Tutorial and Examples

Regression Analysis: How Do I Interpret R-squared and Assess the Goodness-of-Fit?