The Assumptions of Simple Linear Regression
The key to creating any statistical model is to verify if the model actually explains the data. In addition to simple visual inspection, residuals provide a pathway for making a rigorous estimate of model accuracy when applying any form of regression. Read this overview of how residuals are applied.
Identifying Specific Problems Using Residual Plots
In this section, we learn how to use residuals versus fits (or predictor) plots to detect problems with our formulated regression model. Specifically, we investigate:
- how a non-linear regression function shows up on a residuals vs. fits plot
- how unequal error variances show up on a residuals vs. fits plot
- how an outlier shows up on a residuals vs. fits plot.
Note! that although we will use residuals vs. fits plots throughout our discussion here, we just as easily could use residuals vs. predictor plots (providing the predictor is the one in the model)