How to interpret residuals in a linear regression model

In my last blog post I discussed how to calculate the residuals in the Leinhardt dataset using Excel, and that post can be found here:- https://tracyrenee61.medium.com/calculate-the-residuals-of-the-leinhardt-dataset-using-excel-d097c52eede3

In this post I intend to discuss how to interpret the residuals found in a linear regression model.

Residuals are the differences between the observed values and the values predicted by the regression model. They represent the error or the deviation of the observed data from the model’s predictions.

The formula for the residual is:-

Where:-

1. Yi is the is the actual value of y at the ith observation.
2. Yhati is the value predicted by the regression model for the ith observation.
3. Ei is the difference between the observed value and the predicted value.

Residuals are important because:-

1. Residuals help in assessing how well the regression model fits the data. Smaller residuals indicate a better fit.
2. Residuals are used to check…