Simon Jackson discusses the concept of residuals:

The general approach behind each of the examples that we’ll cover below is to:

  1. Fit a regression model to predict variable (Y).

  2. Obtain the predicted and residual values associated with each observation on (Y).

  3. Plot the actual and predicted values of (Y) so that they are distinguishable, but connected.

  4. Use the residuals to make an aesthetic adjustment (e.g. red colour when residual in very high) to highlight points which are poorly predicted by the model.

The post is about 10% understanding what residuals are and 90% showing how to visualize them and spot major discrepancies.

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