Statistical Methods for Data Science
Residuals are the differences between the observed values and the predicted values in a regression model. They play a crucial role in evaluating how well a model fits the data, as they indicate the amount of variation that is not explained by the model. Understanding residuals helps assess the assumptions underlying regression analysis and identify potential issues with the model's fit.
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