How do residuals, the differences between the observed and predicted values, relate to linear regression?
- They are not relevant in linear regression
- They indicate how well the model fits the data
- They measure the strength of the relationship between predictors
- They represent the sum of squared errors
Residuals in linear regression measure how well the model fits the data. Specifically, they represent the differences between the observed and predicted values. Smaller residuals indicate a better fit, while larger residuals suggest a poorer fit.
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