Linear regression models can be used to predict expected values on the response variable given values on the predictors, and \(\epsilon\) represents the difference between a prediction based on the model and what the actual value of the response variable is. Regression is a game of averages, but for any individual observation, the model will contain some error. We will cover their interpretation in detail later. The other \(\beta\)’s are called the coefficients, and represent the relationship between each predictor and the response. ![]() If you have a subject for which every predictor is equal to zero, \(\beta_0\) represents their predicted outcome.
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