Statistical models attempt to predict the value of one or more outcome variables based on one or more predictor variables. However, these estimates are rarely the actual values of the outcome variables. The error term in a model (sometimes known as the error of prediction or the disturbance), often denoted in equations with the Greek letter epsilon (ε), expresses the difference between the actual outcome variables and the outcome variables predicted by the statistical model. This entry introduces error terms and discusses the assumptions underpinning error terms in different statistical models.
With simple linear regression, a regression model can be expressed as follows:
y = b0 + bx + ε.
[Page 426]In this equation, y is the dependent variable, ...
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