What is the difference between error term and residuals in modeling?
An error term exists in the model. It is a mathematical construct.
The residual is the actual value of one data point (deviation from the value predicted by the model).
I understood residuals now.
How does an error term exists in the model? May I request you to give an simple example please.
A simple regression model looks like this
y = b0 + b1*X + e
e is the error term, b0 is the intercept and b1 is the slope
Thanks.
Where that 'e' comes from?
The residual, , is the difference between the value of the dependent variable predicted by the model, , and the true value of the dependent variable, .
I would recommend reading some good introductory regression and linear modeling texts. You may find this one to be helpful:
Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis, by Frank Harrell, Jr.
Steve Denham
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