# Error and Residual in Regression

#### Error and Residual in Regression

In Statistics and Optimization, Statistical Errors and Residuals are two closely related and easily confused measures of “Deviation of a sample from the mean”.

Error is a misnomer; an error is the amount by which an observation differs from its expected value. The errors e are unobservable random variables, assumed to have zero mean and uncorrelated elements each with common variance  σ2.

A Residual, on the other hand, is an observable estimate of the unobservable error. The residuals $\hat{e}$ are computed quantities with mean ${E(\hat{e})=0}$ and variance ${V(\hat{e})=\sigma^2 (1-H)}$.

Like the errors, each of the residuals has zero mean, but each residual may have a different variance. Unlike the errors, the residuals are correlated. The residuals are linear combinations of the errors. If the errors are normally distributed so are the errors.

Note that the sum of the residuals is necessarily zero, and thus the residuals are necessarily not independent. The sum of the errors need not be zero; the errors are independent random variables if the individuals are chosen from the population independently.

The differences between errors and residuals in Regression are:

Residuals are used in various ways to evaluate the regression model, including:

• Residual plots: The residual plots are used to visualize the residuals versus the independent variable or predicted values.
• Mean Squared Error (MSE): The MSE statistic measures the average squared difference between the residuals and zero.

In essence, understanding errors and residuals helps the researcher gauge how well the regression model captures the underlying relationship between variables, despite the inherent randomness or “noise” in real-world data.

Learn about Simple Linear Regression Models

Statistical Models in R Language

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