Errors And Residuals In Statistics
In statistics and optimization, statistical errors and residuals are two closely related and easily confused measures of the deviation of a sample from its "theoretical value". The error of a sample is the deviation of the sample from the (unobservable) true function value, while the residual of a sample is the difference between the sample and the estimated function value.
The distinction is most important in regression analysis, where it leads to the concept of studentized residuals.
Read more about Errors And Residuals In Statistics: Introduction, Regressions, Other Uses of The Word "error" in Statistics
Famous quotes containing the words errors and/or statistics:
“When people put their ballots in the boxes, they are, by that act, inoculated against the feeling that the government is not theirs. They then accept, in some measure, that its errors are their errors, its aberrations their aberrations, that any revolt will be against them. It’s a remarkably shrewed and rather conservative arrangement when one thinks of it.”
—John Kenneth Galbraith (b. 1908)
“We already have the statistics for the future: the growth percentages of pollution, overpopulation, desertification. The future is already in place.”
—Günther Grass (b. 1927)