Errors and residuals

In statistics and optimization, errors and residuals are two closely related and easily confused measures of the deviation of an observed value of an element of a statistical sample from its "true value" (not necessarily observable). The error of an observation is the deviation of the observed value from the true value of a quantity of interest (for example, a population mean). The residual is the difference between the observed value and the estimated value of the quantity of interest (for example, a sample mean). The distinction is most important in regression analysis, where the concepts are sometimes called the regression errors and regression residuals and where they lead to the concept of studentized residuals. In econometrics, "errors" are also called disturbances.^[1]^[2]^[3]

^ Kennedy, P. (2008). A Guide to Econometrics. Wiley. p. 576. ISBN 978-1-4051-8257-7. Retrieved 2022-05-13.
^ Wooldridge, J.M. (2019). Introductory Econometrics: A Modern Approach. Cengage Learning. p. 57. ISBN 978-1-337-67133-0. Retrieved 2022-05-13.
^ Das, P. (2019). Econometrics in Theory and Practice: Analysis of Cross Section, Time Series and Panel Data with Stata 15.1. Springer Singapore. p. 7. ISBN 978-981-329-019-8. Retrieved 2022-05-13.

[Kennedy_2008_p._576-1] Kennedy, P. (2008). A Guide to Econometrics. Wiley. p. 576. ISBN 978-1-4051-8257-7. Retrieved 2022-05-13.

[Wooldridge_2019_p._57-2] Wooldridge, J.M. (2019). Introductory Econometrics: A Modern Approach. Cengage Learning. p. 57. ISBN 978-1-337-67133-0. Retrieved 2022-05-13.

[Das_2019_p._7-3] Das, P. (2019). Econometrics in Theory and Practice: Analysis of Cross Section, Time Series and Panel Data with Stata 15.1. Springer Singapore. p. 7. ISBN 978-981-329-019-8. Retrieved 2022-05-13.

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