You are working with the text-only light edition of "H.Lohninger: Teach/Me Data Analysis, Springer-Verlag, Berlin-New York-Tokyo, 1999. ISBN 3-540-14743-8". Click here for further information.
| You are working with the text-only light edition of "H.Lohninger: Teach/Me Data Analysis, Springer-Verlag, Berlin-New York-Tokyo, 1999. ISBN 3-540-14743-8". Click here for further information.
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Table of Contents  Bivariate Data Regression Analysis of Residuals |
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| See also: regression, residuals, assumptions for regression, leverage effect |   |
The analysis of residuals is important for any regression model. While
numerical analysis is more profound, practice shows that numerical tests
are unsatisfactory for small samples. However, it is possible to use graphical
methods for analyzing residuals. This usually gives better results, since
the human brain is trained to recognize patterns. Thus plots of the residuals
against the independent variable usually give hints as to whether
the assumptions of a least squares regression are fulfilled. The following
slide show displays some examples of data sets which do not fulfill these
assumptions.
Last Update: 2005-Jul-16