Abstract
When linear regression methods are applied to given data, useful results can be expected when the chosen model is considerably plausible, meaning that no substantial indications for inconsistencies and violation of model assumptions can be found. This concerns e.g. the choice and number of incorporated variables, the goodness of fit, the degree of collinearity, the distribution of the errors (normality, homogeneity, uncorrelatedness), the linearity of the assumed relationship, and the influence of individual observations.
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© 2003 Springer-Verlag Berlin Heidelberg
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Groß, J. (2003). Regression Diagnostics. In: Linear Regression. Lecture Notes in Statistics, vol 175. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55864-1_6
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DOI: https://doi.org/10.1007/978-3-642-55864-1_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40178-0
Online ISBN: 978-3-642-55864-1
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