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The Problems of Multicollinearity, Heteroskedasticity, Outlying and Influential Cases and Non-normally Distributed Errors When using Ordinary Least Squares (OLS) Regression
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Multicollinearity, heteroskedasticity, outlying and influential cases, and non-normally distributed errors all present problems for ordinary least squares (OLS) regression. This 8 pager paper explains why each is a problem for OLS, how it can be detected and looks at steps can be taken to deal with each of them. The bibliography cites 5 sources.
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Pages:
8
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Filename:TS14_TEOSLerror.rtf |
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Paper Title:
The Problems of Multicollinearity, Heteroskedasticity, Outlying and Influential Cases and Non-normally Distributed Errors When using Ordinary Least Squares (OLS) Regression
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