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- W2012103190 abstract "The n-dimensional geometry of collinearity and data that are influential in least-squares linear regression is explored. A generalization of vector space dimensionality is introduced to provide an intuitive description of these problems. It is also noted that this new measure of dimensionality plays the role of the usual dimension in a James-Stein like result. Some common regression diagnostics are critically examined in this geometric framework." @default.
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- W2012103190 title "On the n-dimensional geometry of regression diagnostics" @default.
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- W2012103190 doi "https://doi.org/10.1080/03610928708829521" @default.
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