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Because there has been no discussion in three years, the proposed merge will not be implemented. Will remove the merge-proposal banners from the two articles.
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The basic form of a linear predictor function f(i) for data point i (consisting of p explanatory variables), for i = 1, ..., n, is
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A linear model is a model with a linear predictor function. These articles discuss the same concept, but the article
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In the Basic Form section, the matrix vector definitions shouldn't have dimensions (p+1)-by-1 and 1-by-(p+1)Â ?
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then β is of size p (not p+1), x grows to size p after prepending x_{i0} = 1, and everything else works out
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Knowledge. If you would like to participate, please visit the project page, where you can join
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should the n be rewritten as p? should the final x_{ip} be x_{in}?
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f(i) = \beta_0 + \beta_1 x_{i1} + \cdots + \beta_p x_{ip},
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f(i) = \beta_0 + \beta_1 x_{i1} + \cdots + \beta_p x_{in}
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