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翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク Unit-weighted regression ： ウィキペディア英語版 Unit-weighted regression In statistics, unit-weighted regression is a simplified and robust version (Wainer & Thissen, 1976) of multiple regression analysis where only the intercept term is estimated. That is, it fits a model :$\backslash hat\; =\; \backslash hat(\backslash mathbf)\; =\; \backslash hat\; +\; \backslash sum\_i\; x\_i$ where each of the $x\_i$ are binary variables, perhaps multiplied with an arbitrary weight. Contrast this with the more common multiple regression model, where each predictor has its own estimated coefficient: :$\backslash hat\; =\; \backslash hat(\backslash mathbf)\; =\; \backslash hat\; +\; \backslash sum\_i\; \backslash hat\_i\; x\_i$ In the social sciences, unit-weighted regression is sometimes used for classification purposes, i.e. to predict a yes-no answer where indicates "no", $\backslash hat\; \backslash ge\; 0$ "yes". It is easier to interpret than multiple linear regression (known as linear discriminant analysis in the classification case). == Unit weights == Unit-weighted regression is a method of robust regression that proceeds in three steps. First, predictors for the outcome of interest are selected; ideally, there should be good empirical or theoretical reasons for the selection. Second, the predictors are converted to a standard form. Finally, the predictors are added together, and this sum is called the variate, which is used as the predictor of the outcome. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Unit-weighted regression」の詳細全文を読む スポンサード リンク 翻訳と辞書 : 翻訳のためのインターネットリソース Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.