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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 DFFITS ： ウィキペディア英語版 DFFITS DFFITS is a diagnostic meant to show how influential a point is in a statistical regression. It was proposed in 1980. It is defined as the change ("DFFIT"), in the predicted value for a point, obtained when that point is left out of the regression, "Studentized" by dividing by the estimated standard deviation of the fit at that point: :$\backslash text\; =\; \}\; \backslash over\; s\_\; \backslash sqrt$ where $\backslash widehat$ and $\backslash widehat$ is the standard error estimated without the point in question, and $h\_$ is the leverage for the point. DFFITS is very similar to the externally Studentized residual, and is in fact equal to the latter times $\backslash sqrt)\}$. Since when the errors are Gaussian the externally Studentized residual is distributed as Student's t (with a number of degrees of freedom equal to the number of residual degrees of freedom minus one), DFFITS for a particular point will be distributed according to this same Student's t distribution multiplied by the leverage factor $\backslash sqrt)\}$ for that particular point. Thus, for low leverage points, DFFITS is expected to be small, whereas as the leverage goes to 1 the distribution of the DFFITS value widens infinitely. For a perfectly balanced experimental design (such as a factorial design or balanced partial factorial design), the leverage for each point is p/n, the number of parameters divided by the number of points. This means that the DFFITS values will be distributed (in the Gaussian case) as $\backslash sqrt\; \backslash approx\; \backslash sqrt$ times a t variate. Therefore, the authors suggest investigating those points with DFFITS greater than $2\backslash sqrt$. Although the raw values resulting from the equations are different, Cook's distance and DFFITS are conceptually identical and there is a closed-form formula to convert one value to the other (Cohen, Cohen, West & Aiken, 2003). == Development == Previously when assessing a dataset before running a linear regression, the possibility of outliers would be assessed using histograms and scatterplots. Both methods of assessing data points were subjective and there was little way of knowing how much leverage each potential outlier had on the results data. This led to a variety of quantitative measures, including DFFIT, DFBETA. 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「DFFITS」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.