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In regression analysis, partial leverage is a measure of the contribution of the individual independent variables to the leverage of each observation. That is, if ''h''''i'' is the ''i''th element of the diagonal of the hat matrix, the partial leverage is a measure of how ''h''''i'' changes as a variable is added to the regression model. The partial leverage is computed as: : where :''j'' = index of independent variable :''i'' = index of observation :''X''''j''·() = residuals from regressing ''X''''j'' against the remaining independent variables Note that the partial leverage is the leverage of the ''i''th point in the partial regression plot for the ''j''th variable. Data points with large partial leverage for an independent variable can exert undue influence on the selection of that variable in automatic regression model building procedures. In statistics, high-leverage points are those that are outliers with respect to the independent variables. In other words, high-leverage points have no neighbouring points in space, where ''p'' is the number of independent variables in a regression model. This makes the fitted model likely to pass close to a high leverage observation. Hence high-leverage points have the potential to cause large changes in the parameter estimates when they are deleted—i.e., to be influential points. Although an influential point will typically have high leverage, a high leverage point is not necessarily an influential point. The leverage is typically defined as the diagonal of the hat matrix, which is : ==See also== * Partial residual plot * Partial regression plot * Variance inflation factor for a multi-linear fit * Scatterplot matrix 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「Partial leverage」の詳細全文を読む スポンサード リンク
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