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Many statistical analyses are based on distributional assumptions about the population from which the data have been obtained. However, distributional families can have radically different shapes depending on the value of the shape parameter. Therefore, finding a reasonable choice for the shape parameter is a necessary step in the analysis. In many analyses, finding a good distributional model for the data is the primary focus of the analysis. The probability plot correlation coefficient (PPCC) plot is a graphical technique for identifying the shape parameter for a distributional family that best describes the data set. This technique is appropriate for families, such as the Weibull, that are defined by a single shape parameter and location and scale parameters, and it is not appropriate or even possible for distributions, such as the normal, that are defined only by location and scale parameters. The technique is simply "plot the probability plot correlation coefficients for different values of the shape parameter, and choose whichever value yields the best fit". ==Definition== The PPCC plot is formed by: *Vertical axis: Probability plot correlation coefficient; *Horizontal axis: Value of shape parameter. That is, for a series of values of the shape parameter, the correlation coefficient is computed for the probability plot associated with a given value of the shape parameter. These correlation coefficients are plotted against their corresponding shape parameters. The maximum correlation coefficient corresponds to the optimal value of the shape parameter. For better precision, two iterations of the PPCC plot can be generated; the first is for finding the right neighborhood and the second is for fine tuning the estimate. The PPCC plot is used first to find a good value of the shape parameter. The probability plot is then generated to find estimates of the location and scale parameters and in addition to provide a graphical assessment of the adequacy of the distributional fit. The PPCC plot answers the following questions: #What is the best-fit member within a distributional family? #Does the best-fit member provide a good fit (in terms of generating a probability plot with a high correlation coefficient)? #Does this distributional family provide a good fit compared to other distributions? #How sensitive is the choice of the shape parameter? 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「Probability plot correlation coefficient plot」の詳細全文を読む スポンサード リンク
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