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Probability plot correlation coefficient plot
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<p>The probability plot correlation coefficient (PPCC) plot is a <a href="/facts/Graphical_technique/SAo9EjFx">graphical technique</a> for identifying the shape parameter for a distributional family that best describes the data set. This technique is appropriate for families, such as the <a href="/facts/Weibull_distribution/1fEWxbKl">Weibull</a>, that are defined by a single shape parameter and <a href="/facts/Location_parameter/UQTyS3JU">location</a> and <a href="/facts/Scale_parameter/6lxcyKUf">scale parameters</a>, and it is not appropriate or even possible for distributions, such as the <a href="/facts/Normal_distribution/UapjjPyQ">normal</a>, that are defined only by location and scale parameters. </p><p>Many <a href="/facts/Statistical_analysis/sJLjubxm">statistical analyses</a> are based on distributional assumptions about the <a href="/facts/Population_(statistics)/KAILs8E3">population</a> from which the data have been obtained. However, distributional families can have radically different shapes depending on the value of the <a href="/facts/Shape_parameter/7jaG8RDp">shape parameter</a>. 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. </p><p>The technique is simply "plot the <a href="/facts/Probability_plot_correlation_coefficient/h2jKBjYm">probability plot correlation coefficients</a> for different values of the shape parameter, and choose whichever value yields the best fit". </p>