Probability Plot Correlation Coefficient Plot
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".
Read more about Probability Plot Correlation Coefficient Plot: Definition, Comparing Distributions, Tukey-lambda PPCC Plot For Symmetric Distributions, See Also, External Links
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