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In an advanced treatment, the q-q plot can be used to formally test the null hypothesis that the data are normal. This is done by computing the correlation coefficient of the n points in the q-q plot. Depending upon n, the null hypothesis is rejected if the correlation coefficient is less than a threshold.

Cleveland has a lot of reasons why QQ plots are good and why histograms are not so good. Once you learn how to use them, Q-Q plots allow you to identify skewness, heavytailedness, general shape, peaks and so on, the same kinds of features people tend to use histograms to try to assess.

plots are used to assess whether data come from a particular distribution, or whether two datasets have the same parent distribution. If the distributions have the same shape (but not necessarily the same location or scale parameters), then the plot will fall roughly on a straight line.

The QQ plot, or quantile-quantile plot, is a graphical tool to help us assess if a set of data plausibly came from some theoretical distribution such as a normal or exponential.

The QQ plot, or quantile-quantile plot, is a graphical tool to help us assess if a set of data plausibly came from some theoretical distribution such as a normal or exponential.

The function plotpdf() plots a function, usually probability density (pdf) or cumulative dis- tribution function (cdf), over an interval containing the ?interesting? part of the function. The interval is based on quantiles computed from a supplied cdf or quantile function.