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QQPLOT Statement |
The following properties of Q-Q plots and probability plots make them useful diagnostics of how well a specified theoretical distribution fits a set of measurements:
Q-Q plots are more convenient than probability plots for graphical estimation of the location and scale parameters since the x-axis of a Q-Q plot is scaled linearly. On the other hand, probability plots are more convenient for estimating percentiles or probabilities.
There are many reasons why the point pattern in a Q-Q plot may not be linear. Chambers and others (1983) and Fowlkes (1987) discuss the interpretations of commonly encountered departures from linearity, and these are summarized in the following table.
Table 10.13: Quantile-Quantile Plot DiagnosticsDescription of Point Pattern | Possible Interpretation |
All but a few points fall on a line | Outliers in the data |
Left end of pattern is below the line; right end of pattern is above the line | Long tails at both ends of the data distribution |
Left end of pattern is above the line; right end of pattern is below the line | Short tails at both ends of the data distribution |
Curved pattern with slope increasing from left to right | Data distribution is skewed to the right |
Curved pattern with slope decreasing from left to right | Data distribution is skewed to the left |
Staircase pattern (plateaus and gaps) | Data have been rounded or are discrete |
In some applications, a nonlinear pattern may be more revealing than a linear pattern. However, Chambers and others (1983) note that departures from linearity can also be due to chance variation.
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