Plot: QQ
These options are available on the QQ tab of the Graph Options dialog box.
The options described here are used to modify the existing Quantile-Quantile plot using a variety of distributions. Various options are available on this tab for specifying the parameters of the distribution that you select for your plot. It also provides options that can be used to adjust the ranks and the sample size for determining the theoretical quantiles.
- If you want to use the default values, then first select the desired distribution and then clear the Compute parameters from: check box. If you selected the Beta distribution, then Statistica will fit the standardized Beta distribution using the default values of the shape parameters (Shape 1 = 1, Shape 2 = 1). If you selected the Gamma, Lognormal, or Weibull distributions, then Statistica will fit the standardized selected distribution using the default value of the shape parameter (Shape = 1).
- If you want to supply your own values for the shape parameter(s), then you first need to clear the Compute parameters from: check box. You can then specify the desired values in the Shape1 and Shape 2 fields (for the Beta distribution), or a value in the Shape field (for the Gamma, Lognormal, or Weibull distribution).
- If you want to compute (estimate) the shape parameters, then you need to select the Compute parameters from: check box. In that case, you can also enter the threshold ("offset") value in the Threshold field as well as the scale value in the Scale field, if the selected distribution is Beta. If the selected distribution is Gamma, Lognormal, or Weibull, then you need to specify only the threshold value in the Threshold field. The shape parameter(s) will then be estimated either by using the maximum likelihood (see below) or by matching moments approximation. Note that the maximum likelihood method is not available for estimating the shape parameter of the Lognormal distribution.
Refer to the description of the respective distributions to learn more about the respective parameterizations.
Theoretical Quantile = [(i - rankadj)/(n + nadj)]
where i is the i'th ordered observation, n is the number of non-missing values and rankadj and nadj are user-defined adjustments to ensure that this quantity is greater than 0 and less than 1.