Basic Analysis and Graphing - SAS

Chapter 2 Performing Univariate Analysis 57
Fit Distributions
• The Gamma distribution is bound by zero and has a flexible shape.
• The Beta distribution is useful for modeling the behavior of random variables that are constrained to fall
in the interval 0,1. For example, proportions always fall between 0 and 1.
• The Normal Mixtures distribution fits a mixture of normal distributions. This flexible distribution is
capable of fitting multi-modal data. You can also fit two or more distributions by selecting the Normal 2
Mixture, Normal 3 Mixture, or Other options.
• The Smooth Curve distribution... A smooth curve is fit using nonparametric density estimation (kernel
density estimation). The smooth curve is overlaid on the histogram and a slider appears beneath the
plot. Control the amount of smoothing by changing the kernel standard deviation with the slider. The
initial Kernel Std estimate is formed by summing the normal densities of the kernel standard deviation
located at each data point.
• The Johnson Su, Johnson Sb, and Johnson Sl Distributions are useful for its data-fitting capabilities
because it supports every possible combination of skewness and kurtosis.
• The Generalized Log (Glog) distribution is useful for fitting data that are rarely normally distributed
and often have non-constant variance, like biological assay data.
Comparing All Distributions
The All option fits all applicable continuous distributions to a variable. The Compare Distributions report
contains statistics about each fitted distribution. Use the check boxes to show or hide a fit report and overlay
curve for the selected distribution. By default, the best fit distribution is selected.
The Show Distribution list is sorted by AICc in ascending order.
If your data has negative values, the Show Distribution list does not include those distributions that require
data with positive values. If your data has non-integer values, the list of distributions does not include
discrete distributions. Distributions with threshold parameters, like Beta and Johnson Sb, are not included
in the list of possible distributions.
Discrete Fit
Related Information
• “Statistical Details for Continuous Fit Distributions” on page 76
• “Statistical Details for Fitted Quantiles” on page 84
• “Statistical Details for Fit Distribution Options” on page 84
Use the Discrete Fit options to fit a distribution (such as Poisson or Binomial) to a discrete variable. The
available distributions are as follows:
• Poisson
• Binomial
• Gamma Poisson
• Beta Binomial