The UNIVARIATE Procedure
- Overview
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Getting Started
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Syntax
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DetailsMissing ValuesRoundingDescriptive StatisticsCalculating the ModeCalculating PercentilesTests for LocationConfidence Limits for Parameters of the Normal DistributionRobust EstimatorsCreating Line Printer PlotsCreating High-Resolution GraphicsUsing the CLASS Statement to Create Comparative PlotsPositioning InsetsFormulas for Fitted Continuous DistributionsGoodness-of-Fit TestsKernel Density EstimatesConstruction of Quantile-Quantile and Probability PlotsInterpretation of Quantile-Quantile and Probability PlotsDistributions for Probability and Q-Q PlotsEstimating Shape Parameters Using Q-Q PlotsEstimating Location and Scale Parameters Using Q-Q PlotsEstimating Percentiles Using Q-Q PlotsInput Data SetsOUT= Output Data Set in the OUTPUT StatementOUTHISTOGRAM= Output Data SetOUTKERNEL= Output Data SetOUTTABLE= Output Data SetTables for Summary StatisticsODS Table NamesODS Tables for Fitted DistributionsODS GraphicsComputational Resources
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ExamplesComputing Descriptive Statistics for Multiple VariablesCalculating ModesIdentifying Extreme Observations and Extreme ValuesCreating a Frequency TableCreating Basic Summary PlotsAnalyzing a Data Set With a FREQ VariableSaving Summary Statistics in an OUT= Output Data SetSaving Percentiles in an Output Data SetComputing Confidence Limits for the Mean, Standard Deviation, and VarianceComputing Confidence Limits for Quantiles and PercentilesComputing Robust EstimatesTesting for LocationPerforming a Sign Test Using Paired DataCreating a HistogramCreating a One-Way Comparative HistogramCreating a Two-Way Comparative HistogramAdding Insets with Descriptive StatisticsBinning a HistogramAdding a Normal Curve to a HistogramAdding Fitted Normal Curves to a Comparative HistogramFitting a Beta CurveFitting Lognormal, Weibull, and Gamma CurvesComputing Kernel Density EstimatesFitting a Three-Parameter Lognormal CurveAnnotating a Folded Normal CurveCreating Lognormal Probability PlotsCreating a Histogram to Display Lognormal FitCreating a Normal Quantile PlotAdding a Distribution Reference LineInterpreting a Normal Quantile PlotEstimating Three Parameters from Lognormal Quantile PlotsEstimating Percentiles from Lognormal Quantile PlotsEstimating Parameters from Lognormal Quantile PlotsComparing Weibull Quantile PlotsCreating a Cumulative Distribution PlotCreating a P-P Plot
- References
Distributions for Probability and Q-Q Plots
You can use the PROBPLOT and QQPLOT statements to request probability and Q-Q plots that are based on the theoretical distributions summarized in Table 4.33.
Table 4.33: Distributions and Parameters
You can request these distributions with the BETA, EXPONENTIAL, GAMMA, PARETO, GUMBEL, LOGNORMAL, NORMAL, POWER, RAYLEIGH, WEIBULL, and WEIBULL2 options, respectively. If you do not specify a distribution option, a normal probability plot or a normal Q-Q plot is created.
The following sections provide details for constructing Q-Q plots that are based on these distributions. Probability plots are constructed similarly except that the horizontal axis is scaled in percentile units.
Beta Distribution
To create the plot, the observations are ordered from smallest to largest, and the ith ordered observation is plotted against the quantile , where is the inverse normalized incomplete beta function, n is the number of nonmissing observations, and and are the shape parameters of the beta distribution. In a probability plot, the horizontal axis is scaled in percentile units.
The pattern on the plot for ALPHA= and BETA= tends to be linear with intercept and slope if the data are beta distributed with the specific density function
Exponential Distribution
To create the plot, the observations are ordered from smallest to largest, and the ith ordered observation is plotted against the quantile , where n is the number of nonmissing observations. In a probability plot, the horizontal axis is scaled in percentile units.
The pattern on the plot tends to be linear with intercept and slope if the data are exponentially distributed with the specific density function
where is a threshold parameter, and is a positive scale parameter.
Gamma Distribution
To create the plot, the observations are ordered from smallest to largest, and the ith ordered observation is plotted against the quantile , where is the inverse normalized incomplete gamma function, n is the number of nonmissing observations, and is the shape parameter of the gamma distribution. In a probability plot, the horizontal axis is scaled in percentile units.
The pattern on the plot for ALPHA= tends to be linear with intercept and slope if the data are gamma distributed with the specific density function
where
Gumbel Distribution
To create the plot, the observations are ordered from smallest to largest, and the ith ordered observation is plotted against the quantile , where n is the number of nonmissing observations. In a probability plot, the horizontal axis is scaled in percentile units.
The pattern on the plot tends to be linear with intercept and slope if the data are Gumbel distributed with the specific density function
Lognormal Distribution
To create the plot, the observations are ordered from smallest to largest, and the ith ordered observation is plotted against the quantile , where is the inverse cumulative standard normal distribution, n is the number of nonmissing observations, and is the shape parameter of the lognormal distribution. In a probability plot, the horizontal axis is scaled in percentile units.
The pattern on the plot for SIGMA= tends to be linear with intercept and slope if the data are lognormally distributed with the specific density function
where
See Example 4.26 and Example 4.33.
Normal Distribution
To create the plot, the observations are ordered from smallest to largest, and the ith ordered observation is plotted against the quantile , where is the inverse cumulative standard normal distribution and n is the number of nonmissing observations. In a probability plot, the horizontal axis is scaled in percentile units.
The point pattern on the plot tends to be linear with intercept and slope if the data are normally distributed with the specific density function
Generalized Pareto Distribution
To create the plot, the observations are ordered from smallest to largest, and the ith ordered observation is plotted against the quantile () or (), where n is the number of nonmissing observations and is the shape parameter of the generalized Pareto distribution. The horizontal axis is scaled in percentile units.
The point pattern on the plot for ALPHA= tends to be linear with intercept and slope if the data are generalized Pareto distributed with the specific density function
Power Function Distribution
To create the plot, the observations are ordered from smallest to largest, and the ith ordered observation is plotted against the quantile , where is the inverse normalized incomplete beta function, n is the number of nonmissing observations, is one shape parameter of the beta distribution, and the second shape parameter, . The horizontal axis is scaled in percentile units.
The point pattern on the plot for ALPHA= tends to be linear with intercept and slope if the data are power function distributed with the specific density function
where
Rayleigh Distribution
To create the plot, the observations are ordered from smallest to largest, and the ith ordered observation is plotted against the quantile , where n is the number of nonmissing observations. The horizontal axis is scaled in percentile units.
The point pattern on the plot tends to be linear with intercept and slope if the data are Rayleigh distributed with the specific density function
where is a threshold parameter, and is a positive scale parameter.
Three-Parameter Weibull Distribution
To create the plot, the observations are ordered from smallest to largest, and the ith ordered observation is plotted against the quantile , where n is the number of nonmissing observations, and c is the Weibull distribution shape parameter. In a probability plot, the horizontal axis is scaled in percentile units.
The pattern on the plot for C=c tends to be linear with intercept and slope if the data are Weibull distributed with the specific density function
where
See Example 4.34.
Two-Parameter Weibull Distribution
To create the plot, the observations are ordered from smallest to largest, and the log of the shifted ith ordered observation , denoted by , is plotted against the quantile , where n is the number of nonmissing observations. In a probability plot, the horizontal axis is scaled in percentile units.
Unlike the three-parameter Weibull quantile, the preceding expression is free of distribution parameters. Consequently, the C= shape parameter is not mandatory with the WEIBULL2 distribution option.
The pattern on the plot for THETA= tends to be linear with intercept and slope if the data are Weibull distributed with the specific density function
where
See Example 4.34.