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
Overview: UNIVARIATE Procedure
The UNIVARIATE procedure provides the following:
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descriptive statistics based on moments (including skewness and kurtosis), quantiles or percentiles (such as the median), frequency tables, and extreme values
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histograms that optionally can be fitted with probability density curves for various distributions and with kernel density estimates
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cumulative distribution function plots (cdf plots). Optionally, these can be superimposed with probability distribution curves for various distributions.
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quantile-quantile plots (Q-Q plots), probability plots, and probability-probability plots (P-P plots). These plots facilitate the comparison of a data distribution with various theoretical distributions.
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goodness-of-fit tests for a variety of distributions including the normal
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the ability to inset summary statistics on plots
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the ability to analyze data sets with a frequency variable
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the ability to create output data sets containing summary statistics, histogram intervals, and parameters of fitted curves
You can use the PROC UNIVARIATE statement, together with the VAR statement, to compute summary statistics. See the section Getting Started: UNIVARIATE Procedure for introductory examples. In addition, you can use the following statements to request plots:
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the CDFPLOT statement for creating cdf plots
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the HISTOGRAM statement for creating histograms
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the PPPLOT statement for creating P-P plots
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the PROBPLOT statement for creating probability plots
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the QQPLOT statement for creating Q-Q plots
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the CLASS statement together with any of these plot statements for creating comparative plots
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the INSET statement with any of the plot statements for enhancing the plot with an inset table of summary statistics
The UNIVARIATE procedure produces two kinds of graphical output:
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ODS Statistical Graphics output, which is produced when ODS Graphics is enabled prior to your procedure statements
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traditional graphics, which are produced when ODS Graphics is not enabled
See the section Creating High-Resolution Graphics for more information about producing traditional graphics and ODS Graphics output.