The UNIVARIATE Procedure
- Overview
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Getting Started
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Syntax
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Details
Missing Values Rounding Descriptive Statistics Calculating the Mode Calculating Percentiles Tests for Location Confidence Limits for Parameters of the Normal Distribution Robust Estimators Creating Line Printer Plots Creating High-Resolution Graphics Using the CLASS Statement to Create Comparative Plots Positioning Insets Formulas for Fitted Continuous Distributions Goodness-of-Fit Tests Kernel Density Estimates Construction of Quantile-Quantile and Probability Plots Interpretation of Quantile-Quantile and Probability Plots Distributions for Probability and Q-Q Plots Estimating Shape Parameters Using Q-Q Plots Estimating Location and Scale Parameters Using Q-Q Plots Estimating Percentiles Using Q-Q Plots Input Data Sets OUT= Output Data Set in the OUTPUT Statement OUTHISTOGRAM= Output Data Set OUTKERNEL= Output Data Set OUTTABLE= Output Data Set Tables for Summary Statistics ODS Table Names ODS Tables for Fitted Distributions ODS Graphics Computational Resources
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Examples
Computing Descriptive Statistics for Multiple Variables Calculating Modes Identifying Extreme Observations and Extreme Values Creating a Frequency Table Creating Plots for Line Printer Output Analyzing a Data Set With a FREQ Variable Saving Summary Statistics in an OUT= Output Data Set Saving Percentiles in an Output Data Set Computing Confidence Limits for the Mean, Standard Deviation, and Variance Computing Confidence Limits for Quantiles and Percentiles Computing Robust Estimates Testing for Location Performing a Sign Test Using Paired Data Creating a Histogram Creating a One-Way Comparative Histogram Creating a Two-Way Comparative Histogram Adding Insets with Descriptive Statistics Binning a Histogram Adding a Normal Curve to a Histogram Adding Fitted Normal Curves to a Comparative Histogram Fitting a Beta Curve Fitting Lognormal, Weibull, and Gamma Curves Computing Kernel Density Estimates Fitting a Three-Parameter Lognormal Curve Annotating a Folded Normal Curve Creating Lognormal Probability Plots Creating a Histogram to Display Lognormal Fit Creating a Normal Quantile Plot Adding a Distribution Reference Line Interpreting a Normal Quantile Plot Estimating Three Parameters from Lognormal Quantile Plots Estimating Percentiles from Lognormal Quantile Plots Estimating Parameters from Lognormal Quantile Plots Comparing Weibull Quantile Plots Creating a Cumulative Distribution Plot Creating a P-P Plot
- References
Overview: UNIVARIATE Procedure
The UNIVARIATE procedure provides the following:
descriptive statistics based on moments (including skewness and kurtosis), quantiles or percentiles (such as the median), frequency tables, and extreme values
histograms that optionally can be fitted with probability density curves for various distributions and with kernel density estimates
cumulative distribution function plots (cdf plots). Optionally, these can be superimposed with probability distribution curves for various distributions.
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.
goodness-of-fit tests for a variety of distributions including the normal
the ability to inset summary statistics on plots
the ability to analyze data sets with a frequency variable
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:
the CDFPLOT statement for creating cdf plots
the HISTOGRAM statement for creating histograms
the PPPLOT statement for creating P-P plots
the PROBPLOT statement for creating probability plots
the QQPLOT statement for creating Q-Q plots
the CLASS statement together with any of these plot statements for creating comparative plots
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:
ODS Statistical Graphics output, which is produced when ODS Graphics is enabled prior to your procedure statements
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.