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The UNIVARIATE Procedure

Overview
Getting Started
Capabilities of PROC UNIVARIATE Summarizing a Data Distribution Exploring a Data Distribution Modeling a Data Distribution
Syntax
PROC UNIVARIATE Statement BY Statement CDFPLOT Statement CLASS Statement FREQ Statement HISTOGRAM Statement ID Statement INSET Statement OUTPUT Statement PPPLOT Statement PROBPLOT Statement QQPLOT Statement VAR Statement WEIGHT Statement Dictionary of Common Options
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
Examples
Computing Descriptive Statistics for Multiple Variables Calculating Modes Identifying Extreme Observations and Extreme Values Creating a Frequency Table Creating Basic Summary Plots 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

Details: UNIVARIATE Procedure

Subsections:

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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