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The UNIVARIATE Procedure
The UNIVARIATE Procedure
Overview: UNIVARIATE Procedure
Getting Started: UNIVARIATE Procedure
Capabilities of PROC UNIVARIATE
Summarizing a Data Distribution
Exploring a Data Distribution
Modeling a Data Distribution
Syntax: UNIVARIATE Procedure
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: UNIVARIATE Procedure
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: UNIVARIATE Procedure
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
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