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
ODS Table Names
PROC UNIVARIATE assigns a name to each table that it creates. You can use these names to reference the table when you use the Output Delivery System (ODS) to select tables and create output data sets.
Table 4.40: ODS Tables Produced with the PROC UNIVARIATE Statement
|
ODS Table Name |
Description |
Option |
|---|---|---|
|
BasicIntervals |
confidence intervals for mean, standard deviation, variance |
CIBASIC |
|
BasicMeasures |
measures of location and variability |
default |
|
ExtremeObs |
extreme observations |
default |
|
ExtremeValues |
extreme values |
NEXTRVAL= |
|
Frequencies |
frequencies |
FREQ |
|
LocationCounts |
counts used for sign test and signed rank test |
LOCCOUNT |
|
MissingValues |
missing values |
default, if missing values exist |
|
Modes |
modes |
MODES |
|
Moments |
sample moments |
default |
|
Plots |
line printer plots |
PLOTS |
|
Quantiles |
quantiles |
default |
|
RobustScale |
robust measures of scale |
ROBUSTSCALE |
|
SSPlots |
line printer side-by-side box plots |
PLOTS (with BY statement) |
|
TestsForLocation |
tests for location |
default |
|
TestsForNormality |
tests for normality |
NORMALTEST |
|
TrimmedMeans |
trimmed means |
TRIMMED= |
|
WinsorizedMeans |
Winsorized means |
WINSORIZED= |
Table 4.41: ODS Tables Produced with the HISTOGRAM Statement
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ODS Table Name |
Description |
Option |
|---|---|---|
|
Bins |
histogram bins |
MIDPERCENTS secondary option |
|
FitQuantiles |
quantiles of fitted distribution |
any distribution option |
|
GoodnessOfFit |
goodness-of-fit tests for fitted distribution |
any distribution option |
|
HistogramBins |
histogram bins |
MIDPERCENTS option |
|
ParameterEstimates |
parameter estimates for fitted distribution |
any distribution option |