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
Example 4.7 Saving Summary Statistics in an OUT= Output Data Set
This example illustrates how to save summary statistics in an output data set. The following statements create a data set
named Belts, which contains the breaking strengths (Strength) and widths (Width) of a sample of 50 automotive seat belts:
data Belts;
label Strength = 'Breaking Strength (lb/in)'
Width = 'Width in Inches';
input Strength Width @@;
datalines;
1243.51 3.036 1221.95 2.995 1131.67 2.983 1129.70 3.019
1198.08 3.106 1273.31 2.947 1250.24 3.018 1225.47 2.980
1126.78 2.965 1174.62 3.033 1250.79 2.941 1216.75 3.037
1285.30 2.893 1214.14 3.035 1270.24 2.957 1249.55 2.958
1166.02 3.067 1278.85 3.037 1280.74 2.984 1201.96 3.002
1101.73 2.961 1165.79 3.075 1186.19 3.058 1124.46 2.929
1213.62 2.984 1213.93 3.029 1289.59 2.956 1208.27 3.029
1247.48 3.027 1284.34 3.073 1209.09 3.004 1146.78 3.061
1224.03 2.915 1200.43 2.974 1183.42 3.033 1195.66 2.995
1258.31 2.958 1136.05 3.022 1177.44 3.090 1246.13 3.022
1183.67 3.045 1206.50 3.024 1195.69 3.005 1223.49 2.971
1147.47 2.944 1171.76 3.005 1207.28 3.065 1131.33 2.984
1215.92 3.003 1202.17 3.058
;
The following statements produce two output data sets containing summary statistics:
proc univariate data=Belts noprint;
var Strength Width;
output out=Means mean=StrengthMean WidthMean;
output out=StrengthStats mean=StrengthMean std=StrengthSD
min=StrengthMin max=StrengthMax;
run;
When you specify an OUTPUT statement, you must also specify a VAR statement. You can use multiple OUTPUT statements with a
single procedure statement. Each OUTPUT statement creates a new data set with the name specified by the OUT= option. In this
example, two data sets, Means and StrengthStats, are created. See Output 4.7.1 for a listing of Means and Output 4.7.2 for a listing of StrengthStats.
Output 4.7.1: Listing of Output Data Set Means
Output 4.7.2: Listing of Output Data Set StrengthStats
Summary statistics are saved in an output data set by specifying keyword=names after the OUT= option. In the preceding statements, the first OUTPUT statement specifies the keyword MEAN followed by the names StrengthMean and WidthMean. The second OUTPUT statement specifies the keywords MEAN, STD, MAX, and MIN, for which the names StrengthMean, StrengthSD, StrengthMax, and StrengthMin are given.
The keyword specifies the statistic to be saved in the output data set, and the names determine the names for the new variables. The first name listed after a keyword contains that statistic for the first variable listed in the VAR statement; the second name contains that statistic for the second variable in the VAR statement, and so on.
The data set Means contains the mean of Strength in a variable named StrengthMean and the mean of Width in a variable named WidthMean. The data set StrengthStats contains the mean, standard deviation, maximum value, and minimum value of Strength in the variables StrengthMean, StrengthSD, StrengthMax, and StrengthMin, respectively.
See the section OUT= Output Data Set in the OUTPUT Statement for more information about OUT= output data sets.
A sample program for this example, uniex06.sas, is available in the SAS Sample Library for Base SAS software.