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
| OUT= Output Data Set in the OUTPUT Statement |
PROC UNIVARIATE creates an OUT= data set for each OUTPUT statement. This data set contains an observation for each combination of levels of the variables in the BY statement, or a single observation if you do not specify a BY statement. Thus the number of observations in the new data set corresponds to the number of groups for which statistics are calculated. Without a BY statement, the procedure computes statistics and percentiles by using all the observations in the input data set. With a BY statement, the procedure computes statistics and percentiles by using the observations within each BY group.
The variables in the OUT= data set are as follows:
BY statement variables. The values of these variables match the values in the corresponding BY group in the DATA= data set and indicate which BY group each observation summarizes.
variables created by selecting statistics in the OUTPUT statement. The statistics are computed using all the nonmissing data, or they are computed for each BY group if you use a BY statement.
variables created by requesting new percentiles with the PCTLPTS= option. The names of these new variables depend on the values of the PCTLPRE= and PCTLNAME= options.
If the output data set contains a percentile variable or a quartile variable, the percentile definition assigned with the PCTLDEF= option in the PROC UNIVARIATE statement is recorded in the output data set label. See Example 4.8.
The following table lists variables available in the OUT= data set.
Variable Name |
Description |
|---|---|
Descriptive Statistics |
|
CSS |
sum of squares corrected for the mean |
CV |
percent coefficient of variation |
KURTOSIS | KURT |
measurement of the heaviness of tails |
MAX |
largest (maximum) value |
MEAN |
arithmetic mean |
MIN |
smallest (minimum) value |
MODE |
most frequent value (if not unique, the smallest mode) |
N |
number of observations on which calculations are based |
NMISS |
number of missing observations |
NOBS |
total number of observations |
RANGE |
difference between the maximum and minimum values |
SKEWNESS | SKEW |
measurement of the tendency of the deviations to be larger in one direction than in the other |
STD | STDDEV |
standard deviation |
STDMEAN | STDERR |
standard error of the mean |
SUM |
sum |
SUMWGT |
sum of the weights |
USS |
uncorrected sum of squares |
VAR |
variance |
Quantile Statistics |
|
MEDIAN | Q2 | P50 |
middle value (50th percentile) |
P1 |
1st percentile |
P5 |
5th percentile |
P10 |
10th percentile |
P90 |
90th percentile |
P95 |
95th percentile |
P99 |
99th percentile |
Q1 | P25 |
lower quartile (25th percentile) |
Q3 | P75 |
upper quartile (75th percentile) |
QRANGE |
difference between the upper and lower quartiles (also known as the inner quartile range) |
Robust Statistics |
|
GINI |
Gini’s mean difference |
MAD |
median absolute difference |
QN |
2nd variation of median absolute difference |
SN |
1st variation of median absolute difference |
STD_GINI |
standard deviation for Gini’s mean difference |
STD_MAD |
standard deviation for median absolute difference |
STD_QN |
standard deviation for the second variation of the median absolute difference |
STD_QRANGE |
estimate of the standard deviation, based on interquartile range |
STD_SN |
standard deviation for the first variation of the median absolute difference |
Hypothesis Test Statistics |
|
MSIGN |
sign statistic |
NORMAL |
test statistic for normality. If the sample size is less than or equal to 2000, this is the Shapiro-Wilk |
PROBM |
probability of a greater absolute value for the sign statistic |
PROBN |
probability that the data came from a normal distribution |
PROBS |
probability of a greater absolute value for the signed rank statistic |
PROBT |
two-tailed |
SIGNRANK |
signed rank statistic |
T |
Student’s |
statistic. Otherwise, it is the Kolmogorov 
statistic. 
-value for Student’s 
statistic with 
degrees of freedom 