The UNIVARIATE Procedure 
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 
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 
measurement of the tendency of the deviations to be larger in one direction than in the other 
STD 
standard deviation 
STDMEAN 
standard error of the mean 
SUM 
sum 
SUMWGT 
sum of the weights 
USS 
uncorrected sum of squares 
VAR 
variance 
Quantile Statistics 

MEDIAN  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 ShapiroWilk statistic. Otherwise, it is the Kolmogorov statistic. 
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 
twotailed value for Student’s statistic with degrees of freedom 
SIGNRANK 
signed rank statistic 
T 
Student’s statistic to test the null hypothesis that the population mean is equal to 
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