PROC MEANS and PROC UNIVARIATE

Sample statistics for a single variable across all observations are simple to obtain using, for example, PROC MEANS, PROC UNIVARIATE, etc. The simplest method to obtain similar statistics across several variables within an observation is with a 'sample statistics function'. 

For example:
sum_wt=sum(of weight1 weight2 weight3 weight4 weight5);
which is equivalent to
sum_wt=sum(of weight1-weight5);
but is not equivalent to
sum_wt=weight1 + weight2 + weight3 + weight4 + weight5;
because the SUM function returns the sum of non-missing arguments, whereas the '+' operator returns a missing value if any of the arguments are missing.

The following are all valid arguments for the SUM function:
sum(of variable1-variablen) where n is an integer greater than 1
sum(of x y z)
sum(of array-name{*})
sum(of _numeric_)
sum(of x--a) where x precedes a in the PDV order.

Other useful sample statistic functions are:
MAX(argument,...) returns the largest value
MIN(argument,...) returns the smallest value
MEAN(argument,...) returns the arithmetic mean (average)
N(argument,....) returns the number of nonmissing arguments
NMISS(argument,...) returns the number of missing values,
e.g., if nmiss(of test1-test20) le 2 then testmean=mean(of test1-test20); else testmean=.;
STD(argument,...) returns the standard deviation
STDERR(argument,...) returns the standard error of the mean
VAR(argument,...) returns the variance