1. Let’s review some of the statistics you’ve learned in your first class:
Univariate analyses (single variable) are done both graphically and numerically. For categorical variables, use bar graphs and percentages. SAS does this with PROC GCHART and PROC FREQ
PROC GCHART DATA=padgett; VBAR marsh;
title ‘Vertical Bar Graph’;
PROC GCHART DATA=padgett; HBAR marsh;
title ‘Horizontal Bar Graph’;
PROC GCHART DATA=padgett; HBAR plantht;
title ‘Histogram (sort of)’;
*choose your own midpoints with the option /midpoints= a to b by … ;
PROC FREQ DATA=padgett; TABLES marsh type flower; run; quit;

2. For quantitative variables, use histograms, stem and leaf plots, boxplots (especially for comparisons - more later), quantile plots (probability plots). SAS does this with PROCs UNIVARIATE, MEANS, SUMMARY, TABULATE, REPORT, etc. Right now, we’ll concentrate on UNIVARIATE:
PROC UNIVARIATE DATA=padgett; VAR plantht totmass; title ‘Lots of Statistics…’; run; quit;
PROC UNIVARIATE DATA=padgett NORMAL PLOT; VAR plantht totmass; title ‘With the NORMAL and PLOT options’;
run; quit;
*Better histogram is done in UNIVARIATE with the HISTOGRAM statement;
PROC UNIVARIATE DATA=padgett; VAR plantht totmass;
HISTOGRAM plantht /MIDPOINTS 0 to 150 by 25 NORMAL;
INSET MEAN=‘Mean’ (7.2)
STD=‘Standard Deviation’ (7.2)/Font=‘Courier’
POS=NW HEIGHT=3; run; quit;

3. UNIVARIATE also gives very nice normal quantile and normal probability plots:
PROC UNIVARIATE DATA=padgett; VAR plantht totmass;
QQPLOT totmass; PROBPLOT plantht; run; quit;