3.3 - Anatomy of SAS programming for ANOVA

The statistical software SAS is widely used in this course and in previous lessons we came across outputs generated through SAS programs. In this section, we begin to delve further into SAS programming with a special focus on ANOVA-related statistical procedures. STAT 480-course series is also a useful resource for additional help.

Here is the program used  to generate the summary output in Lesson 2.1:

data greenhouse;
input Fert $ Height;

The first line begins with the word ‘data’ and invokes the data step. Notice that the end of each SAS statement has a semi-colon. This is essential. In the dataset, the data to be used and its variables are named. Note that SAS assumes variables are numeric in the input statement, so if we are going to use a variable with alpha-numeric values (e.g. F1 or Control), then we have to follow the name of the variable in the input statement with a “$” sign.

A simple way to input small datasets is shown in this code, wherein we embed the data in the program. This is done with the word “datalines."

datalines;
Control      21
Control      19.5
Control      22.5
Control      21.5
Control      20.5
Control      21
F1      32
F1      30.5
F1      25
F1      27.5
F1      28
F1      28.6
F2      22.5
F2      26
F2      28
F2      27
F2      26.5
F2      25.2
F3      28
F3      27.5
F3      31
F3      29.5
F3      30
F3      29.2
;

The semicolon here ends the dataset.

SAS then produces an output of interest using “proc” statements, short for “procedure”. You only need to use the first four letters, so SAS code is full of “proc” statements to do various tasks. Here we just wanted to print the data to be sure it read it in OK.

proc print data= greenhouse;
title 'Raw Data for Greenhouse Data'; run;

Notice that the data set to be printed is specified in the proc print command. This is an important habit to develop because if not specified SAS will use the last created data set (which may be a different output data set generated as a result of the SAS procedures run up to that point).

The summary procedure can be very useful in both EDA (exploratory data analysis) and obtaining descriptive statistics such as mean, variance, minimum, maximum, etc. When using SAS procedures (including the summary procedure), categorical variables are specified in the class statement. Any variable NOT listed in the class statement is treated as a continuous variable. The target variable for which the summary will be made is specified by the var (for variable) statement.

The output statement creates an output dataset and the 'out=' part assigns a name of your choice to the output. Descriptive statistics also can be named. For example, in the output statement below, mean=mean assigns the name mean to the mean of the variable fert and stderr=se assigns the name se to the standard error. The output data sets of any SAS procedure will not be automatically printed. As illustrated in the code below, the print procedure would then have to be used to print the generated output. In the proc print command a title can be included as a means of identifying and describing the output contents.

proc summary data= greenhouse;
class fert;
var height;
output out=output1 mean=mean stderr=se;
run;
proc print data=output1;
title 'Summary Output for Greenhouse Data';
run;

The two commands “title; run;” right after will erase the title assignment. This prevents the same title to be used in every output generated thereafter, which is a default feature in SAS.

title; run;

Summary Output for Greenhouse Data