Descriptive Statistics Using
PROC MEANS
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These SAS statistics tutorials briefly explain the use and interpretation of standard statistical analysis techniques for Medical, Pharmaceutical, Clinical Trials, Marketing or Scientific Research. The examples include howto instructions for SAS Software.
Preliminary information about PROC MEANS
PROC MEANS produces descriptive statistics (means, standard deviation, minimum,
maximum, etc.) for numeric variables in a set of data. PROC MEANS can be used for
 Describing continuous data where the average has meaning
 Describing the means across groups
 Searching for possible outliers or incorrectly coded values
 Performing a single sample ttest
The syntax of the PROC MEANS statement is:
PROC MEANS <options>; <statements>;
Statistical options that may be requested are: (default statistics are underlined.)

(New to version 8.0)

Other commonly used options available in PROC MEANS include:
 DATA= Specify data set to use
 NOPRINT Do not print output
 MAXDEC=n Use n decimal places to print output
Commonly used statements with PROC MEANS include:
 BY variable list  Statistics are reported for groups in separate tables
 CLASS variable list – Statistics reported by groups in a single table
 VAR variable list – specifies which numeric variables to use
 OUTPUT OUT = datasetname – statistics will be output to a SAS data file
 FREQ variable  specifies a variable that represents a count of observations
A few quick examples of PROC MEANS
* Simplest invocation – on all numeric variables *;
PROC MEANS;
*Specified statistics and variables *;
PROC MEANS N MEAN STD; VAR SODIUM CARBO;
* Subgroup descriptive statistics using by statement*;
PROC SORT; BY SEX;
PROC MEANS; BY SEX;
VAR FAT PROTEIN SODIUM;
* Subgroup descriptive statistics using class statement*;
PROC MEANS; CLASS SEX;
VAR FAT PROTEIN SODIUM;
Example 1: A simple use of PROC MEANS
This example calculates the means of several specified variables, limiting the output to two decimal places. (PROCMEANS1.SAS)
******************************************
* Data on weight, height, and age of a *
* random sample of 12 *
* nutritionally deficient children. *
******************************************;
DATA CHILDREN;
INPUT WEIGHT HEIGHT AGE;
DATALINES;
64 57 8
71 59 10
53 49 6
67 62 11
55 51 8
58 50 8
77 55 10
57 48 9
56 42 10
51 42 6
76 61 12
68 57 9
;
ODS RTF;
proc means;
Title 'Example 1a  PROC MEANS, simplest use';
run;
proc means maxdec=2;var WEIGHT HEIGHT;
Title 'Example 1b  PROC MEANS, limit decimals, specify variables'
run;
proc means maxdec=2 n mean stderr median;var WEIGHT HEIGHT
Title 'Example 1c – PROC MEANS, specify statistics to report'
run;
ODS RTF CLOSE;
Output for Example 1:
Example 1a  PROC MEANS, simplest use
N 
Mean 
Std Dev 
Minimum 
Maximum 

WEIGHT 
12 
62.7500000 
8.9861004 
51.0000000 
77.0000000 
Example 1b  PROC MEANS, limit decimals, specify variables
Variable 
N 
Mean 
Std Dev 
Minimum 
Maximum 
WEIGHT 
12 
62.75 
8.99 
51.00 
77.00 
Example 1c – PROC MEANS, specify statistics to report
Variable 
N 
Mean 
Std Error 
Median 
WEIGHT 
12 
62.75 
2.59 
61.00 
Example 2: Using PROC MEANS using “By Group” and Class statements
This example uses PROC MEANS to calculate means for an entire data set or by grouping variables. (PROCMEANS2.SAS)
***************************************************
* Example 2 for PROC MEANS *
***************************************************;
DATA FERTILIZER;
INPUT FEEDTYPE WEIGHTGAIN;
DATALINES;
1 46.20
1 55.60
1 53.30
1 44.80
1 55.40
1 56.00
1 48.90
2 51.30
2 52.40
2 54.60
2 52.20
2 64.30
2 55.00
;
ODS RTF;
PROC SORT DATA=FERTILIZER;BY FEEDTYPE;
PROC MEANS; VAR WEIGHTGAIN; BY FEEDTYPE;
TITLE 'Summary statistics by group';
RUN;
PROC MEANS; VAR WEIGHTGAIN; CLASS FEEDTYPE;
TITLE 'Summary statistics USING CLASS';
RUN;
ODS RTF CLOSE;
Output for this SAS code is:
Summary Statistics by Group
FEEDTYPE=1
Analysis Variable : WEIGHTGAIN 

N 
Mean 
Std Dev 
Minimum 
Maximum 
7 
51.4571429 
4.7475808 
44.8000000 
56.0000000 
FEEDTYPE=2
N 
Mean 
Std Dev 
Minimum 
Maximum 
6 
54.9666667 
4.7944412 
51.3000000 
64.3000000 
In this first version of the output the BY statement (along with the PROC SORT) creates two tables, one for each value of the BY variable. In this next example, the CLASS statement produces a single table broken down by group (FEEDTYPE.)
Summary statistics USING CLASS
Analysis Variable : WEIGHTGAIN 

FEEDTYPE 
N Obs 
N 
Mean 
Std Dev 
Minimum 
Maximum 
1 
7 
7 
51.4571429 
4.7475808 
44.8000000 
56.0000000 
2 
6 
6 
54.9666667 
4.7944412 
51.3000000 
64.3000000 
Hands on Exercise:
1. Modify the above program to output the following statistics
N MEAN MEDIAN MIN MAX
2. Use MAXDEC=2 to limit number of decimals in output
EXAMPLE 3: Using PROC MEANS to find OUTLIERS
PROC MEANS is a quick way to find large or small values in your data set that may be considered outliers (see PROC UNIVARIATE also.) This example shows the results ofusing PROC means where the MINIMUM and MAXIMUM identify unusual values inthe data set. (PROCMEANS3.SAS)
DATA WEIGHT; INPUT TREATMENT LOSS @@; DATALINES; 2 1.0 1 3.0 1 1.0 1 1.5 1 0.5 1 3.5 1 99 2 4.5 3 6.0 2 3.5 2 7.5 2 7.0 2 6.0 2 5.5 1 1.5 3 2.5 3 0.5 3 1.0 3 .5 3 78 1 .6 2 3 2 4 3 9 1 7 2 2 ; ODS RTF; PROC MEAN; VAR LOSS; TITLE 'Find largest and smallest values'; RUN; ODS RTF CLOSE;
Notice that in this output, PROC means indicates that there is a small value of 99 (could be a missing value code) and a large value of 78 (could be a miscoded number.) This is a quick way to find outliers in your data set.
Analysis Variable : LOSS 

N 
Mean 
Std Dev 
Minimum 
Maximum 
26 
2.0423077 
25.4650062 
99.0000000 
78.0000000 
Also see PROC Univariate for detecting outliers.
EXAMPLE 4: Using PROC MEANS to perform a single sample ttest (or Paired ttest)
To compare two paired groups (such as in a beforeafter situation) where both observations are taken from the same or matched subjects, you can perform a paired ttest using PROC MEANS. To do this convert the paired data into a difference variable and perform a single sample ttest. For example, suppose your data contained the variables WBEFORE and WAFTER, (before and after weight on a diet), for 8 subjects. To perform a paired ttest using PROC MEANS, follow these steps:
 Read in your data.
 Calculate the difference between the two observations (WLOSS is the amount of weight lost), and
 Report the mean loss, tstatistic and pvalue using PROC MEANS.
The hypotheses for this test are:
Ho: μLoss = 0 (The average weight loss was 0)
Ha: μLoss ≠ 0 (The weight loss was different than 0)
For example, the following code performs a paired ttest for weight loss data: (PROCMEANS4.SAS)
DATA WEIGHT;
INPUT WBEFORE WAFTER;
* Calculate WLOSS in the DATA step *;
WLOSS=WAFTERWBEFORE;
DATALINES;
200 190
175 154
188 176
198 193
197 198
310 240
245 204
202 178
;
ODS RTF;
PROC MEANS N MEAN T PRT; VAR WLOSS;
TITLE 'Paired ttest example using PROC MEANS';
RUN;
ODS RTF CLOSE;
Notice that the actual test is performed on the new variable called WLOSS, and that is why it is the only variable requested in the PROC MEANS statement. This is essentially a onesample ttest. The statistics of interest are the mean of WLOSS, the tstatistic associated with the null hypothesis for WLOSS and the pvalue. The SAS output is as follows:
Paired ttest example using PROC MEANS
Analysis Variable : WLOSS 

N 
Mean 
t Value 
Pr > t 
8 
22.7500000 
2.79 
0.0270 
The mean of the variable WLOSS is –22.75. The tstatistic associated with the null hypothesis is –2.79, and the pvalue for this paired ttest is p = 0.027, which provides evidence to reject the null hypothesis.
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