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Descriptive Statistics Using

PROC MEANS

We are here to help you... use this series of class-tested tutorials to learn about SAS.

See www.stattutorials.com/SASDATA for files mentioned in this tutorial © TexaSoft, 2007-13

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 how-to 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

 The syntax of the PROC MEANS statement is:

PROC MEANS <options>; <statements>;

Statistical options that may be requested are: (default statistics are underlined.)

  • N - Number of observations
  • NMISS - Number of missing observations
  • MEAN - Arithmetic average)
  • STD -  Standard Deviation
  • MIN -  Minimum (smallest)
  • MAX -  Maximum (largest)
  • RANGE - Range
  • SUM -  Sum of observations
  • VAR -  Variance    
  • USS – Uncorr. sum of squares
  • CSS -  Corr. sum of squares
  • STDERR - Standard Error
  • T - Student’s t value for testing Ho: md = 0
  • PRT - P-value associated with t-test above
  • SUMWGT - Sum of the WEIGHT variable values    

(New to version 8.0)

  • MEDIAN – 50th percentile
  • P1 – 1st percentile
  • P5 - 5th percentile
  • P10 – 10th percentile
  • P90 - 90th percentile
  • P95 – 95th percentile
  • P99 - 99th percentile
  • Q1 - 1st quartile
  • Q3 - 3rd quartile
  • QRANGE – Quartile range

Other commonly used options available in PROC MEANS include:

Commonly used statements with PROC MEANS include:

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

Variable

N

Mean

Std Dev

Minimum

Maximum

WEIGHT
HEIGHT
AGE

12
12
12

62.7500000
52.7500000
8.9166667

8.9861004
6.8240884
1.8319554

51.0000000
42.0000000
6.0000000

77.0000000
62.0000000
12.0000000

Example 1b - PROC MEANS, limit decimals, specify variables 

Variable

N

Mean

Std Dev

Minimum

Maximum

WEIGHT
HEIGHT

12
12

62.75
52.75

8.99
6.82

51.00
42.00

77.00
62.00

Example 1c – PROC MEANS, specify statistics to report 

Variable

N

Mean

Std Error

Median

WEIGHT
HEIGHT

12
12

62.75
52.75

2.59
1.97

61.00
53.00

 

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

Analysis Variable : WEIGHTGAIN

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 t-test (or Paired t-test)

To compare two paired groups (such as in a before-after situation) where both observations are taken from the same or matched subjects, you can perform a paired t-test using PROC MEANS. To do this convert the paired data into a difference variable and perform a single sample t-test. 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 t-test using PROC MEANS, follow these steps:

  1. Read in your data.
  2. Calculate the difference between the two observations (WLOSS is the amount of weight lost), and
  3. Report the mean loss, t-statistic and p-value 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 t-test for weight loss data: (PROCMEANS4.SAS)

DATA WEIGHT;
       INPUT WBEFORE WAFTER;
       * Calculate WLOSS in the DATA step *;
       WLOSS=WAFTER-WBEFORE;
       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 t-test 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 one-sample t-test. The statistics of interest are the mean of WLOSS, the t-statistic associated with the null hypothesis for WLOSS and the p-value. The SAS output is as follows:

Paired t-test 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 t-statistic associated with the null hypothesis is –2.79, and the p-value for this paired t-test is p = 0.027, which provides evidence to reject the null hypothesis.

 

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