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GENERAL GUIDELINES:
Windows in SAS
File management

The SAS Program
Program syntax
Debugging tips

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 USING SAS PROGRAMMING TO: 
   

1. Prepare the data set 
   Types of data 
   Example programs    
    
2. View the data
   SAS Procedures
  
3. Explore the data  
   Numeric statistics    
   Frequency tables    
    
4. Manipulate the data  
   Basic techniques    
   New variables
  
5. Adding Variables and 
Observations to Data Sets
   The SET Statement
   The MERGE Statement

6. Data Processing
   ARRAY Statement
   Do Loops
   By-Group Processing
   RETAIN Statement

  
NON-PROGRAMMING 
      Alternatives

 
SAMPLE DATA SETS: 
 Height/weight
 Height/weight/region
 Simulated clinical data 
 Simulated Manitoba Health 
    

PROC MEANS: Example 1 8 No keywords specified 08:54 Wednesday, June 30, 1999 The MEANS Procedure Variable N Mean Std Dev Minimum -------------------------------------------------------------- age 17 34.4705882 7.7630346 23.0000000 height 18 69.0555556 3.5225696 62.0000000 weight 18 146.7222222 22.5409576 98.0000000 -------------------------------------------------------------- Variable Maximum ------------------------ age 53.0000000 height 75.0000000 weight 176.0000000 ------------------------ PROC MEANS: Example 2 9 Use of VAR, CLASS, and TITLE keywords CLASSED by gender 08:54 Wednesday, June 30, 1999 The MEANS Procedure Analysis Variable : age N sex Obs N Mean Minimum Maximum ---------------------------------------------------------------- F 7 7 31.1 23.0 47.0 M 11 10 36.8 29.0 53.0 ---------------------------------------------------------------- Analysis Variable : age N N sex Obs Sum Miss ---------------------------------- F 7 218.0 0 M 11 368.0 1 ---------------------------------- PROC MEANS: Example 3 10 Use of VAR BY and OUT= keywords SORTED by gender 08:54 Wednesday, June 30, 1999 ---------------------------- sex=F ----------------------------- The MEANS Procedure Analysis Variable : age N N Mean Minimum Maximum Sum Miss ---------------------------------------------------------------- 7 31.1 23.0 47.0 218.0 0 ---------------------------------------------------------------- ---------------------------- sex=M ----------------------------- Analysis Variable : age N N Mean Minimum Maximum Sum Miss ---------------------------------------------------------------- 10 36.8 29.0 53.0 368.0 1 ---------------------------------------------------------------- PROC MEANS: Example 3 11 Use of VAR BY and OUT= keywords SORTED by gender A print of the OUTPUT data set 08:54 Wednesday, June 30, 1999 Obs sex _TYPE_ _FREQ_ _STAT_ age 1 F 0 7 N 7.0000 2 F 0 7 MIN 23.0000 3 F 0 7 MAX 47.0000 4 F 0 7 MEAN 31.1429 5 F 0 7 STD 7.7337 6 M 0 11 N 10.0000 7 M 0 11 MIN 29.0000 8 M 0 11 MAX 53.0000 9 M 0 11 MEAN 36.8000 10 M 0 11 STD 7.2541 PROC UNIVARIATE example 12 08:54 Wednesday, June 30, 1999 The UNIVARIATE Procedure Variable: age Moments N 17 Sum Weights 17 Mean 34.4705882 Sum Observations 586 Std Deviation 7.76303458 Variance 60.2647059 Skewness 0.95763621 Kurtosis 0.70897216 Uncorrected SS 21164 Corrected SS 964.235294 Coeff Variation 22.5207488 Std Error Mean 1.88281244 Basic Statistical Measures Location Variability Mean 34.47059 Std Deviation 7.76303 Median 32.00000 Variance 60.26471 Mode 30.00000 Range 30.00000 Interquartile Range 9.00000 NOTE: The mode displayed is the smallest of 2 modes with a count of 2. Tests for Location: Mu0=0 Test -Statistic- -----p Value------ Student's t t 18.30803 Pr > |t| <.0001 Sign M 8.5 Pr >= |M| <.0001 Signed Rank S 76.5 Pr >= |S| <.0001 Quantiles (Definition 5) Quantile Estimate 100% Max 53 99% 53 95% 53 90% 47 75% Q3 39 50% Median 32 25% Q1 30 10% 26 5% 23 1% 23 0% Min 23 PROC UNIVARIATE example 13 08:54 Wednesday, June 30, 1999 The UNIVARIATE Procedure Variable: age Extreme Observations ----Lowest---- ----Highest--- Value Obs Value Obs 23 6 39 18 26 4 41 17 28 1 42 10 29 9 47 2 30 15 53 14 Missing Values -----Percent Of----- Missing Missing Value Count All Obs Obs . 1 5.56 100.00