1. SAS Samples Assurant Health SAS Users 11/18/2008 Irvin Snider

2. Systematic Sample From Known Number of Observations

3. data work.subset; do pickit=1 to 395 by 15; set sashelp.shoes point=pickit; output; end; stop; run ;

4. $1,468 $181,739 $54,791 4 Madrid Men's Dress Western Europe 16 $9,845 $595,118 $175,694 12 Heidelberg Men's Casual Western Europe 15 $3,039 $226,678 $70,790 15 Seattle Boot United States 14 $10,194 $754,157 $293,313 37 Chicago Women's Dress United States 13 $361 $39,256 $8,365 22 Montevideo Sport Shoe South America 12 $939 $23,822 $11,759 8 Buenos Aires Slipper South America 11 $1,449 $110,179 $31,503 21 Manila Boot Pacific 10 $1,278 $158,688 $42,760 6 Auckland Women's Dress Pacific 9 $43 $1,836 $449 2 Al-Khobar Sport Shoe Middle East 8 $3,983 $520,271 $127,033 11 Moscow Slipper Eastern Europe 7 $1,012 $78,950 $30,157 3 Mexico City Men's Dress Central America/Caribbean 6 $20,470 $671,837 $353,361 25 Vancouver Men's Casual Canada 5 $488 $54,677 $12,601 2 Calgary Women's Dress Canada 4 $844 $66,017 $16,282 25 Nairobi Boot Africa 3 $1,565 $130,025 $39,452 12 Johannesburg Slipper Africa 2 $769 $191,821 $29,761 12 Addis Ababa Boot Africa 1 Returns Inventory Sales Stores Subsidiary Product Region Obs

5. Systematic Sample From Unknown Number of Observations

6. data work.subset; do pickit=1 to totobs by 25; set sashelp.shoes point=pickit nobs=totobs; output; end; stop; run ;

7. Random Sample with Replacement

8. data work.rsubset (drop=i sampsize); sampsize = 10; do i=1 to sampsize; pickit=ceil(ranuni(0)*totobs); set sashelp.shoes point=pickit nobs=totobs; output; end; stop; run ;

9. Random Sample without Replacement

10. data work.rsubset (drop=obsleft sampsize); sampsize=10; obsleft=totobs; do while (sampsize > 0); pickit + 1; if ranuni(0)<sampsize/obsleft then do; set sashelp.shoes point=pickit nobs=totobs; output; sampsize=sampsize- 1 ; end; obsleft=obsleft-1; end; stop; run ;

11. Permuted Blocks Randomization for Equal Allocation to Treatments A and B

15. Thank You