Dear All
 
I know that you do not have to help me (as this is not a pure R problem) but please do, i am new to R as a CPI compiler, i just need to do a sample to see which sampling method best works in different situations, therefore since this is for practice purposes nobody will finance a real project thats why i need you to help me direct me as to how simulate data (just direct me,not 100% help). See my attachment for problem formulation, you can even suggest a different problem and how i can simulate it.
 
Problem Formulation: 
  
If I want to measure customer satisfaction on say 5000 business outlets that I supply with soft drink. The rating is on a 10 scale point where 1 is lowest and 10 highest. The outlets range from 1 to 5 (1= big supermarkets, 2= medium, 3= Small,  4 = Mini markets, 5 = corner shops).  
  
Data: I have to simulate this sort of data for example 
  





Outlet type 


Population N

Average Buying Power (L/M)

Combined Average Buying

Proportions of total buying


1

50

31000

1550000

19.9100835


2

200

13500

2700000

34.6820809


3

350

4500

1575000

20.2312139


4

1000

600

600000

7.70712909


5

3400

400

1360000

17.4694926


Total

5000

50000

7785000

100


 

 

 

 

 


 

 

L/M =litres per month

 

 
  
This means I have to simulate 50 outlets of type 1 who buy  between say 20000 and 40000 L/M, 200 outlets of type two buying 12000 – 19999 L/M,… etc. Also I have to simulate ratings randomly from 1 to 10. 
  
I really do not know how to simulate data, after simulating I am going to use dollar stratification to sample this data to get info. I want to compare different sampling techniques to see which one is best. 
  
My objective is to sample from this data in such a way that my company will benefit from this survey. If I use SRS my survey results may show that customers are satisfied with average rating of 8, but this sample may not include my most valued customers who buy 19 to 54 percent of my stock 
 
Best Regards
R novice


      
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