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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