Yes, I get your point. I will choose mean and variance in a way that overall mean and s.d. can be kept to 100 and 20 resp.
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How to generate data from normal distribution to reflect a pre aassigned score to a variable?
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I have a set of customers with some score assigned to them. Eg.-
C1 - 10
C2 - 8
C3 - 7
C4 - 3
C5 - 1
I want to simulate the spending power of customers, si from a normal distribution, si ~ N(100,20).
For 1000 iterations, the score assigned to them should reflect on their average spending power. What transformation can achieve this result? Using the example above, C1 and C5 should have the highest and lowest average spending respectively.
6 Commenti
dpb
il 24 Ago 2018
Modificato: dpb
il 24 Ago 2018
Glad to try to help...don't know that I was, much, but trying to clarify the problem description more fully may be what's needed to understand the sampling logic well enough to eventually write the simulation...
The Q? may be whether you're trying to sample for the specific client or stratum of clients of a given classification or from the overall distribution of all clients at this point? It seems the latter once you've determined the classification from the former based on the description but then it appears you need a conditional distribution function representative of those classes for that given transaction...then again, I may be totally missing the whole idea given the limited familiarity with your problem/intent. :)
Risposte (1)
dpb
il 21 Ago 2018
Adjust the mean by some function of the weight--there's no information on by how much, specifically, you might think the difference between 1 and 10 is in real spending, though, nor whether that's a linear or log weighting or any such thing. In the most trivial
s(:,i)=20*randn(1000,1) + 100+C(i);
2 Commenti
dpb
il 21 Ago 2018
"I want all the data to come from a distribution with same mean and sd."
Well, in that case, how can they possibly be any different other than by random sampling variations?
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