How to retrieve the list of numbers from which the average of a normal simulation is derived?

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Angelavtc 2021-9-25

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评论： Angelavtc 2021-9-26

Hello all!

I am currently using the following code to calculate the average of 5000 numbers simulated with a normal distribution with a changing mean and std deviation, from 75 to 125 and 1 to 40, respectively.

n = 5000; %Number of Simulations, sample size 
x1 = 75:125;  %Mean simulation range 
y1 = 1:40; %Standard Deviation range 
for i = 1:length(x1) % mean
    for j = 1:length(y1) % sd
    Exp_Pw(i,j) = mean(0.5*(normrnd(x1(i),y1(j),n,1)));    
    end   
end

The problem is that I don't know precisely the 5000 numbers where the average comes from, and I would like to have that information in maybe a cell array? (if this is the name) that would allow me to use those different simulated numbers in other operations. Any idea how to do it?

Thank you very much!

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Answer 1

David Hill 2021-9-25

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在 MATLAB Online 中打开

n = 5000; %Number of Simulations, sample size 
x1 = 75:125;  %Mean simulation range 
y1 = 1:40; %Standard Deviation range 
N=zeros(n,length(y1),length(x1));
for i = 1:length(x1) % mean
    for j = 1:length(y1) % sd
        N(:,i,j)=normrnd(x1(i),y1(j),n,1);%this will contain all values
        Exp_Pw(i,j) = mean(0.5*(N(n,i,j)));    
    end   
end

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Image Analyst 2021-9-26

在 MATLAB Online 中打开

Since you can do

r = normrnd(mu,sigma)

you have r. I think the confusion was when you said you need to "retrieve" or "identify" r, when in fact you already had r.

I think what you really meant was that you wanted to save all the r into an array. And to save them (instead of just generating, using, and then throwing away the numbers inside each iteration, which would be fine by the way), then yes, you'd need a 3-D array to save them. Anyway, thanks for accepting David's answer to award him reputation points.

Angelavtc 2021-9-26

yes @Image Analyst you are right, there was a mistake in the way I wrote my question. "Save" is better than "retrieve". Thank you!

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Answer 2

Image Analyst 2021-9-25

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I'm not sure what you're asking. If all you have is a mean, say 75, then there are an infinite number of data values that could have gone into getting that mean of 75. You can't know them if all you have is the mean and standard deviation, though you could create some similar ones with randn().