# I want to test whether the skewness of the samples(from A:900*1 matrix) converge to 0 or not by using Montecarlo simulation.

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Chris on 30 Oct 2022
Hi,
I want to test whether the skewness of the samples(from A:900*1 matrix) converge to 0 or not by using Montecarlo simulation.
In this case, what codes do I have to revise...?
nn=100; %effective sample sizes
for n=1:1000;
~~~~~~~~~~~
ss(n)=std(r);
m(n)=mean(r);
med(n)=median(r);
nu(n)=numel(r)
skew_FP(n) = sum((r-m(n)).^3)/(length(n)-1)/ss(n).^3
end
hist(skew_FP())
xlabel('Skew_FP()')
ylabel('Frequency')

Hello Chris,
I have reviewed your code and would like to suggest some changes to improve it:
• Before the for loop, please define the skew_FP array as follows: skew_FP = zeros(1000, 1);. This will initialize the array with zeros to store the skewness values
• To generate the r array, you can use the randpermfunction. It allows you to randomly select k samples from the range of n. For example: r = randperm(n, k);
• Instead of manually calculating the skewness, you can utilize the built-in MATLAB function skewness. This function will compute the skewness of the r array directly. You can assign the result to skew_FP(n) as follows: skew_FP(n) = skewness(r);
For further details on these functions, please refer to the following documentation:
Hope this helps!

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