How to detect data that does not follow a bell curve?
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Hello everyone!
I have significantly large dataset and I need to detect which data does not follow a gaussian distribution or bell curve (see screenshots). I have tried using several tests such as kstest, lillietest, etc but for some reason, these tests do not work for me. I have tried using the histcounts function for visualizing the data distribution that you see in the screenshots. Any insights would be appreciated.
[N1,edges1,bin1] = histcounts(normal);
plot(N1)
[N2,edges2,bin2] = histcounts(abnormal);
plot(N2)
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/1482021/image.png)
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/1482026/image.png)
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Sam Chak
2023-9-14
Hi @Miguel
Not sure if you can try it this way to compare the R-square value. Data that follows the bell curve tends to have a high R-squared value close to 1. Data that does not follow the bell curve usually have relatively low R-squared values.
load('normal.mat');
[N1,edges1,bin1] = histcounts(normal);
x1 = 1:length(N1);
[fitobject1, gof1] = fit(x1', N1', 'gauss1')
load('abnormal.mat');
[N2,edges2,bin2] = histcounts(abnormal);
x2 = 1:length(N2);
[fitobject2, gof2] = fit(x2', N2', 'gauss1')
subplot(211)
plot(N1), grid on, title('N_{1} follows Bell curve')
subplot(212)
plot(N2), grid on, title('N_{2} does not follows Bell curve')
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