I want to use the chi2gof to test if two distributions come from a common distribution (null hypothesis) or if they do not come from a common distribution (alternative hypothesis). I have binned observational data (x), binned model data (y), and the bin edges (bins). Both the observational and model data are counts per bin.
x= [41 22 11 10 9 5 2 3 2]
y= [38.052 24.2655 15.4665 9.8595 6.2895 4.011 2.562 1.6275 2.8665]
bins=[0:9:81]
Because the data is already binned and because I’m testing x against y, I used the following code

[h,p,stat]=chi2gof(x,’Edges’,bins,’Expected’,y)
Manual calculation of the chi2 test statistic results in 4.6861 with a probablity of p=.7905. The above function however, produces a very different result. The resulting stats show different bin edges than designated, the ovserved counts per bin do not match x, the chi2 test statistic is ~87, and p<0.001. Could someone please explain why I’m getting such dramatically different results?I want to use the chi2gof to test if two distributions come from a common distribution (null hypothesis) or if they do not come from a common distribution (alternative hypothesis). I have binned observational data (x), binned model data (y), and the bin edges (bins). Both the observational and model data are counts per bin.
x= [41 22 11 10 9 5 2 3 2]
y= [38.052 24.2655 15.4665 9.8595 6.2895 4.011 2.562 1.6275 2.8665]
bins=[0:9:81]
Because the data is already binned and because I’m testing x against y, I used the following code

[h,p,stat]=chi2gof(x,’Edges’,bins,’Expected’,y)
Manual calculation of the chi2 test statistic results in 4.6861 with a probablity of p=.7905. The above function however, produces a very different result. The resulting stats show different bin edges than designated, the ovserved counts per bin do not match x, the chi2 test statistic is ~87, and p<0.001. Could someone please explain why I’m getting such dramatically different results? I want to use the chi2gof to test if two distributions come from a common distribution (null hypothesis) or if they do not come from a common distribution (alternative hypothesis). I have binned observational data (x), binned model data (y), and the bin edges (bins). Both the observational and model data are counts per bin.
x= [41 22 11 10 9 5 2 3 2]
y= [38.052 24.2655 15.4665 9.8595 6.2895 4.011 2.562 1.6275 2.8665]
bins=[0:9:81]
Because the data is already binned and because I’m testing x against y, I used the following code

[h,p,stat]=chi2gof(x,’Edges’,bins,’Expected’,y)
Manual calculation of the chi2 test statistic results in 4.6861 with a probablity of p=.7905. The above function however, produces a very different result. The resulting stats show different bin edges than designated, the ovserved counts per bin do not match x, the chi2 test statistic is ~87, and p<0.001. Could someone please explain why I’m getting such dramatically different results? chi2gof MATLAB Answers — New Questions

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