how to perform a chi-square goodness of fit test in matlab

To perform a chi-square goodness of fit test in MATLAB, follow these steps:

1. Define the observed data vector, observed_data, representing the frequency of each category.

main.m
observed_data = [10, 20, 30, 25, 15];
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2. Define the expected data vector, expected_data, representing the expected frequency of each category under the null hypothesis.

main.m
expected_data = [10, 20, 30, 25, 15];
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Note: The expected data vector may differ if you have a specific null hypothesis or expected proportion for each category.

3. Calculate the chi-square test statistic, chi_square, using the chi2gof function, specifying the 'ctrs' argument to provide the centers of the bins, and the 'Expected' parameter to provide the expected frequency of each category.

main.m
[chi_square, p_value] = chi2gof(1:length(observed_data), 'Ctrs', 1:length(observed_data), 'Frequency', observed_data, 'Expected', expected_data);
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4. Check the p-value, p_value, to determine the significance of the test.

main.m
if p_value < 0.05
    disp('Reject null hypothesis');
else
    disp('Fail to reject null hypothesis');
end
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Here is the complete code for performing a chi-square goodness of fit test in MATLAB:

main.m
observed_data = [10, 20, 30, 25, 15];
expected_data = [10, 20, 30, 25, 15];
[chi_square, p_value] = chi2gof(1:length(observed_data), 'Ctrs', 1:length(observed_data), 'Frequency', observed_data, 'Expected', expected_data);

if p_value < 0.05
    disp('Reject null hypothesis');
else
    disp('Fail to reject null hypothesis');
end
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