fit the normal distribution to data in matlab

To fit the normal distribution to data in MATLAB, you can use the normfit function from the Statistics and Machine Learning Toolbox. This function estimates the parameters of the normal distribution that best fit a sample of data.

Here's an example of how to use normfit:

main.m
% Generate sample data
data = normrnd(10, 2, 1000, 1);

% Fit normal distribution to data
[mu, sigma] = normfit(data);

% Plot histogram of data with fitted normal distribution
histogram(data, 'Normalization', 'pdf')
hold on
x = linspace(min(data), max(data), 1000);
y = normpdf(x, mu, sigma);
plot(x, y, 'LineWidth', 2)
hold off
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In this example, we generate a sample of 1000 data points from a normal distribution with mean 10 and standard deviation 2 using the normrnd function. We then use normfit to estimate the mean (mu) and standard deviation (sigma) of the normal distribution that best fits the data. Finally, we plot a histogram of the data with the fitted normal distribution curve using histogram and normpdf functions.