How Can I Use Scipy Optimization To Find The Minimum Chi-squared For 3 Parameters And A List Of Data Points?

I have a histogram of sorted random numbers and a Gaussian overlay. The histogram represents observed values per bin (applying this base case to a much larger dataset) and the Gaus

Solution 1:

A Gaussian distribution is completely characterized by its mean and variance (or std deviation). Under the hypothesis that your data are normally distributed, the best fit will be obtained by using x-bar as the mean and s-squared as the variance. But before doing so, I'd check whether normality is plausible using, e.g., a q-q plot.