Just to clarify, there's a difference between the variance of the data and the estimated variance of the population. To get the variance of the data (just as a collection of numbers), you divide the sum square (value-mean) by N, the number of datapoints. To get the unbiased estimate of the variance of the population you divide by N-1 instead.mathman said:I am not sure about the one-sided business. However, usually to get a best fit Gaussian, compute the mean and variance of the data and use a Gaussian with those quantities.
chapter said:Hello, yes when I said one sided I meant that my data only covers the positive side of the distribution. While usually yes all you have to do is get the mean and the variance, my data already follows the shape of a Gaussian and I'm trying to find the best fit for the general case, its a little bit like trying to find the line of best fit using the least mean square error but instead its the Gaussian of best fit.
I have tried using the least mean square error approach but the differential equations of the Gaussian get a bit messy.
here is an example of the data I'm trying to get the Gaussian of best fit to
https://www.dropbox.com/s/mb6ebbdmqq7y0xm/cov_ukv_high_30_frm_112011_to_112012.jpg
The purpose of calculating a Gaussian of best fit in Matlab is to determine the best fitting Gaussian distribution for a given set of data points. This can help in analyzing and understanding the underlying patterns and trends in the data.
The Gaussian of best fit in Matlab is calculated using the fitdist() function. This function takes in the data points as input and returns the parameters of the best fitting Gaussian distribution, such as the mean and standard deviation.
Yes, the Gaussian of best fit can be visualized in Matlab by using the plot() function to plot the probability density function of the calculated distribution. This can help in visually comparing the distribution to the original data points.
No, the Gaussian of best fit is not always the most accurate representation of the data. It is based on the assumption that the data follows a normal distribution, which may not always be the case. It is important to also consider other types of distributions and evaluate the fit using statistical tests.
Yes, the Gaussian of best fit can be used for prediction in Matlab by using the calculated parameters to generate new data points that follow the same distribution. However, the accuracy of the predictions will depend on the accuracy of the fit and the assumption of a normal distribution.