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There are many other options and the function comes with a very detailed help. For example, to create two separate histograms with a greenish color and same number of bins, nhist(A, 'color', 'separate', 'samebins', 'maxbins',50) This function comes with a wealth of options for controlling everything from line properties and graph orientations to histogram properties and statistics displayed (mode, median, standard error, etc.). Of course, this is just the default behavior. Note that it automatically uses the field names for the legend. (Release 2013b) Revised for Version 9.0 (Release 2014a) Revised for Version 9.1 (Release 2014b) Revised. Mu_is_Two: 'mu_is_Two: mean=2.00, std=1.00, 1 points counted in the r.' MATLAB Econometrics Toolbox User's Guide R2020a ed. Mu_is_Zero: 'mu_is_Zero: mean=0.00, std=1.00, 3 points counted in the.
![matlab 2014a histogram matlab 2014a histogram](https://www.researchgate.net/profile/Lidong-Huang-2/publication/278742532/figure/fig2/AS:844325353181195@1578314259662/The-normalized-traditional-and-advanced-gradient-histograms-of-cameraman-image-a-The.png)
Jonathan's nhist lets you compare the histograms of the data sets easily. You can also use boxplot, also from Statistics and Machine Learning Toobox, to create a box and whisker plot that lets you visualize statistical information. Legend(fieldnames(A), 'interpreter', 'none') A.mu_is_Zero = randn(10^5,1) % mean of 0Ī.mu_is_Two = randn(10^5,1)+2 % mean of 2 % This assumes you know the distributionĭist1 = fitdist(A.mu_is_Zero, 'Normal') % fit to a normal distributionĭist2 = fitdist(A.mu_is_Two, 'Normal') % fit to a normal distribution One is to fit each data set to a particular distribution using the function fistdist from the Statistics and Machine Learning Toolbox. One of the things you may want to do when analyzing two sets of data is comparing their distributions. Jiro's pick this week is "Comparing Multiple Histograms" by Jonathan C.