MATLAB: A statistical plot demonstrating quantity and std (or mean)

statisticsStatistics and Machine Learning Toolbox

What kind of statistical plot do you think can demonstrate both the quantity and std (or mean)?

For example, I have a dataset of a large population in different ages. I used clustering method to separate the population into ten groups based on the Euclidian metric of age. Now I want to examine the population quantity (counts) and age std (or mean) in each clustered group. Ideally, the std for each group will be very small, but their mean increases monotonically.

Best Answer

What about a box plot http://www.mathworks.com/help/stats/boxplot.html?s_tid=srchtitle

Related Solutions

MATLAB: Too many plots..too little time!!

Well you can read the documentation as well as we can, but here's a very, very short version of what you'll find there. If you want to plot a histogram of your data (which may or may not be Gaussian) plotted, use histogram(). If you don't want a plot, use histcounts(). If you want the histogram plotted plus the Gaussian over it, use histfit(). If you want just the Gaussian plotted, use fitdist(). bar() and barh() can be used if you want to plot on your own, manually, with the arrays you get returned from histcounts() or histogram(). If you want some idea of how much your data matches a perfect Gaussian, use normplot().

MATLAB: Normalized distribution for histogram

If you have the Statistics Toolbox, you might find the dfittool distribution fitting tool quite useful.

doc dfittool

If not, you can normalize a histogram by scaling the counts in each bin. With the normalized counts, you can plot both the normalized histogram and your curve. The trick is to identify the appropriate scaling factor.

Here is some example code where I plot the normal probability with the normalized histogram data:

% Arbitrary data generation and statistics
dataVec = randn(1000,1);
muData = mean(dataVec);
stdData = std(dataVec);
% Defining the bin centers
binStep    = 0.1;
binCenters = -5:binStep:5;
% Compute the histogram scaling factor. (If all histogram counts are in a
% single bin, the probability = 1)
binCount   = histc(dataVec,binCenters);
probScale  = sum(binCount)*binStep;
% Plot the normalized histogram and change the bar color for line
% visibility
histHandle = bar(binCenters,binCount/probScale,'hist');
set(histHandle,'FaceColor',[1,1,1]); 
hold on;
% Overlay the distribution fit on the histogram
x = binCenters;
y = normpdf(x,muData,stdData);
plot(x,y);
xlabel('Data');
ylabel('Probability');

Hope this helps to get you started!

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Related Solutions

MATLAB: Too many plots..too little time!!

MATLAB: Normalized distribution for histogram

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