How do I efficiently find the mean and covariance of a cell containing matrices with different rows?

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I have a 1x15 cell which contains 15 matrices, and each of them has different number of rows but all have the same number of columns(14). I am trying to find the mean and covariance of the whole cell. I compute them by concatenating all matrices into a big matrix and use the built-in functions to get the result, which looks like this:
M = C{1};
for i=2:15
M = [M; C{i}];
mean = mean(M);
cov = cov(M);
I think this approach does not fully take advantage of the flexibility of cells. Is there a more efficient way to do that?

Accepted Answer

ANKUR KUMAR on 10 Oct 2018
Edited: ANKUR KUMAR on 10 Oct 2018
Use concatenate to mix up all cells into one matrix.
cat(2,C{:}); %you can use this only if number of rows must be same in all cells
cat(1,C{:}); %for your data
After using cat, calculate mean and cov in usual manner.
As your data have the different number of rows in all cells, you cannot use cat(2,C{:}).

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