Calculating std and 95% confidence intervals in a 3d matrix along the 3rd dimension

My data is organized in a 3d matrix (subjects x conditions x regions of interest). I've collapsed (averaged) across subjects and conditions so that I'm left with one value for each region of interest using mean(mean(M(:, :, :))). However, I'm trying to find the std and 95% confidence interval (to help with visualization) and I'm not sure if it is statistically sound to use the same logic to calculate the std (i.e. std(std(M(:, :, :))). And, when calculating the 95% CI, which dimension should I use to decide my sample size?

Answer 1

[Aditya Patil]

std(std(M)) would give you std deviations of the std deviations, which I assume is not what you are expecting. If you want std deviations along a specific axis, use the following syntax

std(M,w,dim)

See Standard Deviation Along Matrix Rows for an example.

Alternately, if you want std deviation across all values, do

std(M,w,'all')