## normalbounds

Return the boundaries containing 100% p points drawn from a normal distribution.

bounds = normalbounds(p)
bounds = normalbounds(p, mu)
bounds = normalbounds(p, mu, sigma)

## Inputs

p Fraction of data within bounds (e.g., 0.95 for 95%) Mean (defaults to 0) Standard deviation (defaults to 1)

## Outputs

bounds Bounds containing p fraction of data

## Example

We'll make sure that the theoretical and empircal bounds match up reasonably well for some random draws.

% Generate 10,000 samples from a normal distribution.
n     = 10000;
mu    = 1;
sigma = 3;
x     = sigma * randn(1, n) + mu;

% Get the theoretical 95% bounds.
b = normalbounds(0.95, mu, sigma);

% See what percentage of the data are within bounds.
sum(b(1) < x & x < b(2)) / n
ans =
0.9477