For each element of x, compute the cumulative distribution function (CDF) of the normal distribution with mean mu and standard deviation sigma. The size of p is the common size of x, mu and sigma. A scalar input functions as a constant matrix of the same size as the other inputs.

Default values are mu = 0, sigma = 1.

When called with three output arguments, i.e. [p, plo, pup], normcdf computes the confidence bounds for p when the input parameters mu and sigma are estimates. In such case, pcov, a $2\times 2$ matrix containing the covariance matrix of the estimated parameters, is necessary. Optionally, alpha, which has a default value of 0.05, specifies the 100 * (1 - alpha) percent confidence bounds. plo and pup are arrays of the same size as p containing the lower and upper confidence bounds.

[…] = normcdf (…, "upper") computes the upper tail probability of the normal distribution with parameters mu and sigma, at the values in x. This can be used to compute a right-tailed p-value. To compute a two-tailed p-value, use 2 * normcdf (-abs (x), mu, sigma).

Further information about the normal distribution can be found at https://en.wikipedia.org/wiki/Normal_distribution

See also: norminv, normpdf, normrnd, normfit, normlike, normstat

Source Code: normcdf