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function [pf,pv,pr,alfa]=normtest(stat,mn,vr,alfa)
% [pf,pv,pr,alfa]=NORMTEST(stat,mn,vr,alfa)
% Test whether a particular value is derived from a hypothesized NORMAL
% (GAUSSIAN) distribution with known (postulated) mean and variance, reject
% if the probability of more extreme than observed values is very unlikely,
% e.g. below (1-alfa)x100 per cent. This is a TWO-SIDED test, where the
% algorithm depends on the symmetry of the distribution. It is a ONE-SAMPLE
% test for every ELEMENT of the array offered (i.e. quite literally ONE
% sample, of one point, each), unlike Matlab's own ZTEST.
% stat The values being tested individually, could be a vector or a matrix
% mn The expected value of the normal distribution under the null [0]
% vr The variance of the normal distribution under the null [1]
% alfa The significance level for a (1-alfa)x100 confidence [0.05]
% pf 0 if it PASSES, i.e. the statistic is derived from the null
% 1 if it FAILS, i.e. the statistic is REJECTED to be from null
% pv The 'p-value' of having an even more extreme value of the test
% pr The percentage of the test values that are being rejected,
% useful if you give a whole vector of stat values
% alfa The significance level [0.05] for a (1-alfa)x100 confidence
% mu=randn; vr=rand; al=randi(100)/100; mn=randi(1e3);
% [a,b,c]=normtest(randn(mn,1),0,1,al);
% Last modified by, 07/03/2018
% Default confidence limit
% ONE-SAMPLE (single value!) TEST ON THE MEAN (applied on every element)
% This is the probability of a single test value even more extremely
% removed, in the absolute sense, from the expectation, given a known
% standard deviation
% This is the test, reject gives a 1, as in, could arise by chance
% This is the report on the rejections
% Provide an uplifting message
if length(stat)>1
disp(sprintf('\n%s %i%% rejected at the %i%% confidence level',...