normdf Documentation


PLS_Toolbox Documentation: normdf	< lognormdf	paretodf >

normdf

Purpose

Normal / Gaussian distribution.

Synopsis

prob = normdf(function,x,a,b)

Description

Estimates cumulative distribution function (cumulative, cdf), probability density function (density, pdf), quantile (inverse of cdf), or random numbers for a Normal distribution.

This distribution is used for many data types including physical attributes and sums of quantities. It is a symmetric distribution and the variance can be smaller, equal, or larger than the mean.

INPUTS:

function = [ {'cumulative'} | 'density' | 'quantile' | 'random' ], defines the functionality to be used. Note that the function recognizes the first letter of each string so that the string could be: [ 'c' | 'd' | 'q' | 'r' ].

x = matrix in which the sample data is stored, in the interval (-inf,inf).

for function=quantile - matrix with values in the interval (0,1).

for function=random - vector indicating the size of the random matrix to create.

a = mode/location parameter (real).

b = scale parameter (real and positive).

Note: If inputs (x, a, and b) are not equal in size, the function will attempt to resize all inputs to the largest input using the RESIZE function.

Note: Functions will typically allow input values outside of the acceptable range to be passed but such values will return NaN in the results.

Examples

Cumulative:

>> prob = normdf('c',[1.9600 2.5758])

ans =

0.9750 0.9950

>> x = -5:.1:5;

>> plot(x,normdf('c',x,0,1)), vline([ 0 ; normdf('q',[0.975; 0.995],0,1)])

Density:

>> prob = normdf('d',[1.9600 2.5758],0,1)

ans =

0.0584 0.0145

>> x = -5:.1:5;

>> plot(x,normdf('d',x,0,1)), vline([0; normdf('q',[0.975; 0.995],0,1)])

Quantile:

ans =

1.9600 2.5758

Random:

>> prob = normdf('r',[4 1],0,1)

ans =

-0.4326

-1.6656

0.1253

0.2877