C Library Functions  - uniplt (3)

NAME
uniplt(3f) - [M_datapac:LINE_PLOT] generate a Uniform probability plot (line printer graph)

CONTENTS

Synopsis
Description
Input Arguments
Output
Examples
Author
Maintainer
License
References

SYNOPSIS
SUBROUTINE UNIPLT(X,N) 

       REAL(kind=wp),intent(in) :: X(:)
       INTEGER,intent(in)       :: N

DESCRIPTION
UNIPLT(3f) generates a uniform probability plot.

The prototype uniform distribution used herein is defined on the unit interval (0,1). This distribution has mean = 0.5 and standard deviation = sqrt(1/12) = 0.28867513.

This distribution has the probability density function 

       f(X) = 1
As used herein, a probability plot for a distribution is a plot of the ordered observations versus the order statistic medians for that distribution.

The uniform probability plot is useful in graphically testing the composite (that is, location and scale parameters need not be specified) hypothesis that the underlying distribution from which the data have been randomly drawn is the uniform distribution.

If the hypothesis is true, the probability plot should be near-linear.

A measure of such linearity is given by the calculated probability plot correlation coefficient.

INPUT ARGUMENTS

X The vector of (unsorted or sorted) observations.
N The integer number of observations in the vector X. The maximum allowable value of N for this subroutine is 7500.

OUTPUT
A one-page uniform probability plot.

EXAMPLES
Sample program: 

   program demo_uniplt
   use M_datapac, only : uniplt, label
   implicit none
   call label(’uniplt’)
   ! call uniplt(x,y)
   end program demo_uniplt
Results:

AUTHOR
The original DATAPAC library was written by James Filliben of the Statistical Engineering Division, National Institute of Standards and Technology.

MAINTAINER
John Urban, 2022.05.31

LICENSE
CC0-1.0

REFERENCES
o Filliben, ’Techniques for Tail Length Analysis’, Proceedings of the Eighteenth Conference on the Design of Experiments in Army REsearch Development and Testing (Aberdeen, Maryland, October, 1972), pages 425-450.
o Hahn and Shapiro, Statistical Methods in Engineering, 1967, pages 260-308.
o Johnson and Kotz, Continuous Univariate Distributions--2, 1970, pages 57-74.

Nemo Release 3.1 uniplt (3) July 22, 2023
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