IMSL C Stat Library
cvm_normality_test
Performs a Cramer-von Mises test for normality.
Synopsis
#include <imsls.h>
float imsls_f_cvm_normality_test (int nobs, float x[], … , 0)
The type double function is imsls_d_cvm_normality_test.
Required Arguments
int nobs (Input)
Number of observations. nobs must be greater than or equal to 3.
float x[] (Input)
Vector of length nobs containing the observations.
Return Value
The p‑value for the Cramer-von Mises test of normality.
Synopsis with Optional Arguments
#include <imsls.h>
float imsls_f_cvm_normality_test (int nobs, float x[],
IMSLS_STAT, float *cvmstat,
IMSLS_N_MISSING, int *nmiss,
0)
Optional Arguments
IMSLS_STAT, float *cvmstat (Output)
The Cramer-von Mises statistic.
IMSLS_N_MISSING, int *nmiss (Output)
The number of missing observations.
Description
Given a data sample {Xi, i=1 .. n}, where n = nobs and Xi = x[i-1], function imsls_f_cvm_normality_test computes the Cramer-von Mises (CvM) normality statistic W = cvmstat and the corresponding Return Value (p-value) P = P == {probability that a normally distributed n element sample would have a CvM statistic > W}. If P is sufficiently small (e.g. P < .05), then the CvM test indicates that the null hypothesis that the data sample is normally-distributed should be rejected. W is calculated:
Where is the cumulative distribution function of standard normal N(0,1) distribution, , and and s are the sample mean and standard deviation respectively. P is calculated by first transforming W to an “n-adjusted” statistic W*:
and then calculating P in terms of W* using a parabolic approximation taken from Table 4.9 in Stephens (1986).
Example
This example is taken from Conover (1980, pages 364 and 195). The data consists of 50 two digit numbers taken from a telephone book. The CvM test fails to reject the null hypothesis of normality at the .05 level of significance.
 
#include <imsls.h>
#include <stdio.h>
 
int main()
{
int nobs = 50, nmiss;
float p_value, cvmstat;
 
float x[] = {
23.0, 36.0, 54.0, 61.0, 73.0, 23.0, 37.0, 54.0, 61.0, 73.0,
24.0, 40.0, 56.0, 62.0, 74.0, 27.0, 42.0, 57.0, 63.0, 75.0,
29.0, 43.0, 57.0, 64.0, 77.0, 31.0, 43.0, 58.0, 65.0, 81.0,
32.0, 44.0, 58.0, 66.0, 87.0, 33.0, 45.0, 58.0, 68.0, 89.0,
33.0, 48.0, 58.0, 68.0, 93.0, 35.0, 48.0, 59.0, 70.0, 97.0};
 
p_value = imsls_f_cvm_normality_test (nobs, x,
IMSLS_STAT, &cvmstat,
IMSLS_N_MISSING, &nmiss,
0);
 
printf ("Cramer-von Mises statistic = %11.4f \n", cvmstat);
printf ("p-value = %11.4f\n", p_value);
printf ("# missing values = %4d\n", nmiss);
}
Output
 
Cramer-von Mises statistic = 0.0520
p-value = 0.4747
# missing values = 0
 
Informational Errors
IMSLS_PVAL_UNDERFLOW
The p‑value has fallen below the minimum value of # for which its calculation has any accuracy; ZERO is returned.
Fatal Errors
IMSLS_TOO_MANY_MISSING
After removing the missing observations only 2 observations remain. The test cannot proceed.