Statistics::MVA::Bartlett - Multivariate Test of Equality of Population Covariance Matrices.
This document describes Statistics::MVA::Bartlett version 0.0.4
# we have several groups of data each with 3 variables my $data_X = [ [qw/ 191 131 53/], [qw/ 200 137 52/], [qw/ 173 127 50/], [qw/ 160 118 47/], [qw/ 188 134 54/], [qw/ 186 129 51/], [qw/ 163 115 47/], ]; my $data_Y = [ [qw/ 211 122 49/], [qw/ 201 144 47/], [qw/ 242 131 54/], [qw/ 184 108 43/], [qw/ 223 127 51/], [qw/ 208 125 50/], [qw/ 199 124 46/], ]; my $data_Z = [ [qw/ 185 134 50/], [qw/ 171 128 49/], [qw/ 174 131 52/], [qw/ 186 107 49/], [qw/ 211 118 51/], [qw/ 217 122 49/], ]; use Statistics::MVA::Bartlett; # Create a Statistics::MVA::Bartlett object and pass it the data as a series of Lists-of-Lists within an anonymous array. my $bart1 = Statistics::MVA::Bartlett->new([$data_X, $data_Y, $data_Z]); # Access the output using the bartlett_mva method. In void context it prints a report to STDOUT. $bart->bartlett_mva; # In LIST-context it returns the relevant parameters. my ($chi, $df, $p) = $bart->bartlett_mva;
Bartlett's test is used to test if k samples have equal variances. This multivariate form tests for homogeneity of the variance-covariance matrices across samples. Some statistical tests assume such homogeneity across groups or samples. This test allows you to check that assumption. See http://www.itl.nist.gov/div898/handbook/eda/section3/eda357.htm.
'Statistics::MVA' => '0.0.1', 'Carp' => '1.08', 'Math::Cephes' => '0.47', 'Statistics::Distributions' => '1.02',
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Daniel S. T. Hughes
Copyright (c) 2010, Daniel S. T. Hughes
<email@example.com>. All rights reserved.
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