FreeBSD Manual Pages

PointEstimation(3)    User Contributed Perl Documentation   PointEstimation(3)

NAME
Statistics::PointEstimation - Perl module for computing confidence
intervals in parameter estimation with Student's	T distribution
Statistics::PointEstimation::Sufficient - Perl module for computing the
confidence intervals using sufficient statistics

SYNOPSIS
# example for Statistics::PointEstimation
use Statistics::PointEstimation;

my @r=();
for(\$i=1;\$i<=32;\$i++) #generate a uniformly distributed sample	with mean=5
{

\$rand=rand(10);
push @r,\$rand;
}

my \$stat = new	Statistics::PointEstimation;
\$stat->set_significance(95); #set the significance(confidence)	level to 95%
\$stat->output_confidence_interval(); #output summary
\$stat->print_confidence_interval();  #output the data hash related to confidence interval estimation

#the following	is the same as \$stat->output_confidence_interval();
print "Summary	 from the observed values of the sample:\n";
print "\tsample size= ", \$stat->count()," , degree of freedom=", \$stat->df(), "\n";
print "\tmean=", \$stat->mean()," , variance=",	\$stat->variance(),"\n";
print "\tstandard deviation=",	\$stat->standard_deviation()," ,	standard error=", \$stat->standard_error(),"\n";
print "\t the estimate	of the mean is ", \$stat->mean()," +/- ",\$stat->delta(),"\n\t",
" or (",\$stat->lower_clm()," to ",\$stat->upper_clm," )	with ",\$stat->significance," % of confidence\n";
print "\t t-statistic=T=",\$stat->t_statistic()," , Prob >|T|=",\$stat->t_prob(),"\n";

#example for Statistics::PointEstimation::Sufficient

use strict;
use Statistics::PointEstimation;
my (\$count,\$mean,\$variance)=(30,3.996,1.235);
my \$stat = new	Statistics::PointEstimation::Sufficient;
\$stat->set_significance(99);
\$stat->output_confidence_interval();
\$stat->set_significance(95);
\$stat->output_confidence_interval();

DESCRIPTION
Statistics::PointEstimation
This module is	a subclass of Statistics::Descriptive::Full. It	uses T-distribution for	point estimation
assuming the data is normally distributed or the sample size is sufficiently large. It	overrides the
add_data() method in Statistics::Descriptive to compute the confidence	interval with the specified significance
level	(default is 95%). It also computes the t-statistic=T and Prob>|T| in case of hypothesis
testing of paired T-tests.

Statistics::PointEstimation::Sufficient
This module is a subclass of Statistics::PointEstimation. Instead of taking the	real data points as the	input,
it will	compute	the confidence intervals based on the sufficient statistics and	the sample size	inputted.
To use this module, you	need to	pass the sample	size, the sample mean ,	and the	sample variance	into the load_data()
function. The output will be exactly the same as the Statistics::PointEstimation Module.

AUTHOR
Yun-Fang	Juan , Yahoo! Inc.  (yunfang@yahoo-inc.com)