Statistics::PointEstimation - Perl module for computing the confidence interval in parameter estimation with Student's T distribution


  use Statistics::PointEstimation;
  my @r=();
  for($i=1;$i<=32;$i++) #generate a uniformly distributed sample with mean=5   
          push @r,$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";


  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.


Yun-Fang Juan , Yahoo! Inc. (


Statistics::Descriptive Statistics::Distributions