# NAME

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

# SYNOPSIS

`  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->add_data(@r);
\$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";```

# DESCRIPTION

```  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.```

# AUTHOR

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

# SEE ALSO

Statistics::Descriptive Statistics::Distributions