Provided by: libstatistics-descriptive-perl_3.0702-1_all bug


       Statistics::Descriptive - Module of basic descriptive statistical functions.


       version 3.0702


           use Statistics::Descriptive;
           my $stat = Statistics::Descriptive::Full->new();
           my $mean = $stat->mean();
           my $var = $stat->variance();
           my $tm = $stat->trimmed_mean(.25);
           $Statistics::Descriptive::Tolerance = 1e-10;


       This module provides basic functions used in descriptive statistics.  It has an object
       oriented design and supports two different types of data storage and calculation objects:
       sparse and full. With the sparse method, none of the data is stored and only a few
       statistical measures are available. Using the full method, the entire data set is retained
       and additional functions are available.

       Whenever a division by zero may occur, the denominator is checked to be greater than the
       value $Statistics::Descriptive::Tolerance, which defaults to 0.0. You may want to change
       this value to some small positive value such as 1e-24 in order to obtain error messages in
       case of very small denominators.

       Many of the methods (both Sparse and Full) cache values so that subsequent calls with the
       same arguments are faster.


       version 3.0702


   Sparse Methods
       $stat = Statistics::Descriptive::Sparse->new();
            Create a new sparse statistics object.

            Effectively the same as

              my $class = ref($stat);
              undef $stat;
              $stat = new $class;

            except more efficient.

            Adds data to the statistics variable. The cached statistical values are updated

            Returns the number of data items.

            Returns the mean of the data.

            Returns the sum of the data.

            Returns the variance of the data.  Division by n-1 is used.

            Returns the standard deviation of the data. Division by n-1 is used.

            Returns the minimum value of the data set.

            Returns the index of the minimum value of the data set.

            Returns the maximum value of the data set.

            Returns the index of the maximum value of the data set.

            Returns the sample range (max - min) of the data set.

   Full Methods
       Similar to the Sparse Methods above, any Full Method that is called caches the current
       result so that it doesn't have to be recalculated.  In some cases, several values can be
       cached at the same time.

       $stat = Statistics::Descriptive::Full->new();
            Create a new statistics object that inherits from Statistics::Descriptive::Sparse so
            that it contains all the methods described above.

            Adds data to the statistics variable.  All of the sparse statistical values are
            updated and cached.  Cached values from Full methods are deleted since they are no
            longer valid.

            Note:  Calling add_data with an empty array will delete all of your Full method
            cached values!  Cached values for the sparse methods are not changed

       $stat->add_data_with_samples([{1 => 10}, {2 => 20}, {3 => 30},]);
            Add data to the statistics variable and set the number of samples each value has been
            built with. The data is the key of each element of the input array ref, while the
            value is the number of samples: [{data1 => smaples1}, {data2 => samples2}, ...].

            NOTE: The number of samples is only used by the smoothing function and is ignored
            otherwise. It is not equivalent to repeat count. In order to repeat a certain datum
            more than one time call add_data() like this:

                my $value = 5;
                my $repeat_count = 10;
                    [ ($value) x $repeat_count ]

            Returns a copy of the data array.

            Returns a copy of the data array without outliers. The number minimum of samples to
            apply the outlier filtering is $Statistics::Descriptive::Min_samples_number, 4 by

            A function to detect outliers need to be defined (see "set_outlier_filter"),
            otherwise the function will return an undef value.

            The filtering will act only on the most extreme value of the data set (i.e.: value
            with the highest absolute standard deviation from the mean).

            If there is the need to remove more than one outlier, the filtering need to be re-run
            for the next most extreme value with the initial outlier removed.

            This is not always needed since the test (for example Grubb's test) usually can only
            detect the most exreme value. If there is more than one extreme case in a set, then
            the standard deviation will be high enough to make neither case an outlier.

            Set the function to filter out the outlier.

            $code_ref is the reference to the subroutine implementing the filtering function.

            Returns "undef" for invalid values of $code_ref (i.e.: not defined or not a code
            reference), 1 otherwise.

            ·   Example #1: Undefined code reference

                    my $stat = Statistics::Descriptive::Full->new();
                    $stat->add_data(1, 2, 3, 4, 5);

                    print $stat->set_outlier_filter(); # => undef

            ·   Example #2: Valid code reference

                    sub outlier_filter { return $_[1] > 1; }

                    my $stat = Statistics::Descriptive::Full->new();
                    $stat->add_data( 1, 1, 1, 100, 1, );

                    print $stat->set_outlier_filter( \&outlier_filter ); # => 1
                    my @filtered_data = $stat->get_data_without_outliers();
                    # @filtered_data is (1, 1, 1, 1)

                In this example the series is really simple and the outlier filter function as
                well.  For more complex series the outlier filter function might be more complex
                (see Grubbs' test for outliers).

                The outlier filter function will receive as first parameter the
                Statistics::Descriptive::Full object, as second the value of the candidate
                outlier. Having the object in the function might be useful for complex filters
                where statistics property are needed (again see Grubbs' test for outlier).

       $stat->set_smoother({ method => 'exponential', coeff => 0, });
            Set the method used to smooth the data and the smoothing coefficient.  See
            "Statistics::Smoother" for more details.

            Returns a copy of the smoothed data array.

            The smoothing method and coefficient need to be defined (see "set_smoother"),
            otherwise the function will return an undef value.

            Sort the stored data and update the mindex and maxdex methods.  This method uses
            perl's internal sort.

            If called with a non-zero argument, this method sets a flag that says the data is
            already sorted and need not be sorted again.  Since some of the methods in this class
            require sorted data, this saves some time.  If you supply sorted data to the object,
            call this method to prevent the data from being sorted again. The flag is cleared
            whenever add_data is called.  Calling the method without an argument returns the
            value of the flag.

            Returns the skewness of the data.  A value of zero is no skew, negative is a left
            skewed tail, positive is a right skewed tail.  This is consistent with Excel.

            Returns the kurtosis of the data.  Positive is peaked, negative is flattened.

       $x = $stat->percentile(25);
       ($x, $index) = $stat->percentile(25);
            Sorts the data and returns the value that corresponds to the percentile as defined in

            ·   For example, given the 6 measurements:

                -2, 7, 7, 4, 18, -5

                Then F(-8) = 0, F(-5) = 1/6, F(-5.0001) = 0, F(-4.999) = 1/6, F(7) = 5/6, F(18) =
                1, F(239) = 1.

                Note that we can recover the different measured values and how many times each
                occurred from F(x) -- no information regarding the range in values is lost.
                Summarizing measurements using histograms, on the other hand, in general loses
                information about the different values observed, so the EDF is preferred.

                Using either the EDF or a histogram, however, we do lose information regarding
                the order in which the values were observed.  Whether this loss is potentially
                significant will depend on the metric being measured.

                We will use the term "percentile" to refer to the smallest value of x for which
                F(x) >= a given percentage.  So the 50th percentile of the example above is 4,
                since F(4) = 3/6 = 50%; the 25th percentile is -2, since F(-5) = 1/6 < 25%, and
                F(-2) = 2/6 >= 25%; the 100th percentile is 18; and the 0th percentile is
                -infinity, as is the 15th percentile, which for ease of handling and backward
                compatibility is returned as undef() by the function.

                Care must be taken when using percentiles to summarize a sample, because they can
                lend an unwarranted appearance of more precision than is really available.  Any
                such summary must include the sample size N, because any percentile difference
                finer than 1/N is below the resolution of the sample.

            (Taken from: RFC2330 - Framework for IP Performance Metrics, Section 11.3.  Defining
            Statistical Distributions.  RFC2330 is available from:
            <> .)

            If the percentile method is called in a list context then it will also return the
            index of the percentile.

       $x = $stat->quantile($Type);
            Sorts the data and returns estimates of underlying distribution quantiles based on
            one or two order statistics from the supplied elements.

            This method use the same algorithm as Excel and R language (quantile type 7).

            The generic function quantile produces sample quantiles corresponding to the given

            $Type is an integer value between 0 to 4 :

              0 => zero quartile (Q0) : minimal value
              1 => first quartile (Q1) : lower quartile = lowest cut off (25%) of data = 25th percentile
              2 => second quartile (Q2) : median = it cuts data set in half = 50th percentile
              3 => third quartile (Q3) : upper quartile = highest cut off (25%) of data, or lowest 75% = 75th percentile
              4 => fourth quartile (Q4) : maximal value

            Example :

              my @data = (1..10);
              my $stat = Statistics::Descriptive::Full->new();
              print $stat->quantile(0); # => 1
              print $stat->quantile(1); # => 3.25
              print $stat->quantile(2); # => 5.5
              print $stat->quantile(3); # => 7.75
              print $stat->quantile(4); # => 10

            Sorts the data and returns the median value of the data.

            Returns the harmonic mean of the data.  Since the mean is undefined if any of the
            data are zero or if the sum of the reciprocals is zero, it will return undef for both
            of those cases.

            Returns the geometric mean of the data.

       my $mode = $stat->mode();
            Returns the mode of the data. The mode is the most commonly occurring datum.  See
            <> . If all values occur only once,
            then mode() will return undef.

            "trimmed_mean(ltrim)" returns the mean with a fraction "ltrim" of entries at each end
            dropped. "trimmed_mean(ltrim,utrim)" returns the mean after a fraction "ltrim" has
            been removed from the lower end of the data and a fraction "utrim" has been removed
            from the upper end of the data.  This method sorts the data before beginning to
            analyze it.

            All calls to trimmed_mean() are cached so that they don't have to be calculated a
            second time.

            "frequency_distribution_ref($num_partitions)" slices the data into $num_partitions
            sets (where $num_partitions is greater than 1) and counts the number of items that
            fall into each partition. It returns a reference to a hash where the keys are the
            numerical values of the partitions used. The minimum value of the data set is not a
            key and the maximum value of the data set is always a key. The number of entries for
            a particular partition key are the number of items which are greater than the
            previous partition key and less then or equal to the current partition key. As an

               $f = $stat->frequency_distribution_ref(2);
               for (sort {$a <=> $b} keys %$f) {
                  print "key = $_, count = $f->{$_}\n";


               key = 2.5, count = 4
               key = 4, count = 3

            since there are four items less than or equal to 2.5, and 3 items greater than 2.5
            and less than 4.

            "frequency_distribution_refs(\@bins)" provides the bins that are to be used for the
            distribution.  This allows for non-uniform distributions as well as trimmed or sample
            distributions to be found.  @bins must be monotonic and must contain at least one
            element.  Note that unless the set of bins contains the full range of the data, the
            total counts returned will be less than the sample size.

            Calling "frequency_distribution_ref()" with no arguments returns the last
            distribution calculated, if such exists.

       my %hash = $stat->frequency_distribution($partitions);
       my %hash = $stat->frequency_distribution(\@bins);
       my %hash = $stat->frequency_distribution();
            Same as "frequency_distribution_ref()" except that it returns the hash clobbered into
            the return list. Kept for compatibility reasons with previous versions of
            Statistics::Descriptive and using it is discouraged.

            "least_squares_fit()" performs a least squares fit on the data, assuming a domain of
            @x or a default of 1..$stat->count().  It returns an array of four elements "($q, $m,
            $r, $rms)" where

            "$q and $m"
                satisfy the equation C($y = $m*$x + $q).

            $r  is the Pearson linear correlation cofficient.

                is the root-mean-square error.

            If case of error or division by zero, the empty list is returned.

            The array that is returned can be "coerced" into a hash structure by doing the

              my %hash = ();
              @hash{'q', 'm', 'r', 'err'} = $stat->least_squares_fit();

            Because calling "least_squares_fit()" with no arguments defaults to using the current
            range, there is no caching of the results.


       I read my email frequently, but since adopting this module I've added 2 children and 1 dog
       to my family, so please be patient about my response times.  When reporting errors, please
       include the following to help me out:

       ·   Your version of perl.  This can be obtained by typing perl "-v" at the command line.

       ·   Which version of Statistics::Descriptive you're using.  As you can see below, I do
           make mistakes.  Unfortunately for me, right now there are thousands of CD's with the
           version of this module with the bugs in it.  Fortunately for you, I'm a very patient
           module maintainer.

       ·   Details about what the error is.  Try to narrow down the scope of the problem and send
           me code that I can run to verify and track it down.


       Current maintainer:

       Shlomi Fish, <> , ""


       Colin Kuskie

       My email address can be found at under Who's Who or at: .


       Fabio Ponciroli & Adzuna Ltd. team (outliers handling)


       RFC2330, Framework for IP Performance Metrics

       The Art of Computer Programming, Volume 2, Donald Knuth.

       Handbook of Mathematica Functions, Milton Abramowitz and Irene Stegun.

       Probability and Statistics for Engineering and the Sciences, Jay Devore.


       Copyright (c) 1997,1998 Colin Kuskie. All rights reserved.  This program is free software;
       you can redistribute it and/or modify it under the same terms as Perl itself.

       Copyright (c) 1998 Andrea Spinelli. All rights reserved.  This program is free software;
       you can redistribute it and/or modify it under the same terms as Perl itself.

       Copyright (c) 1994,1995 Jason Kastner. All rights reserved.  This program is free
       software; you can redistribute it and/or modify it under the same terms as Perl itself.


       This program is free software; you can redistribute it and/or modify it under the same
       terms as Perl itself.


       Shlomi Fish <>


       This software is copyright (c) 1997 by Jason Kastner, Andrea Spinelli, Colin Kuskie, and

       This is free software; you can redistribute it and/or modify it under the same terms as
       the Perl 5 programming language system itself.


       Please report any bugs or feature requests on the bugtracker website

       When submitting a bug or request, please include a test-file or a patch to an existing
       test-file that illustrates the bug or desired feature.


       You can find documentation for this module with the perldoc command.

         perldoc Statistics::Descriptive

       The following websites have more information about this module, and may be of help to you.
       As always, in addition to those websites please use your favorite search engine to
       discover more resources.

       ·   MetaCPAN

           A modern, open-source CPAN search engine, useful to view POD in HTML format.


       ·   Search CPAN

           The default CPAN search engine, useful to view POD in HTML format.


       ·   RT: CPAN's Bug Tracker

           The RT ( Request Tracker ) website is the default bug/issue tracking system for CPAN.


       ·   AnnoCPAN

           The AnnoCPAN is a website that allows community annotations of Perl module


       ·   CPAN Ratings

           The CPAN Ratings is a website that allows community ratings and reviews of Perl


       ·   CPANTS

           The CPANTS is a website that analyzes the Kwalitee ( code metrics ) of a distribution.


       ·   CPAN Testers

           The CPAN Testers is a network of smoke testers who run automated tests on uploaded
           CPAN distributions.


       ·   CPAN Testers Matrix

           The CPAN Testers Matrix is a website that provides a visual overview of the test
           results for a distribution on various Perls/platforms.


       ·   CPAN Testers Dependencies

           The CPAN Testers Dependencies is a website that shows a chart of the test results of
           all dependencies for a distribution.


   Bugs / Feature Requests
       Please report any bugs or feature requests by email to "bug-statistics-descriptive at", or through the web interface at
       <>. You will be
       automatically notified of any progress on the request by the system.

   Source Code
       The code is open to the world, and available for you to hack on. Please feel free to
       browse it and play with it, or whatever. If you want to contribute patches, please send me
       a diff or prod me to pull from your repository :)


         git clone git://