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 on...

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 on...

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 on...

This module aims at providing some advanced statistical functions without storing all data in memory, at the cost of certain (predictable) precision loss. Data is represented by a set of bins that only store counts of fitting data points. Most bins a...

This module provide methods to smooth the trend of a series of statistical data. The methods provided are the "Exponential" and the "Weighted Exponential" (see respectively "Statistics::Descriptive::Smoother::Exponential" and "Statistics::Descriptive...

This module partially extends the module Statistics::Descriptive to handle weighted variates. Like that module, this module has an object-oriented design and supports two different types of data storage and calculation objects: sparse and full. With ...

This module provides basic functions used in descriptive statistics. It borrows very heavily from Statistics::Descriptive::Full (which is included with Statistics::Descriptive) with one major difference. This module is optimized for discretized data ...

This module implement the exponential smoothing algorithm to smooth the trend of a series of statistical data. This algorithm works well for unsmoothed data build with big number of samples. If this is not the case you might consider using the "Weigh...

This module implement the weighted exponential smoothing algorithm to smooth the trend of a series of statistical data. This algorithm can help to control large swings in the unsmoothed data that arise from small samples for those data points. The al...

Statistics are updated every time a new data point is added in. The common practice to calculate descriptive statistics for 5 data points as well as 1 billion points is to store them in an array, loop over the array to calculate the mean, then loop o...

I wrote Statistics::OLS to perform Ordinary Least Squares (linear curve fitting) on two dimensional data: y = a + bx. The other simple statistical module I found on CPAN (Statistics::Descriptive) is designed for univariate analysis. It accomodates OL...

This is a perl binding for CLUTO. Please refer to the CLUTO's manual sections 5.6 - 5.8 for details of each function. Basically, Statistics::Cluto has all corresponding methods for functions described in the manual. loading matrix Initial matrix can ...

Statistics::TTest This is the Statistical T-Test module to compare 2 independent samples. It takes 2 array of point measures, compute the confidence intervals using the PointEstimation module (which is also included in this package) and use the T-sta...

Statistics::Frequency is a simple class for counting *elements*, in other words, their *frequencies*. Note that Statistics::Frequency is not similar to statistics modules like, say, Statistics::Descriptive. Statistics::Frequency doesn't operate on nu...

This module exports a single routine, get_histogram, which expects an array reference as its only required argument. The array should contain a sequence of numbers, and the response will be an ascii-formatted histogram, including some header lines pr...

See Orwant, Hietaniemi, and Macdonald, *Mastering Algorithms in Perl*, O'Reilly 1999. From Chapter 15: The significance tests covered so far can only pit one group against another. Sure, we could do a t-test of every possible pair of web design firms...

See Orwant, Hietaniemi, and Macdonald, *Mastering Algorithms in Perl*, O'Reilly 1999. From Chapter 15: The significance tests covered so far can only pit one group against another. Sure, we could do a t-test of every possible pair of web design firms...

Calculate basic descriptive statistics (mean, variance, standard deviation, skewness, kurtosis) without the need to store any data point/sample. Statistics are updated each time a new data point/sample comes in. There are three amazing things about B...

This package provides standard least squares linear regression functionality without the need for storing the complete data history. Like any other, it finds best m,k (in least squares sense) so that y = m*x + k fits data points (x_1,y_1),...,(x_n,y_...

Tests for a difference between independent samples. It is commonly recommended as an alternative to the oneway independent groups ANOVA when variances are unequal, as its test-statistic, *T*, is not dependent on an estimate of variance. As a randomiz...