> > > This suite consists of five packages: Statistics, Discrete Probability, Standard Probability Distributions, Hypothesis Testing, and Correlation & Regression which offer the following functionality.
> > > Statistics Module
> > > The Statistics module incorporates evaluation procedures of standard quantitative measures of centrality (mean) and dispersion of (discrete) numerical sets. This module incorporates weighted averages, geometric mean, Inter-Quartile range, mean and standard deviation, sample variance and the coefficient of variation.
> > > Discrete Probability Module
> > > The Discrete Probability module encapsulates the foundations of discrete probability and discrete probability distributions. This component includes the addition law, conditional probability, cumulative distribution function, mean and variance of a distribution, expected values, covariance and simplification of expressions involving random variables.
> > > Correlation and Regression Module
> > > Allows the user to investigate relationships between two variables. These finding can be used to predict one variable from the given values of other variables. We cover linear (Spearman's, t-test, z-transform) and rank (Spearman's, Kendall's) correlation, linear regression and conditional means.
> > > Standard Probability Distributions Module
> > > This module assists in the development of applications that incorporate the Binomial, Poisson, Normal, Lognormal, Pareto, Uniform, Hypergeometric and Exponential probability distributions. The probability density function, cumulative distribution function and inverse, mean, variance, Skewness and Kurtosis are implemented where appropriate and/or their approximations for each distribution. We also offer methods which randomly generate numbers from a given distribution.
> > > Confidence Intervals and Hypothesis Testing Module
> > > Within this component we present two aspects of inferential statistics known as confidence intervals and hypothesis testing. Confidence intervals determine the level of confidence in pointwise statistics (e.g. mean, variance) of the sample in relation to the statistics for the entire population. With hypothesis testing the user can judge which of several hypotheses sampled evidence best supports.

> > Prices

> > > WebCab Probability and Statistics v3.3 J2SE Edition
> > > Single Developer License | $199
> > > 4 Developer Team License | $339
> > > Site Wide Developer License | $677
> > > Demo License (limited functionality) | $0

> > > Prices are expressed in US Dollars.

> > Product Details

> > > This suite consists of four modules with the following features:
> > > Statistics Module
> > > - Measures of Mean
> > >   - Arithmetic Mean - a measure of centrality for quantitative data.
> > >   - Median - the middle value when the observations are arranged in order of magnitude.
> > >   - Mode - the most frequently occurring observation.
> > >   - Weighted Average - the arithmetic average of a weighted set
> > >   - Geometric Mean - the nth root of the product of all numerical observations.
> > >   - Range - the difference between the largest and the smallest observation.
> > > - Measures of Dispersion
> > >   - Inter-Quartile Range (IRQ) - a measure of dispersion which is not affected by extreme values.
> > >   - Mean Deviation - the sum of the distances between the observation and the arithmetic mean.
> > >   - Sample Variance - the sum of the squares of the distances between the observations and the arithmetic mean.
> > >   - Sample Standard Deviation - the measure of dispersion which has the same units as the observations and is the square root of the sample variance.
> > >   - Coefficient of Variation - computes the spread of sets for observations which have been made in different units.
> > > - Data Presentation
> > >   - Frequency Tables - show the number of the elements inside some intervals.
> > >   - Cumulative Frequency Tables - show the number of the elements within a data set either the highest value (or above the lowest value) of the present frequency table.
> > >   - Relative Frequency Tables - help to compare two or more data sets, normalized the frequencies.
> > > Discrete Probability Module
> > > - Discrete Probability
> > >   - Discrete probability - a priori and relative frequency definition.
> > >   - The Addition Law - used when combining event set or testing for their independence.
> > >   - Conditional probability - the probability of an event assume that another event takes place.
> > >   - Complementary event probability - the probability of a set of events not taking place.
> > > - Discrete Probability Distributions
> > >   - Cumulative distribution function - the sum of the probabilities of a sequence of events.
> > >   - Mean - the sum of the product of the probability of an event with its value.
> > >   - Variance - a measure of the distributions spread from the mean.
> > >   - Expected values - the expected value of the random variable.
> > >   - Covariance - the covariance of two random variables.
> > >   - Ro - the population correlation coefficient of two random variables.
> > > - Expression with random variables and basic probability laws
> > >   - Simplify expression - simplify expressions involving the mean, variance, expected values and covariance of random variables.
> > >   - Union and Intersection - the basic formulae for calculating the union and the intersection of two or many events.
> > > Correlation and Regression Module
> > > - Statistic quantities
> > >   - Mean - calculates the arithmetic mean.
> > >   - Sample variance - calculates the sample variance.
> > > - Correlation coefficients
> > >   - Pearson's product moment correlation coefficient - the most widely used linear correlation coefficient for a data set.
> > >   - t-test, z-transform - provides an analytic framework to establishing a confidence level for Pearson's coefficient.
> > >   - Spearman's and Kendall's rank correlation coefficients - measure the association between two variables of an ordered data set.
> > > - Regression line - using the method of least squares to determine the line of best fit.
> > > - Confidence interval for the conditional mean - determines the confidence interval for the true regression line.
> > > Standard Probability Distributions Module
> > > - Discrete Random Variables
> > >   - Binomial distribution - used to model an experiment which has two outcomes `successes' and `failures' of elements from a finite set.
> > >   - Poisson distribution - used to model instances such as the number of cars arriving at a petrol station over 1 hour.
> > >   - Poisson Approximation of the Binomial distribution - Approximation of the Binomial distribution used when the number of trial is large and the probability of is small.
> > >   - Hypergeometic Probability Distribution - closely related to the Binomial probability distribution.
> > >   - Normal Approximation of the Binomial Distribution - approximation of the Binomial Probability Distribution by the Normal Probability Distribution.
> > > - Continuous Random Variables
> > >   - Normal distribution - Used in a broad range of applications include finance (asset price evolution,...), scientific measurement,...
> > >   - Log Normal distribution - Used for example when modeling investment returns and the distribution of insurance claim sizes.
> > >   - Pareto Distribution - Useful for cautiously modeling the distribution of large insurance claims.
> > >   - Uniform Distribution - Used to model situations where the probability is proportional to the length of the interval.
> > >   - Exponential Distribution - Can be used to describe situations such as the time between arrivals at a petrol station.
> > > - Numerical Methods
> > >   - Extended Trapezoidal Rule - this method is implemented in order to evaluate the non-analytic probability density functions of the Normal and Lognormal distributions.
> > > Hypothesis Testing Module
> > > - Normal Confidence Interval - used when large samples with `>30` elements are considered.
> > >   - Two-sided confidence interval for the mean, proportions, difference between means and difference between proportions.
> > >   - One-sided confidence interval for the mean, proportions and difference between means.
> > >   - Estimating the sample size for a given confidence of the mean.
> > >   - Estimating the sample size for a given confidence of the proportions.
> > > - Student Confidence Interval - used when small samples with `<=30` elements are considered.
> > >   - Two-sided confidence interval for the mean and the difference between means.
> > >   - One-sided confidence interval for the mean.
> > > - Normal Hypothesis Testing - used when large samples with `>30` elements are considered.
> > >   - Two-sided hypothesis testing for the mean, proportions, difference between means and difference between proportions.
> > >   - One-sided hypothesis testing for the mean, proportions, difference between means and difference between proportions.
> > > - Student Hypothesis Testing - used when small samples with `<=30` elements are considered.
> > >   - Two-sided hypothesis testing for the mean, proportions and the difference between means.
> > >   - One-sided confidence interval for the mean, proportions and difference between means.
> > > This product also contains the following features:
> > > - GUI Bundle - we bundle a suite of graphical user interface JavaBean components allowing the developer to plug-in a wide range of GUI functionality (including charts/graphs) into their client applications.
> > > - JDBC Mediator - A J2SE Component which mediates between a J2SE component, its J2SE Clients and the Database server. The JDBC Mediator J2SE classes are a convenient way of enhancing all financial and mathematical specific methods with JDBC-based functionality. Check the jdbc subpackage of every J2SE class for JavaDocs documentation.
> > > - Web Application Example - A Java WAR file which contains a JSP example that makes use of the functionality provided by our J2SE Component.
> > > - Synthetic JDBC - The JDBC functionality provided by the Web Application example included within this package. This Web Application is an example of how to make a JSP client using our J2SE Component while manually implementing the JDBC code. The JSP Application applies J2SE methods to certain rows from the database and lists the output in HTML format.

> > Prerequisites

> > > - An Operating System running JavaTM
> > > - Pentium III® 500 Mhz
> > > - 128MB RAM
> > > Software requirements:
> > > - JDK 1.3 or compatible

> > Compatibility

> > > Operating System for Deployment:
> > > - Windows 2003, XP, 2000, NT, 9x
> > > - Sun Solaris
> > > - Linux
> > > - IBM AIX

> > > Built Using:

> > > - JavaTM 2 SDK Standard Edition 1.3.1/1.4.x

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