
EJB Suite offers functionality from Basic Statistics, Discrete Probability, Standard Probability Distributions, Hypothesis Testing, Correlation and Linear Regression.
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, InterQuartile 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, ttest, ztransform) 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.
Curve Fitting Module
The Curve Fitting module offers procedures by which linear and nonlinear functions can be fitted in accordance with the least squares approach to a given data set which may or may not exhibit measurement errors. We also include functionality which performs ANNOVA type analysis including goodnessoffit measures such as the RSquared measure and TTest statistic.
Confidence Intervals and Hypothesis Testing Module
Within this component we present two aspects of inferential statistics known as confidence intervals and hypothesis testing.


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Offers functionality from Basic Statistics, Discrete Probability, Standard Probability Distributions, Hypothesis Testing, Correlation and Linear Regression.
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, InterQuartile 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, ttest, ztransform) 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.
Curve Fitting Module
The Curve Fitting module offers procedures by which linear and nonlinear functions can be fitted in accordance with the least squares approach to a given data set which may or may not exhibit measurement errors. We also include functionality which performs ANNOVA type analysis including goodnessoffit measures such as the RSquared measure and TTest statistic.
Confidence Intervals and Hypothesis Testing Module
Within this component we present two aspects of inferential statistics known as confidence intervals and hypothesis testing.
WebCab Probability and Stat (J2EE Ed.): http://www.webcabcomponents.com/demo/StatisticsJ2EEDemo.jar
WebCab Probability and Stat (J2SE Ed.): http://www.webcabcomponents.com/Java/demo/StatisticsJ2SEDemo.jar 
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