WebCab Probability and Stat (J2SE Ed.) v3.6

WebCab Probability and Stat (J2SE Ed.)
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Last Update:2006-09-12 - Minor Update: Bugs fixed
Requirements:An Operating System running Java
Type:Demo
Category:Business::Math & Scientific Tools
Language:English


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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, 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.


Curve Fitting Module

The Curve Fitting module offers procedures by which linear and non-linear 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 goodness-of-fit measures such as the R-Squared measure and T-Test statistic.


Confidence Intervals and Hypothesis Testing Module

Within this component we present two aspects of inferential statistics known as confidence intervals and hypothesis testing.





Java - JavaBeans - Class Libraries - J2SE - JSP - Basic - Statistics - Discrete - Probability - Standard - Distributions - Hypothesis - Testing - Correlation - Linear - Regression.

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