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WebCab Probability and Stat for Delphi 3.6

Statistics, Discrete Prob, Distributions, Hypo. testing, Correlation,Regression.


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For WebCab Probability and Stat for Delphi 3.6Publisher's description


WebCab Probability and Stat for Delphi is a Business software developed by WebCab Components. After our trial and test, the software is proved to be official, secure and free. Here is the official description for WebCab Probability and Stat for Delphi:

EditByBrothersoft: This suite consists of six packages: Statistics, Discrete Probability, Standard Probability Distributions, Curve Fitting, Hypothesis Testing, and Correlation & Regression which offer the following functionality.

Statistics Module
The Statistics module incorporates topic from data presentation (incl. standard, relative and cumulative Frequency tables), Basic Statistics (incl. measure of centrality, dispersion and relative location), Grouped Data (incl. Sample Mean, Variance and Standard Deviation) and Quality Control(incl. R-Chart, S-Chart and Median Chart).

Discrete Probability Module
The Discrete Probability module encapsulates the probabilistic study of finite set of events (i.e. discrete probability) and experiments with a finite number of outcomes (i.e. discrete random variables). Including: probability measures, union/intersection law, conditionals/complementary probability; cumulative distribution functions, mean/variance/expected return of Random Variable.

Standard Probability Distributions Module
This module assists in the development of applications that incorporate the Binomial, Poisson, Normal, Lognormal, Pareto, Uniform, Hypergeometric, Weibull 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
Allows the fitting of linear and non-linear functions for a data set which may or may not exhibit measurement errors in accordance with the least squares approach. A general linear Algorithm and the specialized Levenberg-Marquardt algorithm to handle the non-linear case are provided. We also include functionality which performs ANNOVA type Analysis including goodness-of-fit measures such as the R-Squared measure and T-Test statistic.
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