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StatAssist

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Version:2.0
Price ($ US): $49.00
Average rating: 0
File size: 1.57 (Mb)
License: Shareware
Downloaded: 269 Times!
Last updated: 11/6/2006
Release date:9/1/2006
Platform (OS): Windows 98,
Windows NT4.0,
Windows Server 2000,
Windows Server2003,
Windows ME,
Windows XP
Minimum Requirements: 32 MB of RAM
Functional limitations: Limited number of distributions.

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StatAssist displays graphs and related properties of more than 40 probability distributions. This application will save you time and prevent calculation errors if your work or study involves the use of continuous or discrete distributions. It allows to quickly select a distribution of interest, specify parameter values, and view the graphs - probability density, cumulative probability, survival, hazard, and cumulative hazard. You can easily export the results to various formats, or print them. With StatAssist, you don't need complex statistical tables - it calculates moments, quantiles, and tail probabilities as you modify distribution parameters. In addition, it includes an efficient random number generator, a visual distribution gallery, and a comprehensive on-line documentation. Supported distribution properties: min, max, mode, mean, variance, standard deviation, coef. of variation, skewness, and kurtosis. Supported distributions: Bernoulli, Beta, Binomial, Cauchy (Lorentz), Chi-Squared, Discrete Uniform, Erlang, Error Function, Exponential, F Distribution, Fatigue Life (Birnbaum-Saunders), Frechet, Gamma, Generalized Extreme Value (GEV), Generalized Logistic, Generalized Pareto, Geometric, Gumbel Max, Gumbel Min, Hypergeometric, Inverse Gaussian, Johnson SB, Johnson SU, Laplace (Double Exponential), Logarithmic, Logistic, Lognormal, Negative Binomial, Normal, Pareto, Pert, Phased Bi-Exponential, Phased Bi-Weibull, Poisson, Power Function, Rayleigh, Student's t, Triangular, Uniform, Wakeby, Weibull.

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EasyFit
Fit over 40 probability distributions to your data and select the best model in seconds. Reduce your analysis times and make better decisions by applying the state-of-the-art data fitting methods, goodness of fit tests, and high-quality graphs.
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