EasyFit allows to easily and quickly select the probability distribution which best fits to data, reducing your analysis times by 70-95% over manual methods. It includes numerous features designed to save you time, prevent analysis errors, and help you make better decisions.
An integrated environment provided by EasyFit includes data management, analysis, and reporting capabilities ensuring the high quality of your projects. It is very easy to learn and use - requires only a basic knowledge of statistics, so you can master it in a day or two.
The key feature of EasyFit is the ability to automatically fit over 40 distributions to sample data. Advanced users can also apply the flexible manual fitting capability. After the distributions are fitted, they are ranked accordingly to the goodness of fit tests (Kolmogorov-Smirnov, Anderson-Darling, Chi-Squared) allowing you to select the best model.
A variety of high-quality graphs (probability density, cumulative probability, survival, hazard, P-P plot etc.) can be used to visually compare the fitted distributions and make sure you have chosen the most valid one. Finally, you can apply the integrated StatAssist tool to get a comprehensive information (moments, quantiles, probabilities) on the selected distribution.
Additional features include hypertext reports, random number generator, and visual distribution gallery.
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, 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.
Keywords: distribution fitting probability fit goodness density cumulative graphs statistics CDF PDF reliability survival hazard failure exponential normal lognormal weibull gamma laplace beta chi rayleigh extreme gumbel logistic pareto student johnson darling |
