SYSTAT is a powerful statistical software that has every statistical procedure you need to carry out efficient statistical analysis of your data. It provides you with features from the most elementary descriptive statistics to very advanced statistical methodology based on sophisticated algorithms. If you are a novice statistical user, you can work with its friendly and simple menu-dialog. If you are a statistically-savvy user, you might prefer to use its intuitive command language, and analyze your data swiftly and with ease. In either case, you can exploit its staggering range of powerful techniques to analyze many types of data to answer many types of questions. You can carry out very comprehensive analysis of univariate and multivariate data based on linear, general linear, and mixed linear models. You can carry out different types of robust regression analysis when your data are not suitable for conventional multiple regression analysis. You can also compute partial least-squares regression. You can design experiments, carry out power analysis, do probability calculations on a whole lot of distributions and fit them to data. You can perform matrix computations. Anything else you may need---Time Series, Survival Analysis, Response Surface Optimization, Spatial Statistics, Test Item Analysis, Cluster Analysis, Classification and Regression Trees, Correspondence Analysis, Multidimensional Scaling, Conjoint Analysis, Quality Analysis, Path Analysis, etc., etc.---SYSTAT has them all.

SYSTAT's Monte Carlo module helps you accomplish your simulation tasks

You can use the powerful Mersenne-Twister random number generator for your bootstrap and simulation tasks. You can exploit SYSTAT's random number generator from as many as 43 univariate and multivariate, discrete and continuous distributions for your Monte Carlo exercises. When the distributions are more complicated, use Rejection Sampling and Adaptive Rejection Sampling to draw random samples. For your complex Bayesian computations, suitably adapt SYSTAT's generic Markov chain Monte Carlo (MCMC) procedures like various types of Metropolis-Hastings and Gibbs Sampling features to draw random samples and to carry out Monte Carlo integration thereof.

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