Extreme Optimization Statistics Library for .NET

Extreme Optimization Statistics Library for .NET icon

Sorry, unfortunately, Extreme Optimization Statistics Library for .NET is no longer in our catalog because of its illegal activity.

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Publisher description

Descriptive Statistics - Measures of central tendency: mean, median, trimmed mean, harmonic mean, geometric mean. - Measures of scale: variance, standard deviation, range, interquartile range, absolute deviation from mean and median. - Higher moments: skewness, kurtosis. Probability Distributions - Probability density function (PDF). - Cumulative distribution function (CDF). - Percentile or inverse cumulative distribution function. - Moments: mean, variance, skewness and kurtosis. - Generate random samples from any distribution. Continuous Probability Distributions - Beta distribution. - Cauchy distribution. - Chi-squared distribution. - Erlang distribution. - Exponential distribution. - F distribution. - Gamma distribution. - Gumbel distribution. - Laplace distribution. - Logistic distribution. - Lognormal distribution. - Normal distribution. - Pareto distribution. - Rayleigh distribution. - Student t distribution. - Triangular distribution. - Uniform distribution. - Weibull distribution. Discrete Probability Distributions - Bernoulli distribution. - Binomial distribution. - Geometric distribution. - Hypergeometric distribution. - Negative binomial distribution. - Poisson distribution. - Uniform distribution. Histograms - One-dimensional histograms. - Probability distribution associated with a histogram. Statistical tests - Tests for the mean: one sample z-test, one sample t-test. - Paired and unpaired two-sample t test for the difference between two sample means. - One sample chi-squared test for variance. - F-test for the ratio of two variances. - One and two sample Kolmogorov-Smirnov test. - Anderson-Darling test for normality. - Chi-squared goodness-of-fit test. - Bartlett and Levene tests for homogeneity of variances. General Linear Model - Infrastructure for General Linear Model and Generalized Linear Model calculations. - Analysis of variance. - Regression


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