Search Results for "tests"

...The library contains the density (pdf), cumulative (cdf), quantile, and random number generator (RNG) routines of the following distributions: Ansari, Beta, Binomial, Cauchy, Chi square, Exponential, Fisher's F, Gamma, Geometric, Hypergeometric, Kendall, Logistic, Log normal, Negative binomial, Noncentral beta, Noncentral chi square, Noncentral f, Noncentral t, Normal, Poisson, Sign Rank, Spearman, Student's T, Tukey, Uniform, Weibull, Wilcoxon, and many more. Normality tests, such as: Kolmogorov-Smirnov, Anderson-Darling, Cramer-Von Mises, D'Agostino-Pearson, Jarque Bera, Kolmogorov-Lilliefors, Shapiro-Francia, Shapiro-Wilk. And many others.

JStats is a small but powerful Java application/applet for conducting statistical tests. The following tests are supported: * Parametric tests: T-test, ANOVA, Repeated Measures ANOVA * Non-parametric tests: Wilcoxon Rank-Sum, Wilcoxon Signed-Ranks, Kruskal-Wallis, Friedman * Check if datasets are normally distributed: Jarque-Bera, Shapiro-Wilk * Check if datasets have equal variances: F-test, Bartlett's test, John, Nagao and Sugiura's test * Correlation: Correlation coefficient, Spearman Rank correlation, linear regression * Confidence intervals test * Outliers: Generalized Extreme Studentized (ESD) test, outliers in ANOVA The latest version is available as applet on http://aiguy.org/Statistics.html

Statcato is a Java software application for elementary statistics. Its features include data and graph generation, probability distributions, descriptive statistics, confidence intervals, hypothesis tests, correlation, regression, and analysis of var

Statastizard(c)2012 Nick Emblow is a program designed to guide its user into the right statistical test, perform the test, and report the results in a meaningful way. The idea of this is that the user won't actually need to know that they are using a mann-whitney U over a t-test, for example - it just tests for normality and homoscedastic of variance, and decides which test to use, then reports the results. Why? Because time after time, people come to me with some test that they've done that they think "proves" something - when in reality they've either not done a comparative test to start with (ex. a correlation), failed to test for confounding factors (such as normality etc) and as a result, failed to appropriately identify even the right test to do.