Search Results for "interval"

Computing similarity factor (f2) bootstrap bias corrected and accelerated confidence interval

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,...

ICE is a simple mathematical calculator with very useful shortcuts for Engineer: works natively with tolerance, can run Monte Carlo analysis, uses engineer notation, includes most of the day by day functions used by engineers.

1/3 is not 0.33. Pi is not 3.14. Actually 1/3 is in interval [0.33, 0.34] and pi is in interval [3.14, 3.15]. During computation truncating error is accumulating. At end of computation it is needed to know real boundaries of result. The solution is given by interval arithmetic. Simply speaking, because 1/3 is in interval [0.33, 0.34] and 1/7 is in interval [0.14, 0.15], value 1/3 - 1/7 must be in [0.18, 0.2].

...Mixed models partition mortality into exogenous and endogenous components, so that the intrinsic survivorship can be estimated without the interference from extrinsic noise. DeDAY supports both interval-censored data and exact event-time data. Using MLE (Maximum Likelihood Estimate), DeDAY fits statistic model to the data. DeDAY also calculates the variances and the multi-dimensional confidence limits of model parameters. DeDAY is free for academic users.

Python extension module based on C-XSC (C++ Language eXtensions for Scientific Computation) and the associated Toolbox. Provides a real and complex interval arithmetic with verified results for numerical applications.

Interval Analysis is a collection of interval analysis tools, allowing for robust root-finding and tracking of bounds of numerical error due to uncertain inputs and floating-point error.