Statistics and Probability
Gives functionality from Basic Data, Discrete Probability, Standard Possibility Distributions, Hypothesis Testing, Relationship and Linear Regression.
Data Module
The Statistics component incorporates evaluation procedures associated with standard quantitative measures associated with centrality (mean) and distribution of (discrete) numerical units. This module incorporates measured averages, geometric mean, Inter-Quartile range, mean and regular deviation, sample variance as well as the coefficient of variation.
Under the radar Probability Module
The Under the radar Probability module encapsulates the particular foundations of discrete possibility and discrete probability distributions. This component includes the particular addition law, conditional possibility, cumulative distribution function, just mean and variance of the distribution, expected values, covariance and simplification of expression involving random variables.
Relationship and Regression Module
Enables you investigate relationships among two variables. These obtaining may be used to predict one adjustable from your given values associated with other variables. We protect linear (Spearmans, t-test, z-transform) and rank (Spearmans, Kendalls) correlation, linear regression plus conditional means.
Standard Possibility Distributions Module
This component assists in the advancement of applications that include the Binomial, Poisson, Regular, Lognormal, Pareto, Uniform, Hypergeometric and Exponential probability distributions. The probability density functionality, cumulative distribution function plus inverse, mean, variance, Skewness and Kurtosis are applied where appropriate and/or their own approximations for each submission. We also offer strategies which randomly generate figures from the given distribution.
Contour Fitting Module
The Contour Fitting module offers methods through which linear and non-linear functions can be installed in accordance using the minimum squares approach to the data set which might or may not show measurement errors. We furthermore include functionality which works ANNOVA type analysis which includes goodness-of-fit measures like the R-Squared measure and T-Test figure.
Confidence Intervals and Speculation Testing Module
In this element we present two elements of inferential statistics referred to as confidence intervals and speculation testing.
|
WebCab Probability and Stat (J2SE Ed.)
Downloads
: 211
