SurvivalDistribution[{e_1, e_2, ...}] represents a survival distribution with event times e_i.SurvivalDistribution[{w_1, w_2, ...} -> {e_1, e_2, ...}] represents a survival ...
MardiaCombinedTest[data] tests whether data follows a MultinormalDistribution using the Mardia combined test.MardiaCombinedTest[data, " property"] returns the value of " ...
Basic descriptive statistics operations. Given a list with n elements x_i, the mean Mean[list] is defined to be μ(x)OverscriptBox[x, _]∑x_i/n. The variance Variance[list] ...
MardiaSkewnessTest[data] tests whether data follows a MultinormalDistribution using the Mardia skewness test.MardiaSkewnessTest[data, " property"] returns the value of " ...
Mathematica's sophisticated algorithms for handling higher mathematical functions to arbitrary precision—and in symbolic form—immediately brings a new level of accuracy—and ...
Hypothesis tests give quantitative answers to common questions, such as how good the fit is between data and a particular distribution, whether these distributions have the ...
Based on original algorithms developed at Wolfram Research, Mathematica's core randomness generation is both highly efficient and of exceptional quality. Mathematica can ...
The ability to generate pseudorandom numbers is important for simulating events, estimating probabilities and other quantities, making randomized assignments or selections, ...
In many practical situations it is convenient to consider limits in which a fixed amount of something is concentrated into an infinitesimal region. Ordinary mathematical ...
ParallelProduct[expr, {i, i_max}] evaluates the product \[Product]i = 1 i_max expr in parallel.ParallelProduct[expr, {i, i_min, i_max}] starts with i = ...