JacobiSD (Built-in Mathematica Symbol)

JacobiSD[u, m] gives the Jacobi elliptic function sd(u | m).

Mean (Built-in Mathematica Symbol)

Mean[list] gives the statistical mean of the elements in list. Mean[dist] gives the mean of the symbolic distribution dist.

Projection Method for NDSolve (Mathematica Tutorial)

When a differential system has a certain structure, it is advantageous if a numerical integration method preserves the structure. In certain situations it is useful to solve ...

PearsonChiSquareTest (Built-in Mathematica Symbol)

PearsonChiSquareTest[data] tests whether data is normally distributed using the Pearson \[Chi]^2 test.PearsonChiSquareTest[data, dist] tests whether data is distributed ...

ListLogPlot (Built-in Mathematica Symbol)

ListLogPlot[{y_1, y_2, ...}] makes a log plot of the y_i, assumed to correspond to x coordinates 1, 2, ....ListLogPlot[{{x_1, y_1}, {x_2, y_2}, ...}] makes a log plot of the ...

Introduction to Graph Drawing (Mathematica Tutorial)

Mathematica provides functions for the aesthetic drawing of graphs. Algorithms implemented include spring embedding, spring-electrical embedding, high-dimensional embedding, ...

StateFeedbackGains (Built-in Mathematica Symbol)

StateFeedbackGains[ss, {p_1, p_2, ..., p_n}] gives the state feedback gain matrix for the StateSpaceModel object ss such that the poles of the closed-loop system are p_i.

MardiaCombinedTest (Built-in Mathematica Symbol)

MardiaCombinedTest[data] tests whether data follows a MultinormalDistribution using the Mardia combined test.MardiaCombinedTest[data, " property"] returns the value of " ...

Compiling Mathematica Expressions (Mathematica Tutorial)

If you make a definition like f[x_]:=x Sin[x], Mathematica will store the expression x Sin[x] in a form that can be evaluated for any x. Then when you give a particular value ...

GeneralizedLinearModelFit (Built-in Mathematica Symbol)

GeneralizedLinearModelFit[{y_1, y_2, ...}, {f_1, f_2, ...}, x] constructs a generalized linear model of the form g -1 (\[Beta]_0 + \[Beta]_1 f_1 + \[Beta]_2 f_2 + ...) that ...