ListLinePlot[{y_1, y_2, ...}] plots a line through a list of values, assumed to correspond to x coordinates 1, 2, .... ListLinePlot[{{x_1, y_1}, {x_2, y_2}, ...}] plots a ...

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 ...

Plot[f, {x, x_min, x_max}] generates a plot of f as a function of x from x_min to x_max. Plot[{f_1, f_2, ...}, {x, x_min, x_max}] plots several functions f_i.

Reduce[expr, vars] reduces the statement expr by solving equations or inequalities for vars and eliminating quantifiers. Reduce[expr, vars, dom] does the reduction over the ...

Mathematica can run parallel kernels in a number of different ways; locally on the same machine or remote on other machines connected in a network. Furthermore, the network ...

ListPlot[{y_1, y_2, ...}] plots points corresponding to a list of values, assumed to correspond to x coordinates 1, 2, .... ListPlot[{{x_1, y_1}, {x_2, y_2}, ...}] plots a ...

Plot3D[f, {x, x_min, x_max}, {y, y_min, y_max}] generates a three-dimensional plot of f as a function of x and y. Plot3D[{f_1, f_2, ...}, {x, x_min, x_max}, {y, y_min, ...

This loads packages containing some test problems and utility functions. One of the first and simplest methods for solving initial value problems was proposed by Euler: ...

DistributionFitTest[data] tests whether data is normally distributed. DistributionFitTest[data, dist] tests whether data is distributed according to dist. ...

Extrapolation methods are a class of arbitrary-order methods with automatic order and step-size control. The error estimate comes from computing a solution over an interval ...