ParametricPlot[{f_x, f_y}, {u, u_min, u_max}] generates a parametric plot of a curve with x and y coordinates f_x and f_y as a function of u. ParametricPlot[{{f_x, f_y}, ...

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 ...

Ticks::ticks

StreamDensityPlot[{{v_x, v_y}, s}, {x, x_min, x_max}, {y, y_min, y_max}] generates a stream plot of the vector field {v_x, v_y} as a function of x and y, superimposed on a ...

The single command Manipulate lets you create an astonishing range of interactive applications with just a few lines of input. Manipulate is designed to be used by anyone who ...

NIntegrate[f, {x, x_min, x_max}] gives a numerical approximation to the integral \[Integral]_x_min^x_max\ f\ d \ x. NIntegrate[f, {x, x_min, x_max}, {y, y_min, y_max}, ...] ...

Plots of data based on measurements often have vertical lines or intervals centered at the points to indicate the associated error estimates. Mathematica lets you add such ...

BetaDistribution[\[Alpha], \[Beta]] represents a continuous beta distribution with shape parameters \[Alpha] and \[Beta].

EstimatedDistribution[data, dist] estimates the parametric distribution dist from data.EstimatedDistribution[data, dist, {{p, p_0}, {q, q_0}, ...}] estimates the parameters ...

SmoothDensityHistogram[{{x_1, y_1}, {x_2, y_2}, ...}] plots a smooth kernel histogram of the values {x_i, y_i}.SmoothDensityHistogram[{{x_1, y_1}, {x_2, y_2}, ...}, espec] ...