Statistical Model Analysis (Mathematica Tutorial)

When fitting models to data, it is often useful to analyze how well the model fits the data and how well the fitting meets the assumptions of the model. For a number of ...

Real Polynomial Systems (Mathematica Tutorial)

A real polynomial system is an expression constructed with polynomial equations and inequalities combined using logical connectives and quantifiers and

GraphicsComplex (Built-in Mathematica Symbol)

GraphicsComplex[{pt_1, pt_2, ...}, data] represents a graphics complex in which coordinates given as integers i in graphics primitives in data are taken to be pt_i.

Text (Built-in Mathematica Symbol)

Text[expr] displays with expr in plain text format. Text[expr, coords] is a graphics primitive that displays the textual form of expr centered at the point specified by ...

Lie Symmetry Methods for Solving ... (Mathematica Tutorial)

Around 1870, Marius Sophus Lie realized that many of the methods for solving differential equations could be unified using group theory. Lie symmetry methods are central to ...

Introduction (TetGenLink Tutorial)

TetGen is a quality tetrahedral mesh generator and a three-dimensional Delaunay triangulator. It is used by Mathematica for various operations, such as interpolation in ...

NonlinearModelFit (Built-in Mathematica Symbol)

NonlinearModelFit[{y_1, y_2, ...}, form, {\[Beta]_1, ...}, x] constructs a nonlinear model with structure form that fits the y_i for successive x values 1, 2, ... using the ...

NLimit (Numerical Calculus Package Symbol)

NLimit[expr, z -> z_0] numerically finds the limiting value of expr as z approaches z_0.

LogitModelFit (Built-in Mathematica Symbol)

LogitModelFit[{y_1, y_2, ...}, {f_1, f_2, ...}, x] constructs a binomial logistic regression model of the form 1/(1 + E -(\[Beta]_0 + \[Beta]_1 f_1 + \[Beta]_2 f_2 + \ ...)) ...

ProbitModelFit (Built-in Mathematica Symbol)

ProbitModelFit[{y_1, y_2, ...}, {f_1, f_2, ...}, x] constructs a binomial probit regression model of the form 1/2 (1 + erf((\[Beta]_0 + \[Beta]_1 f_1 + \[Beta]_2 f_2 + \ ...