Version 6.0 greatly extended Mathematica's powerful symbolic document paradigm, integrating support for editable symbolic graphics, structure-programmable table layouts, ...

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 ...

Which[test_1, value_1, test_2, value_2, ...] evaluates each of the test_i in turn, returning the value of the value_i corresponding to the first one that yields True.

Mathematica's symbolic architecture makes it unprecedentedly easy to create and manipulate sophisticated layouts for user interfaces—both as static structures and with ...

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[{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 + \ ...

Huge numerical datasets are routine for Mathematica. Its powerful array primitives make large-scale array manipulation both easy to specify and highly efficient. And its ...

LinearModelFit[{y_1, y_2, ...}, {f_1, f_2, ...}, x] constructs a linear model of the form \[Beta]_0 + \[Beta]_1 f_1 + \[Beta]_2 f_2 + ... that fits the y_i for successive x ...

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 ...

Mathematica's dynamic interactivity system makes it easy to view and annotate any object in a dynamic way. Building on Mathematica's symbolic programming architecture, ...