ZetaZero[k] represents the k\[Null]^th zero of the Riemann zeta function on the critical line.ZetaZero[k, t] represents the k\[Null]^th zero with imaginary part greater than ...
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 ...
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 ...
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 ...
Different measures of distance or similarity are convenient for different types of analysis. Mathematica provides built-in functions for many standard distance measures, as ...
Directly integrated into Mathematica's uniform architecture for handling lists of data is an array of highly optimized algorithms for transforming and smoothing datasets that ...
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 + \ ...
Mathematica integrates many aspects of statistical data analysis, from getting and exploring data to building high-quality models and and deducing consequences. Mathematica ...