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 + \ ...
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 ...
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 ...
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 ...
Tensors are mathematical objects that give generalizations of vectors and matrices. In Mathematica, a tensor is represented as a set of lists, nested to a certain number of ...
Correlation[v_1, v_2] gives the correlation between the vectors v_1 and v_2.Correlation[m] gives the correlation matrix for the matrix m.Correlation[m_1, m_2] gives the ...
FittedModel[...] represents the symbolic fitted model obtained from functions like LinearModelFit.
TotalVariation[matrix] gives the total variation for matrix.
Mathematica's descriptive statistics functions operate both on explicit data and on symbolic representations of statistical distributions. When operating on explicit data, ...