MultivariateTDistribution (Built-in Mathematica Symbol)

MultivariateTDistribution[\[CapitalSigma], \[Nu]] represents the multivariate Student t distribution with scale matrix \[CapitalSigma] and degrees of freedom parameter ...

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 + \ ...)) ...

PoissonDistribution (Built-in Mathematica Symbol)

PoissonDistribution[\[Mu]] represents a Poisson distribution with mean \[Mu].

ProbabilityPlot (Built-in Mathematica Symbol)

ProbabilityPlot[list] generates a plot of the CDF of list against the CDF of a normal distribution.ProbabilityPlot[dist] generates a plot of the CDF of the distribution dist ...

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 + \ ...

Data Formats in Wolfram|Alpha (Mathematica Tutorial)

In addition to its graphical results, Wolfram|Alpha can provide alternative representations that contain additional information or are well suited to particular tasks. These ...

Introduction to Control Objects (Mathematica Tutorial)

Mathematica includes many controls and structures related to controls as part of its core language. These control objects are supported in a completely seamless way ...

NormalDistribution (Built-in Mathematica Symbol)

NormalDistribution[\[Mu], \[Sigma]] represents a normal (Gaussian) distribution with mean \[Mu] and standard deviation \[Sigma].NormalDistribution[] represents a normal ...

LogPlot (Built-in Mathematica Symbol)

LogPlot[f, {x, x_min, x_max}] generates a log plot of f as a function of x from x_min to x_max. LogPlot[{f_1, f_2, ...}, {x, x_min, x_max}] generates log plots of several ...

Termination Conditions (Mathematica Tutorial)

Mathematically, sufficient conditions for a local minimum of a smooth function are quite straightforward: x^* is a local minimum if ∇f(x^*)=0 and the Hessian ∇^2f(x^*) is ...