Random Number Generation (Mathematica Tutorial)

The ability to generate pseudorandom numbers is important for simulating events, estimating probabilities and other quantities, making randomized assignments or selections, ...

Expectation (Built-in Mathematica Symbol)

Expectation[expr, x \[Distributed] dist] gives the expectation of expr under the assumption that x follows the probability distribution dist. Expectation[expr, x ...

ListPlot (Built-in Mathematica Symbol)

ListPlot[{y_1, y_2, ...}] plots points corresponding to a list of values, assumed to correspond to x coordinates 1, 2, .... ListPlot[{{x_1, y_1}, {x_2, y_2}, ...}] plots a ...

DiscreteRatio (Built-in Mathematica Symbol)

DiscreteRatio[f, i] gives the discrete ratio f(i + 1)/f(i).DiscreteRatio[f, {i, n}] gives the multiple discrete ratio.DiscreteRatio[f, {i, n, h}] gives the multiple discrete ...

TruncatedDistribution (Built-in Mathematica Symbol)

TruncatedDistribution[{x_min, x_max}, dist] represents the distribution obtained by truncating the values of dist to lie between x_min and ...

CDF (Built-in Mathematica Symbol)

CDF[dist, x] gives the cumulative distribution function for the symbolic distribution dist evaluated at x.CDF[dist, {x_1, x_2, ...}] gives the multivariate cumulative ...

TransformedDistribution (Built-in Mathematica Symbol)

TransformedDistribution[expr, x \[Distributed] dist] represents the transformed distribution of expr where the random variable x follows the distribution ...

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 + \ ...)) ...

PDF (Built-in Mathematica Symbol)

PDF[dist, x] gives the probability density function for the symbolic distribution dist evaluated at x.PDF[dist, {x_1, x_2, ...}] gives the multivariate probability density ...

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 + \ ...