Statistics

===Statistical computing===

[[File:Gretl screenshot.png|thumb|upright=1.15|right|[[gretl]], an example of an [[List of open source statistical packages|open source statistical package]]]]

{{main|Computational statistics}}

The rapid and sustained increases in computing power starting from the second half of the 20th century have had a substantial impact on the practice of statistical science. Early statistical models were almost always from the class of [[linear model]]s, but powerful computers, coupled with suitable numerical [[algorithms]], caused an increased interest in [[Nonlinear regression|nonlinear models]] (such as [[Artificial neural network|neural networks]]) as well as the creation of new types, such as [[generalized linear model]]s and [[multilevel model]]s.

Increased computing power has also led to the growing popularity of computationally intensive methods based on [[Resampling (statistics)|resampling]], such as [[permutation test]]s and the [[Bootstrapping (statistics)|bootstrap]], while techniques such as [[Gibbs sampling]] have made use of [[Bayesian model]]s more feasible. The computer revolution has implications for the future of statistics with a new emphasis on "experimental" and "empirical" statistics. A large number of both general and special purpose [[List of statistical packages|statistical software]] are now available. Examples of available software capable of complex statistical computation include programs such as [[Mathematica]], [[SAS (software)|SAS]], [[SPSS]], and [[R (programming language)|R]].