Parametric statistics is a branch of statistics which leverages models based on a fixed (finite) set of parameters. Conversely nonparametric statistics...

than finite dimensional, as in parametric statistics. Nonparametric statistics can be used for descriptive statistics or statistical inference. Nonparametric...

particular parametric family of probability distributions. In that case, one speaks of non-parametric statistics as opposed to the parametric statistics just...

In statistics, a parametric model or parametric family or finite-dimensional model is a particular class of statistical models. Specifically, a parametric...

class of parametric models. Non-parametric: The assumptions made about the process generating the data are much less than in parametric statistics and may...

of a variable Parametric statistics, a branch of statistics that assumes data has come from a type of probability distribution Parametric derivative, a...

inferential statistics. The typical parameters are the expectations, variance, etc. Unlike parametric statistics, nonparametric statistics make no assumptions...

confidence intervals for Pearson's correlation coefficient. In the "non-parametric" bootstrap, n pairs (xi, yi) are resampled "with replacement" from the...

may have better type-1 error control than some non-parametric alternatives. Furthermore, non-parametric methods, such as the Mann-Whitney U test discussed...

significantly higher standard deviation (representing outliers). Robust parametric statistics can proceed in two ways: by designing estimators so that a pre-selected...

In statistics, the t-statistic is the ratio of the difference in a number’s estimated value from its assumed value to its standard error. It is used in...

a Population" (PDF). Technical Report SFB 386, No. 399, Department of Statistics, University of Munich. Retrieved 17 April 2016. Johnson, R. W. (Summer...

used to non-parametrically estimate the distribution of the error term. Bayesian linear regression applies the framework of Bayesian statistics to linear...

unit-treatment additivity. If the response variable is expected to follow a parametric family of probability distributions, then the statistician may specify...

the most fundamental tools in non-parametric statistics and inference. Important special cases of the order statistics are the minimum and maximum value...

distribution makes sampling difficult and invalidates commonly-used parametric statistics. A similar pattern is found among predators that search for their...

In statistics, biweight midcorrelation (also called bicor) is a measure of similarity between samples. It is median-based, rather than mean-based, thus...

In statistics, simple linear regression (SLR) is a linear regression model with a single explanatory variable. That is, it concerns two-dimensional sample...

In statistics, normality tests are used to determine if a data set is well-modeled by a normal distribution and to compute how likely it is for a random...

common in machine learning. In nonparametric statistics, a kernel is a weighting function used in non-parametric estimation techniques. Kernels are used in...

counterfactual post-hoc so that the use of the tools of parametric statistics is justified. Since parametric statistics depends on any observation following a Gaussian...

it renders parametric statistics as commonly used by biologists invalid. Log-transformation of data before the application of parametric test, or the...

see. Analysis of data obtained by ranking commonly requires non-parametric statistics. It is not always possible to assign rankings uniquely. For example...

Parameter identification problem Parameter space Parametric family Parametric model Parametric statistics Pareto analysis Pareto chart Pareto distribution...

order, thereby easing the move to order-based inference and non-parametric statistics. To construct a stem-and-leaf display, the observations must first...

completely constructed using information derived from the data. That is, no parametric equation is assumed for the relationship between predictors and dependent...

nonparametric and parametric statistics SuperCROSS – comprehensive statistics package with ad-hoc, cross tabulation analysis Systat – general statistics package...

In statistics, D'Agostino's K2 test, named for Ralph D'Agostino, is a goodness-of-fit measure of departure from normality, that is the test aims to gauge...

In non-parametric statistics, the Theil–Sen estimator is a method for robustly fitting a line to sample points in the plane (simple linear regression)...

In statistics, ordinary least squares (OLS) is a type of linear least squares method for choosing the unknown parameters in a linear regression model (with...