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Univariate distribution is a dispersal type of a single random variable described either with a probability mass function (pmf) for discrete probability distribution, or probability density function (pdf) for continuous probability distribution. [14] It is not to be confused with multivariate distribution.
In statistics, a unit root test tests whether a time series variable is non-stationary and possesses a unit root.The null hypothesis is generally defined as the presence of a unit root and the alternative hypothesis is either stationarity, trend stationarity or explosive root depending on the test used.
Continuous uniform distribution. One of the simplest examples of a discrete univariate distribution is the discrete uniform distribution, where all elements of a finite set are equally likely.
In mathematics, a univariate object is an expression, equation, function or polynomial involving only one variable.Objects involving more than one variable are multivariate.
In probability theory and statistics, the multivariate normal distribution, multivariate Gaussian distribution, or joint normal distribution is a generalization of the one-dimensional normal distribution to higher dimensions.
Simple linear regression is a statistical method used to model the linear relationship between an independent variable and a dependent variable.