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In statistics, a generalized linear model (GLM) is a flexible generalization of ordinary linear regression.The GLM generalizes linear regression by allowing the linear model to be related to the response variable via a link function and by allowing the magnitude of the variance of each measurement to be a function of its predicted value.
For very small samples the multinomial test for goodness of fit, and Fisher's exact test for contingency tables, or even Bayesian hypothesis selection are preferable to the G-test. [2] McDonald recommends to always use an exact test (exact test of goodness-of-fit, Fisher's exact test) if the total sample size is less than 1 000 .
In mathematics, the Kuratowski–Ryll-Nardzewski measurable selection theorem is a result from measure theory that gives a sufficient condition for a set-valued function to have a measurable selection function. [1] [2] [3] It is named after the Polish mathematicians Kazimierz Kuratowski and Czesław Ryll-Nardzewski. [4]
In statistics, a generalized estimating equation (GEE) is used to estimate the parameters of a generalized linear model with a possible unmeasured correlation between observations from different timepoints. [1] [2]
where Γ is the gamma function and I is the regularized incomplete beta function. Although there are other forms of the cumulative distribution function, the first form presented above is very easy to evaluate through recursive computing. [1] In statistical software R, the cumulative distribution function is implemented as pt.
A generalized chi-square variable or distribution can be parameterized in two ways. The first is in terms of the weights w i {\displaystyle w_{i}} , the degrees of freedom k i {\displaystyle k_{i}} and non-centralities λ i {\displaystyle \lambda _{i}} of the constituent non-central chi-squares, and the coefficients s {\displaystyle s} and m ...
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In statistics, generalized least squares (GLS) is a method used to estimate the unknown parameters in a linear regression model.It is used when there is a non-zero amount of correlation between the residuals in the regression model.