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In statistics, a sequence of random variables is homoscedastic (/ ˌ h oʊ m oʊ s k ə ˈ d æ s t ɪ k /) if all its random variables have the same finite variance; this is also known as homogeneity of variance. The complementary notion is called heteroscedasticity, also known as heterogeneity of variance.
In statistics, a sequence of random variables is homoscedastic (/ ˌ h oʊ m oʊ s k ə ˈ d æ s t ɪ k /) if all its random variables have the same finite variance; this is also known as homogeneity of variance. The complementary notion is called heteroscedasticity, also known as heterogeneity of variance.
In statistics, Bartlett's test, named after Maurice Stevenson Bartlett, [1] is used to test homoscedasticity, that is, if multiple samples are from populations with equal variances. [2] Some statistical tests, such as the analysis of variance, assume that variances are equal across groups or samples, which can be checked with Bartlett's test.
The Lagrange multiplier (LM) test statistic is the product of the R 2 value and sample size: LM = n R 2 . {\displaystyle {\text{LM}}=nR^{2}.} This follows a chi-squared distribution , with degrees of freedom equal to P − 1, where P is the number of estimated parameters (in the auxiliary regression).
In solving mathematical equations, particularly linear simultaneous equations, differential equations and integral equations, the terminology homogeneous is often used for equations with some linear operator L on the LHS and 0 on the RHS. In contrast, an equation with a non-zero RHS is called inhomogeneous or non-homogeneous, as exemplified by ...
Statistical testing for a non-zero heterogeneity variance is often done based on Cochran's Q [13] or related test procedures. This common procedure however is questionable for several reasons, namely, the low power of such tests [14] especially in the very common case of only few estimates being combined in the analysis, [15] [7] as well as the specification of homogeneity as the null ...
Simpson's paradox is a phenomenon in probability and statistics in which a trend appears in several groups of data but disappears or reverses when the groups are combined. This result is often encountered in social-science and medical-science statistics, [ 1 ] [ 2 ] [ 3 ] and is particularly problematic when frequency data are unduly given ...
In this case, the beta-binomial model distribution is a popular and analytically tractable alternative model to the binomial distribution since it provides a better fit to the observed data. [2] To capture the heterogeneity of the families, one can think of the probability parameter of the binomial model (say, probability of being a boy) is ...