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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.
Homogeneity and heterogeneity; only ' b ' is homogeneous Homogeneity and heterogeneity are concepts relating to the uniformity of a substance, process or image.A homogeneous feature is uniform in composition or character (i.e., color, shape, size, weight, height, distribution, texture, language, income, disease, temperature, radioactivity, architectural design, etc.); one that is heterogeneous ...
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.
A heterogeneous taxon, a taxon that contains a great variety of individuals or sub-taxa; usually this implies that the taxon is an artificial grouping; Genetic heterogeneity, multiple origins causing the same disorder in different individuals. Allelic heterogeneity, different mutations at the same locus causing the same disorder.
In chemistry, the dispersity is a measure of the heterogeneity of sizes of molecules or particles in a mixture. A collection of objects is called uniform if the objects have the same size, shape, or mass.
In information theory and statistics, negentropy is used as a measure of distance to normality. [4] [5] [6] Out of all distributions with a given mean and variance, the normal or Gaussian distribution is the one with the highest entropy.
Comproportionation or symproportionation is a chemical reaction where two reactants containing the same element but with different oxidation numbers, form a compound having an intermediate oxidation number. It is the opposite of disproportionation. [1]
In statistics, overdispersion is the presence of greater variability (statistical dispersion) in a data set than would be expected based on a given statistical model. A common task in applied statistics is choosing a parametric model to fit a given set of empirical observations.