components-of-variance model
1Model selection — is the task of selecting a statistical model from a set of candidate models, given data. In the simplest cases, a pre existing set of data is considered. However, the task can also involve the design of experiments such that the data collected is …
2Random effects model — In statistics, a random effect(s) model, also called a variance components model is a kind of hierarchical linear model. It assumes that the data describe a hierarchy of different populations whose differences are constrained by the hierarchy. In …
3Cosmic variance — For the weblog, see Cosmic Variance (blog). Physical cosmology Universe …
4Baddeley's model of working memory — Schematic of Baddeley s Model Alan Baddeley and Graham Hitch proposed a model of working memory in 1974, in an attempt to describe a more accurate model of short term memory. Baddeley Hitch proposed their tripartite working memory model as an… …
5Mixture model — See also: Mixture distribution In statistics, a mixture model is a probabilistic model for representing the presence of sub populations within an overall population, without requiring that an observed data set should identify the sub population… …
6Analysis of variance — In statistics, analysis of variance (ANOVA) is a collection of statistical models, and their associated procedures, in which the observed variance in a particular variable is partitioned into components attributable to different sources of… …
7Generalized linear model — In statistics, the generalized linear model (GLM) is a flexible generalization of ordinary least squares regression. It relates the random distribution of the measured variable of the experiment (the distribution function ) to the systematic (non …
8Global climate model — AGCM redirects here. For Italian competition regulator, see Autorità Garante della Concorrenza e del Mercato. Climate models are systems of differential equations based on the basic laws of physics, fluid motion, and chemistry. To “run” a model,… …
9Principal components analysis — Principal component analysis (PCA) is a vector space transform often used to reduce multidimensional data sets to lower dimensions for analysis. Depending on the field of application, it is also named the discrete Karhunen Loève transform (KLT),… …
10Multivariate analysis of variance — (MANOVA) is a generalized form of univariate analysis of variance (ANOVA). It is used when there are two or more dependent variables. It helps to answer : 1. do changes in the independent variable(s) have significant effects on the dependent …
