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[Home]Analysis of variance

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Analysis of variance (ANOVA) is a collection of statistical models and their associated procedures which compare means by splitting the overall observed variance into different parts. There are three conceptual classes of such models:
  1. The fixed effects model assumes that the data come from normal populations which differ in their means.
  2. Random effects models assume that the data describe a hierarchy of different populations whose differences are constrained by the hierarchy.
  3. [Mixed models]? describe situations where both fixed and random effects are present.

The fundamental technique is a partitioning of the total sum of squares into components related to the effects in the model used. For example, we show the model for a simplified ANOVA with one type of treatment at different levels. (If the treatment levels are quantitative and the effects are linear, a linear regression analysis may be appropriate.)

The number of [degrees of freedom]? (abbreviated 'df') can be partitioned in a similar way and specifies the [Chi-squared distribution]? which describes the associated sums of squares.

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Last edited June 30, 2001 3:27 pm by Larry Sanger (diff)
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