Analysis of variance

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.)

• SS_Total = SS_Error + SS_Treatments

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.

• df_Total = df_Error + df_Treatments

back to Statistics