# Definition of Anova

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**Anova**Definition from Business & Finance Dictionaries & Glossaries

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A basic statistical technique for analyzing experimental data. It subdivides the total variation of a data set into meaningful component parts associated with specific sources of variation in order to test a hypothesis on the parameters of the model or to estimate variance components. There are three models: fixed, random and mixed.

Raynet Business & Marketing Glossary

a method of analysis for determining the level of statistical significance of differences among the means of two or more

a research statistical technique for examining the differences among means for two or more populations.

Copyright © 2001, Ray Wright**Anova**Definition from Government Dictionaries & Glossaries

**Anova**Definition from Science & Technology Dictionaries & Glossaries

Electronic Statistics Textbook

The purpose of

For more information, see the ANOVA/MANOVA chapter.

*analysis of variance*(*ANOVA*) is to test for significant differences between means by comparing (i.e., analyzing) variances. More specifically, by partitioning the total variation into different sources (associated with the different effects in the design), we are able to compare the variance due to the between-groups (or treatments) variability with that due to the within-group (treatment) variability. Under the null hypothesis (that there are no mean differences between groups or treatments in the population), the variance estimated from the within-group (treatment) variability should be about the same as the variance estimated from between-groups (treatments) variability.For more information, see the ANOVA/MANOVA chapter.

Common Concepts in Statistics

A test for significant differences between means by comparing variances. It concerns a normally distributed response (outcome) variable and a single categorical explanatory (predictor) variable which represents treatments or groups. ANOVA is a special case of multiple regression where indicator variables (or orthogonal polynomials) are used to describe the discrete levels of factor variables. The term analysis of variance refers not to the model but to the method of determining which effects are statistically significant. Major assumptions of ANOVA are the normality of the response variable (the response variable should be normally distributed within each group), and homogeneity of variances (it is assumed that the variances in the different groups of the design are equal). Under the null hypothesis (that there are no mean differences between groups or treatments in the population), the variance estimated from the within-group (treatment) random variability (

**residual sum of squares**= RSS) should be about the same as the variance estimated from between-groups (treatments) variability (**explained sum of squares**= ESS). If the null hypothesis is true, there should be no difference between within and between groups variability, and their ratio (variance ratio), mean ESS / mean RSS should be equal to 1. This is known as the**F test**or variance ratio test (see also**one-way**and**two-way ANOVA**). The ANOVA approach is based on the partitioning of sums of squares and degrees of freedom associated with the response variable. ANOVA interpretations of main effects and interactions are not so obvious in other regression models. An accumulated ANOVA table reports the results from fitting a succession of regression models to data from a factorial experiment. Each main effect is added on to the constant term followed by the interaction(s). At each level an F test result is also reported showing the extra effect of adding each variable so it can be worked out which model would fit best. In a two-way ANOVA with equal replications, the order of terms added to the model does not matter, whereas, this is not the case if there are unequal replications. When the assumptions of ANOVA are not met, its non-parametric equivalent**Kruskal-Wallis test**may be used. (A tutorial on ANOVA )**Anova**Definition from Computer & Internet Dictionaries & Glossaries

Computer Abbreviations v1.5

Analysis Of Variance

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Analysis Of Variance

**Anova**Definition from Encyclopedia Dictionaries & Glossaries

English Wikipedia - The Free Encyclopedia

**Analysis of variance**(

**ANOVA**) is a collection of statistical models used to analyze the differences among group means and their associated procedures (such as "variation" among and between groups), developed by statistician and evolutionary biologist Ronald Fisher. In the ANOVA setting, the observed variance in a particular variable is partitioned into components attributable to different sources of variation. In its simplest form, ANOVA provides a statistical test of whether or not the means of several groups are equal, and therefore generalizes the

*t*-test to more than two groups. ANOVAs are useful for comparing (testing) three or more means (groups or variables) for statistical significance. It is conceptually similar to multiple two-sample t-tests, but is less conservative (results in less type I error) and is therefore suited to a wide range of practical problems.

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^{®}**Anova**Definition from Medicine Dictionaries & Glossaries

Aids Glossary

analysis of variance; a statistical technique that analyzes the contribution to an experimental result made by independent variables.

Aegis**Anova**Definition from Language, Idioms & Slang Dictionaries & Glossaries

WordNet 2.0

**Noun**

1. a statistical method for making simultaneous comparisons between two or more means; a statistical method that yields values that can be tested to determine whether a significant relation exists between variables

(synonym) analysis of variance

(hypernym) multivariate analysis

(classification) statistics