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# Análisis de la varianza y la covarianza

Análisis paramétrico y no paramétrico de la varianza, análisis interactivo y no interactivo de la covarianza, comparaciones múltiples

## Funciones

 `anova1` One-way analysis of variance `anova2` Two-way analysis of variance `anovan` N-way analysis of variance `aoctool` Interactive analysis of covariance `canoncorr` Canonical correlation `dummyvar` Create dummy variables `friedman` Friedman’s test `kruskalwallis` Kruskal-Wallis test `multcompare` Multiple comparison test

## Ejemplos y procedimientos

• One-Way ANOVA

Use one-way ANOVA to determine whether data from several groups (levels) of a single factor have a common mean.

• Two-Way ANOVA

In two-way ANOVA, the effects of two factors on a response variable are of interest.

• N-Way ANOVA

In N-way ANOVA, the effects of N factors on a response variable are of interest.

• ANOVA with Random Effects

ANOVA with random effects is used where a factor's levels represent a random selection from a larger (infinite) set of possible levels.

• Other ANOVA Models

N-way ANOVA can also be used when factors are nested, or when some factors are to be treated as continuous variables.

• Multiple Comparisons

Multiple comparison procedures can accurately determine the significance of differences between multiple group means.

• Analysis of Covariance

Analysis of covariance is a technique for analyzing grouped data having a response (y, the variable to be predicted) and a predictor (x, the variable used to do the prediction).

• Nonparametric Methods

Statistics and Machine Learning Toolbox™ functions include nonparametric versions of one-way and two-way analysis of variance.