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# 분산분석과 공분산 분석

모수적/비모수적 분산분석, 대화형/비대화형 공분산 분석, 다중 비교

## 함수

 `anova1` 일원분산분석 `anova2` 이원분산분석 `anovan` 다원분산분석 `aoctool` Interactive analysis of covariance `canoncorr` Canonical correlation `dummyvar` Create dummy variables `friedman` Friedman’s test `kruskalwallis` Kruskal-Wallis test `multcompare` 다중 비교 검정

## 예제 및 방법

• 일원분산분석(One-way ANOVA)

일원분산분석을 사용하여 단일 인자의 여러 그룹(수준)에 있는 데이터가 공통 평균을 갖는지 확인합니다.

• 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.