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분산분석과 공분산 분석

모수적/비모수적 분산분석, 대화형/비대화형 공분산 분석, 다중 비교


aoctoolInteractive analysis of covariance
canoncorrCanonical correlation
dummyvarCreate dummy variables
friedmanFriedman’s test
kruskalwallisKruskal-Wallis test
multcompare다중 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.