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Inference Using Analysis of Variance for Comparing Multiple Means

In: Foundations of Applied Statistical Methods

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  • Hang Lee

    (Massachusetts General Hospital, Department of Biostatistics)

Abstract

This chapter discusses single-factor analysis of variance (ANOVA) which is mainly applied to compare three or more independent means. The words “single factor” refer to that the means are compared across levels of a “single” classification variable (i.e., classification of means by a single categorical variable). The classification variable is called independent variable or factor (thus, the method is also called single-factor ANOVA) and the outcome variable of which the means are compared is called dependent variable. This method requires certain assumptions: (1) the dependent variable values are the observations sampled from a normal distribution and (2) the population variances are equal (homoscedasticity) across the levels of the independent variable.

Suggested Citation

  • Hang Lee, 2014. "Inference Using Analysis of Variance for Comparing Multiple Means," Springer Books, in: Foundations of Applied Statistical Methods, edition 127, chapter 0, pages 75-86, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-02402-8_4
    DOI: 10.1007/978-3-319-02402-8_4
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