Inference Using Analysis of Variance for Comparing Multiple Means

Hang Lee
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Hang Lee: Massachusetts General Hospital, Department of Biostatistics

Chapter Chapter 4 in Foundations of Applied Statistical Methods, 2014, pp 75-86 from Springer

Abstract: 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.

Keywords: Individual Data Point; Unique Deviation; Rejection Region; Group Sample Size; Random Sampling Error (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1007/978-3-319-02402-8_4

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