ANOVA and Ordinary Regression

Part of the Springer Texts in Statistics book series (STS)

In this chapter, we address the standard task of learning about a process when it is observed incompletely, by means of a finite number of its realisations. We study two simple settings, analysis of variance (ANOVA) and simple regression, with the standard assumptions of normality and equal residual variance. We are interested in efficient estimation of a priori specified population quantities.


Mean Square Error Linear Regression Model Residual Variance High Leverage Ordinary Regression 
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© Springer 2008

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