<?xml version="1.0" encoding="utf-8"?><!DOCTYPE article  PUBLIC '-//OASIS//DTD DocBook XML V4.4//EN'  'http://www.docbook.org/xml/4.4/docbookx.dtd'><article><articleinfo><title>FAQ/HierarchicalDesigns</title><revhistory><revision><revnumber>4</revnumber><date>2013-03-08 10:17:58</date><authorinitials>localhost</authorinitials><revremark>converted to 1.6 markup</revremark></revision><revision><revnumber>3</revnumber><date>2007-11-16 14:24:20</date><authorinitials>IanNimmoSmith</authorinitials></revision><revision><revnumber>2</revnumber><date>2007-10-24 14:10:09</date><authorinitials>IanNimmoSmith</authorinitials></revision><revision><revnumber>1</revnumber><date>2007-10-24 13:16:29</date><authorinitials>IanNimmoSmith</authorinitials></revision></revhistory></articleinfo><section><title>Sums of Squares for Hierarchical Designs</title><para>We will explore the Expected Mean Squares for a standard <emphasis role="strong">Split Plot Design</emphasis>. </para><para>Suppose $$G$$ levels of a factor $$A$$ with $$K$$ families $${\F_text{gk},\text{k}=1\ldots K\}$$ nested within each group $$\{A_text{g},text{g}=1\ldots G\}$$, and within that family there are $$\mbox{n}_text{jk}$$ individuals on whom measurements are made. </para><para>Then pooling the K variances we have </para><para>$$\mbox{Pooled Variance V = } \frac{\sum_text{k}<superscript>text{K} (n_text{k}-1) \mbox{V}_text{k}}{\sum_text{k}</superscript>text{K} (n_text{k} -1)} $$ </para><para>and we can use this pooled variance to obtain the standard error of the mean since </para><para>$$\mbox{Pooled Mean Standard Error = } \sqrt{ \frac{\mbox{V}}{\sum_text{k}^text{K} n_text{k}} } $$ </para></section></article>