<?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/intercept</title><revhistory><revision><revnumber>9</revnumber><date>2014-02-26 16:54:16</date><authorinitials>PeterWatson</authorinitials></revision><revision><revnumber>8</revnumber><date>2014-02-26 16:52:54</date><authorinitials>PeterWatson</authorinitials></revision><revision><revnumber>7</revnumber><date>2013-03-08 10:17:11</date><authorinitials>localhost</authorinitials><revremark>converted to 1.6 markup</revremark></revision><revision><revnumber>6</revnumber><date>2007-01-25 12:32:10</date><authorinitials>PeterWatson</authorinitials></revision><revision><revnumber>5</revnumber><date>2006-09-27 10:46:10</date><authorinitials>IanNimmoSmith</authorinitials></revision><revision><revnumber>4</revnumber><date>2006-08-03 14:53:30</date><authorinitials>PeterWatson</authorinitials></revision><revision><revnumber>3</revnumber><date>2006-08-01 09:06:32</date><authorinitials>pc0082.mrc-cbu.cam.ac.uk</authorinitials></revision><revision><revnumber>2</revnumber><date>2006-08-01 09:00:12</date><authorinitials>pc0082.mrc-cbu.cam.ac.uk</authorinitials></revision><revision><revnumber>1</revnumber><date>2006-08-01 08:59:45</date><authorinitials>pc0082.mrc-cbu.cam.ac.uk</authorinitials></revision></revhistory></articleinfo><section><title>The intercept in regression</title><para>The <emphasis>constant</emphasis> term in a regression ANOVA table tests whether the mean of the outcome measure is zero. It is usually not important if the significance test result in a positive outcome, but can be useful if the outcome is a difference. </para><para>For example suppose <emphasis role="strong">digit span forward</emphasis> and the <emphasis role="strong">GOAT</emphasis> tests are both measured at the same two times on the same person. We wish to see if the difference over time in <emphasis role="strong">digit span forward</emphasis> is explainable by the difference over time in the <emphasis role="strong">GOAT</emphasis>. This can be done by taking the difference between the two time points for each test score. Then we need to regress the <emphasis role="strong">GOAT difference</emphasis> on <emphasis role="strong">digit span forward difference</emphasis>.   </para><para>In the output we now check the t-test for the constant (or intercept) term in the regression. Seeing if the constant term is zero tells us if there is a non-zero difference in digit span forward scores, averaged over individuals, even after accounting for their difference in GOAT scores.  </para><para>See also: <emphasis role="strong">Everitt B, and Hay D.</emphasis> (1992) p.90-91 <emphasis>Talking About Statistics: A Psychologist’s Guide to Design and Analysis</emphasis>. Edward Arnold. This text looks at the intercept to see if the difference in dysphoria scores is the same across group (a group x dysphoria (covariate) interaction). </para></section></article>