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The assumption of [[homoscedasticity]], also known as homogeneity of variance, assumes equality of population variances.<ref name="Green" /> However, different variance-correlation matrix can be specified to account for this, and the heterogeneity of variance can itself be modeled.
;Independence of observations (No Autocorrelation of Model's Residuals)
Independence is an assumption of general linear models, which states that cases are random samples from the population and that scores on the dependent variable are independent of each other.<ref name="Green">{{cite book|last=Salkind|first=Samuel B. Green, Neil J.|title=Using SPSS for Windows and Macintosh : analyzing and understanding data|year=2004|publisher=Pearson Education|___location=Upper Saddle River, NJ|isbn=978-0-13-146597-8|edition=4th|url-access=registration|url=https://archive.org/details/usingspssforwind00samu}}</ref> One of the main purposes of multilevel models is to deal with cases where the assumption of independence is violated; multilevel models do, however, assume that 1) the level 1 and level 2 residuals are uncorrelated and 2) The errors (as measured by the residuals) at the highest level are uncorrelated.<ref>{{cite web |title=Introduction to Multilevel Modeling Using HLM 6 |author=ATS Statistical Consulting Group |url=http://www.ats.ucla.edu/stat/hlm/seminars/hlm6/outline_hlm_seminar.pdf |archive-date=31 December 2010 |archive-url=https://web.archive.org/web/20101231163641/http://www.ats.ucla.edu/stat/hlm/seminars/hlm6/outline_hlm_seminar.pdf }}</ref>
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