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Chapter 15 Mixed Models - CMU Statistics

Chapter 15 Mixed ModelsA flexible approach to correlated OverviewCorrelated data arise frequently in statistical analyses. This may be due to group-ing of subjects, , students within classrooms, or to repeated measurements oneach subject over time or space, or to multiple related outcome measures at onepoint in time. Mixed model analysis provides a general, flexible approach in thesesituations, because it allows a wide variety of correlation patterns (or variance-covariance structures) to be explicitly mentioned in Chapter 14, multiple measurements per subject generally resultin the correlated errors that are explicitly forbidden by the assumptions of standard(between-subjects) AN(C)OVA and regression Models .

slope) randomly deviating from the mean intercept for each age group. This results in a group of parallel \personal" regression lines (or non-parallel if the slope is also random). Then, it is reasonable (but not certain) that the errors around the personal regression lines will be uncorrelated. One way to do this is to use

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Transcription of Chapter 15 Mixed Models - CMU Statistics

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