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  "body": "\n## Summary\n\nA mixed model is a linear model with both fixed effects and random effects.\nFixed effects are unknown constants, and random effects are random variables whose covariance is to be estimated.\nRandom effects describe correlated groups of observations that share a source of variation.\nExamples are repeated measurements on the same person or pupils in the same school.\nIt extends the [[general linear model|A06SGFCSCE]], and its covariance parameters are usually estimated by maximum likelihood or restricted maximum likelihood.\nMixed models are standard for longitudinal, clustered and hierarchical data.\n\n## Explanations\n\n### L1 human\n\nMeasure the blood pressure of ten patients five times each.\nThe five values of one patient are closer to each other than to the values of other patients, because each patient has a personal level.\nA mixed model gives each patient a random personal shift around the population mean, plus independent measurement noise.\nThe fixed part answers questions about the population, and the variance of the random shifts says how much patients differ.\n",
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