Introduction to Mixed Modelling: Beyond Regression and Analysis of Variance
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1. The need for more than one random-effect term when fitting a regression line.
2. The need for more than one random-effect term in a designed experiment.
3. Estimation of the variances of random-effect terms.
4. Interval estimates for fixed-effect terms in mixed models.
5. Estimation of random effects in mixed models: best linear unbiased predictors.
6. More advanced mixed models for more elaborate data sets.
7. Two case studies.
8. The use of mixed models for the analysis of unbalanced experimental designs.
9. Beyond mixed modelling.
10. Why is the criterion for fitting mixed models called residual maximum likelihood?