Course detail

LCE5872 - Experimental Statistics II and Mixed Models

Credit hours

In-class work
per week
per week
15 weeks
120 hours

Clarice Garcia Borges Demetrio
Renata Alcarde Sermarini
Sônia Maria De Stefano Piedade
Taciana Villela Savian

To provide a solid methodological basis for the model-based analysis of continuous data in experimental
research, including the use of Hasse diagrams, and mixed models for incomplete data.

Model definition and formulation, associated matrix concepts, and statistical inference for the analysis of
continuous data, including the use of Hasse diagrams, models for incomplete data through the mixed
model approach, including theory, estimation, model checking, and diagnostics. All methods and
applications will use the R software system.
1. Hasse Diagrams.
2. Introduction to mixed models.
3. Unbalanced cross-classifications.
4. Split-plot experiments.
5. Split-Block experiments.
6. Incomplete block designs.
7. Lattice squares. 8. Analysis of groups of experiments.
9. Analysis of groups of experiments with common treatments (augmented block).

Bailey, R. A. (2008). Design of Comparative Experiments. Cambridge, Cambridge University Press.
348p. ( ISBN-13: 9780521683579)
Box, G.E.P.; Hunter, W.G.; Hunter, J.S. (2005). Statistics for experimenters:Design, Innovation, and
Discovery. 2nd Edition. Wiley, New York. 664p. (ISBN-13: 978-0471718130)
Casella, G. (2010). Statistical Design. New York, Springer. 330p. ( ISBN-13: 978-1441926142)
Cochran, W.G. and Cox, G.M. (1992). Experimental designs. 2nd Edition. Wiley, New York. 640p. (
ISBN-13: 978-0471545675)
Cox, D.R.; Donnelly, C.A. (2011). Principles of Applied Statistical. Cambridge, Cambridge University
Press. 212p. (ISBN: 9781107644458)
Davis, C. S. (2010). Statistical Methods for the Analysis of Repeated Measurements. Springer Texts in
Statistics. 439p. (ISBN-13: 978-1441929761)
Demidenko, E. (2004). Mixed Models: Theory and Applications. New Jersey, Wiley. 736p. (ISBN-13:
Dey, A. (2010). Incomplete Block Design. World Scientific Publishing Company. 1 edition. 288p. (ISBN13: 978-9814322683)
Federer, W.T. (1993). Statistical design and analysis for intercropping experiments. Springer-Verlag.
New York.298p. ( ISBN-13: 978-0387979236)
Fitzmaurice; G. M.; Laird, N. M.; Ware, J. H. (2004). Applied Longitudinal Analysis. Wiley Series in
Probability and Statistics. 536p. (ISBN-13: 978-0471214878)
Galwey, N. W. (2006) Introduction to Mixed Modelling: Beyond Regression and Analysis of Variance.
Chichester, Wiley. 376p. (ISBN-13: 978-0470014967)
Lawson, J. (2010). Design and Analysis of Experiments with SAS. Chapman & Hall/CRC Texts in
Statistical Science. 596p. (ISBN-13: 978-1420060607)
McCulloch, C.E.; Searle, S. R.; Neuhaus, J.M. (2008). Generalized, Linear, and Mixed Models. Wiley
Series in Probability and Statistics. 424p. (ISBN-13: 978-0470073711)
Mead, R.; Curnow, R.N.; Hasted, A. M.; Curnow, R.M. (2002). Statistical Methods in Agriculture and
Experimental Biology, 3th Edition. Champman & Hall/CRC, USA., 488 p. (ISBN-13: 978-1584881872)
Milliken, G.A.; Johnson, D.E. (2004). Analysis of messy data. Vol I : Designed of experiments.
Chapmann Hall. London. 674p. (ISBN-13: 978-1584883340)
Montgomery, D.C. (2008). Design and analysis of experiments. 7th Edition. Wiley, New York. 680p.
(ISBN-13: 978-0470128664)
Morris, M. (2010). Design of Experiments: An Introduction Based on Linear Models. Chapman &
Hall/CRC Texts in Statistical Science. 370p. (ISBN-13: 978-1584889236)
Pinheiro, J. C.; Bates, D. M. (2009). Mixed-effects models in S and S-PLUS. New York, Springer. 548p. (
ISBN-13: 978-1441903174)
Ryan, T. P. (2007). Modern experimental design. Hoboken, N.J., Wiley-Interscience. 593p. (ISBN-13:
Searle, S.R.; Casella, G.; McCulloch, C.E. (2006).Variance components. John Wiley & Sons, Inc.. New York. 536p. (ISBN-13: 978-0470009598).
Venables, W.N. and Ripley, B.D. (2010). Modern applied statistics with S: Statistics and Computing.
Springer, New York. 506p. (ISBN-13: 978-1441930088)
Verbeke, G.; Molenberghs, G. (2009). Linear Mixed Models for Longitudinal Data. Springer Series in
Statistics. 592p. (ISBN-13: 978-1441902993)
Welham, S.J.; Gezan, S.A.; Clark, S.J.; Mead; A. (2014) Statistical Methods in Biology. Design and
Analysis of Experiments and Regression. CRC Press.
WEST, B.; WELCH, K.B; GALECKI, A.T. (2014). Linear Mixed Models: A Practical Guide Using Statistical
Software. 2ª ed. Chapman & Hall/CRC.
Wu, L. (2009). Mixed effects models for complex data. Chapman and Hall/CRC. 431p. (ISBN-13: 978-