Textbooks


The course has no required text, but the following books will be useful to you. In each of the sections below the references are listed in order from basic to more advanced.

General biostatistics books


Whitlock, M. and D. Schluter. 2009. The analysis of biological data. Greenwood Village, Colo., Roberts and Co. Publishers.
A readable introduction to analysing data in biology. A good place to refresh your memories. I will assume that you have already taken an undergraduate course in statistics and are familiar with basic principles covered in Chapters 1 through 17. Some of the material in the later chapters will be used in this course.

Grafen, A. and R. Hails. 2002. Modern statistics for the life sciences. Oxford ; New York, Oxford University Press.
An introduction to the general linear model approach to analyzing data. Emphasis is on concepts and intuition rather than mathematics. A bit heavy on hypothesis testing and light on estimation and confidence intervals.

Quinn, G. P. and M. J. Keough. 2002. Experimental design and data analysis for biologists. Cambridge, UK; New York, Cambridge University Press.
An excellent second course in statistics and biological data analysis. Emphasis in examples is on ecology, especially marine intertidal ecology. Full of practical information on the best approaches to use in particular circumstances and the reasons why.

R books

Zuur, A. F., E. N. Ieno and E. Meesters. 2009. A beginner's guide to R. New York, Springer. UBC Online.
A readable, detailed introduction to data manipulation and plotting in R. Doesn't get much farther with data analysis than tables and graphs.

Dalgaard, P. 2008, Introductory statistics with R. 2nd. ed. New York, Springer. UBC Online.
A clear introduction to the basics of R and how to carry out the standard methods for analyzing data. Better for starters than Crawley's but less comprehensive.

Crawley, M. J. 2007, The R book. Chichester, England; Hoboken, N.J., Wiley.
A helpful reference for methods in R, including linear mixed modeling. Explanations not always straightforward. His approach to model simplification is outdated. We'll be discussing alternative model selection approaches in class.

Venables, W. N. and B. D. Ripley. 2002. Modern applied statistics with S-PLUS. 4th. New York, Springer.
The standard reference for statistical analysis of data using R and S. Covers most of the methods you will every need.

Pinheiro, J. C. and D. M. Bates. 2000. Mixed-effects models in S and S-PLUS. New York, Springer. UBC online
The standard reference for linear mixed effects modeling using R. Aimed at an advanced level. At least two of the chapters are essential reading if you use nlme in R to analyze your data.
Zuur, A. F., E. N. leno, N. J. Walker, A. A. Saveliev and G. M. Smith. 2009. Mixed effects models and extensions in ecology with R. UBC Online.
A useful guide to advanced methods of data analysis in ecology as well as to carrying them out in R. Topics including nonlinear regression, additive modeling, mixed-effects models, nonindependent data, generalized least squares and generalized additive models.

Borcard, D., F. Gillet and P. Legendre. 2011. Numerical ecology with R. Springer. UBC Online.
A guide to multivariate analysis of ecological data, including graphics and ordination.

Stevens, M. H. H. 2009. A primer of ecology with R. New York, Springer. UBC Online.
Using R to analyze models and data in population and community ecology.

Paradis, E. 2006. Analysis of phylogenetics and evolution with R. New York, Springer. UBC online.
Explains how to carry out phylogenetic comparative analysis using the ape package.

Sarkar, D. 2008. Lattice: Multivariate data visualization with R. New York, Springer.
UBC online.
The creator of the lattice package explains all.

More specialized references

Gotelli, N. J. and A. M. Ellison. 2004. A primer of ecological statistics. Sunderland, Mass., Sinauer Associates Publishers.
A clear overview of basic principles and methods in analyzing ecological data. Not particularly rich with data or examples. The overview of multivariate methods is excellent.

Hilborn, R. and M. Mangel. 1997. The ecological detective: confronting models with data. Princeton, NJ, Princeton University Press.
A great overview of how to fit models to data using a likelihood and model selection approach.

Bolker, B. M. 2008. Ecological models and data in R. Princeton, NJ, Princeton University Press.
A good complement to Hilborn and Mangel, with the added practical information on how to implement the general approach using R.

Burnham, K. P. and D. R. Anderson. 2002. Model selection and multimodel inference: a practical information-theoretic approach. 2nd. New York, Springer.
The authoritative treatise on modern approaches to model selection.

Efron, B. and R. Tibshirani. 1998. An introduction to the bootstrap. Boca Raton, FL, Chapman & Hall/CRC Press.
An accessable introduction, at least the first few chapters.

Felsenstein, J.  2004. Inferring phylogenies. Sunderland, Mass, Sinauer.
The master's voice. Chapter 25 is a clear and compact summary of comparative methods.