Week

Date

Topic

Readings

Jan 10

Lecture: Overview of basic statistical methods

For lecture

• Manly—Chapter 1 & Appendix A
• Salsburg, David S. 1985. The religion of statistics as practiced in medical journals. American Statistician 39(3): 220–223. [online at UNC libraries]
• Kendall, W. L. & Gould, W. R. 2002. An appeal to undergraduate wildlife programs: send scientists to learn statistics. Wildlife Society Bulletin 30:623–627. [Davis library: SK351.W62a]

Reference only

• Bland, J. M. & Altman, D. G. 1994–2006. Statistics Notes from the British Medical Journal. (read as necessary) http://www-users.york.ac.uk/~mb55/pubs/pbstnote.htm

Some Reviews of Manly (2001)

• Stow, Craig. 2002. Review of Manly (2001) Risk Analysis 22(1): 185. http://www.blackwell-synergy.com/links/doi/10.1111/0272-4332.00015/full/
• Ziegel, Eric R. 2002. Review of Manly (2001). Technometrics 44(2): 201. [online at UNC libraries]

For lab

• Cryer, Jonathan. 2001. Problems with Using Microsoft Excel for Statistics. Presentation at the Joint Statistical Meetings, Atlanta, GA. http://www.cs.uiowa.edu/~jcryer/JSMTalk2001.pdf
• Ellner, Stephen P. 2001. Review of R 1.1.1. Bulletin of the Ecological Society of America 82(2): 127–128. [online at UNC libraries]
• Burns, Patrick. 2006. R Relative to Statistical Packages: Comment 1 on Technical Report Number 1 (Version 1.0) Strategically using General Purpose Statistics Packages: A Look at Stata, SAS and SPSS. Technical Report Series #1. Statistical Consulting Group UCLA Academic Technology Services. http://www.ats.ucla.edu/stat/technicalreports/Number1/R_relative_statpack.pdf
• Burns, Patrick. 2005. A Guide for the Unwilling S User. http://www.burns-stat.com/pages/Tutor/unwilling_S.pdf
• R/S/S+: Statistics? Programme? and What else?--An ABC Introduction. [A nice visual overview of R and its history that was created by the Computer Science Department at the University of South Dakota.] http://www.usd.edu/csci/research/890/R2Splus4CSS.pdf

Jan 15

Lecture: Important probability distributions in environmental science

For lecture

• Manly—Appendix A, Chapter 3: 3.1–3.3

For lab

• Wainer, Howard. 1984. How to display data badly. The American Statistician 38(2): 137–147. [online at UNC libraries]
• Gelman, A., Pasarica, C., & Dodhia, R. 2002. Let's practice what we preach: Turning tables into graphs. The American Statistician 56:121-130. http://www.stat.columbia.edu/~gelman/research/published/dodhia.pdf
• Broman, Karl (Biostatistics, John Hopkins) Top Ten Worst Graphs (in the scientific literature) http://www.biostat.jhsph.edu/~kbroman/topten_worstgraphs/

Reference only

• McLaughlin, Michael P. 1999. A Compendium of Common Probability Distributions.
  http://www.causascientia.org/math_stat/Dists/Compendium.pdf

Lecture: Important probability distributions in environmental science (continued)

Jan 29

Lecture: Sampling procedures

For lecture

• Manly—Chapter 2

Reference only

• Schreuder, Hans T.; Ernst, Richard; Ramirez-Maldonado, Hugo. 2004. Statistical techniques for sampling and monitoring natural resources. Gen. Tech. Rep. RMRS-GTR-126. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 111 p.
  Publication: http://www.fs.fed.us/rm/pubs/rmrs_gtr126.pdf
  Accompanying data sets and R code: http://www.fs.fed.us/rm/pubs/rmrs_gtr126_data_sets.zip

• Lumley, Thomas. 2007. Survey Analysis in R. http://faculty.washington.edu/tlumley/survey/
  This web site contains a wealth of documentation about the R survey package. Included are help pages, textbook examples, and slides from tutorials. Particularly useful are "Specifying a survey design", "Simple summary statistics", and the slides from a tutorial presented at useR 2006.

Lecture: Regression

For lecture

• Manly—Chapter 3
• Suits, D. B. 1957. Use of dummy variables in regression equations. Journal of the American Statistical Association 52(280): 548–551. [online at UNC libraries]

Feb 12

Lecture: Likelihood theory

For lecture

• Myung, In Jae. 2001. Tutorial on maximum likelihood estimation. Journal of Mathematical Psychology 47: 90–100. [online at UNC libraries]

Reference only

• Roff, Derek A. 2006. Introduction to Computer-Intensive Methods of Data Analysis in Biology. Cambridge University Press. Chapter 2 "Maximum Likelihood" [UNC Zoology library QH 324.2 .R62 2006]

Feb 19

For lecture

• Manly—Chapter 3
• Wilson, K. and Grenfell, B. T. 1997. Generalized linear modelling for parasitologists. Parasitology Today 13(1): 33–38. (errata appear in Parasitology Today 13(5): 162.) [online at UNC libraries]
• Atkinson, P., Jiskoot, H., Massari, R., & Murray, T. 1998. Generalized linear modelling in geomorphology. Earth Surface Processes and Landforms 23(13): 1185–1195. [online at UNC libraries]
• McArdle, Brian H. and Anderson, Marti J. 2004. Variance heterogeneity, transformations and models of species abundance: a cautionary tale. Canadian Journal of Fisheries and Aquatic Sciences 61: 1294–1302. [online at UNC libraries]

Feb 26

For lecture

• Anderson, D. R., Burnham, K. P., & Thompson, W. L. 2000. Null Hypothesis Testing: Problems, Prevalence, and an Alternative. Journal of Wildlife Management 64(4): 912–923. http://www.warnercnr.colostate.edu/~anderson/PDF_files/TESTING.pdf
• Burnham, Kenneth P. and David R. Anderson. 2001. Kullback-Leibler information as a basis for strong inference in ecological studies. Wildlife Research 28: 111–119. http://www.publish.csiro.au/?act=view_file&file_id=WR99107.pdf
• Johnson, J. B. and Omland, K. S. 2004. Model selection in ecology and evolution. Trends in Ecology and Evolution 19(2): 101–108. [online at UNC libraries]
• Mazerolle, Marc J. 2006. Improving data analysis in herpetology: using Akaike's Information Criterion (AIC) to assess the strength of biological hypotheses. Amphibia-Reptilia 27: 169–180. mazerolle.pdf

Mar 5

Mar 12

Mar 19

For lecture

• Manly—Chapter 4
• Hesterberg, Tim, Monaghan, Shaun, Moore, David S., Clipson, Ashley, & Epstein, Rachel. Bootstrap methods and permutation tests, pp. 1–65. [online supplement to Chapter 18 of The Practice of Business Statistics, W. H. Freeman, New York] http://bcs.whfreeman.com/pbs/cat_160/PBS18.pdf
• Efron, Bradley & Tibshirani, Robert. 1991. Statistical data analysis in the computer age. Science 253(5018): 390–395. [online at UNC libraries]

For lab

• Canty, Angelo J. 2002. Resampling methods in R: The boot Package. R News 2/3: 2–7. http://cran.r-project.org/doc/Rnews/Rnews_2002-3.pdf

Reference only

• Roff, Derek A. 2006. Introduction to Computer-Intensive Methods of Data Analysis in Biology. Cambridge University Press. Chapter 3 "The Jackknife", Chapter 4 "The Bootstrap", and Chapter 5 "Randomization and Monte Carlo Methods" [UNC Zoology library QH 324.2 .R62 2006]

Mar 26

For lab

• Hothorn, Torsten, Kurt Hornik, Mark van de Wiel, and Achim Zeileis. 2006. coin: A Computational Framework for Conditional Inference. http://cran.stat.ucla.edu/doc/vignettes/coin/coin.pdf

For lecture

• Manly—Chapter 4
• Bender, R. & S. Lange. 2001. Adjusting for multiple testing—when and how? Journal of Clinical Epidemiology 54: 343–349. [online at UNC libraries]
• Garcia, L.V. 2004. Escaping the Bonferroni iron claw in ecological studies. Oikos 105: 657–663. [online at UNC libraries]
• Moran, Matthew D. 2003. Arguments for rejecting the sequential Bonferroni in ecological studies. Oikos 100: 403–405. [online at UNC libraries]
• Morrison, D. 1991. Personal Type I error rates in the ecological sciences. Bulletin of the Ecological Society of Australia Incorporated 21(3): 49–53. morrison.pdf
• Gelman, Andrew & Hall Stern. 2006. The difference between “significant” and “not significant” is not itself statistically significant. The American Statistician 60(4): 328–331. http://www.stat.columbia.edu/~gelman/research/published/signif4.pdf
• Krantz, D. H. 1999. The null hypothesis testing controversy in psychology. Journal of the American Statistical Association 94: 1372–1381. [online at UNC libraries]
• Nester, M. R. 1996. An applied statistician's creed. Applied Statistics 45: 401–410. [online at UNC libraries]
• Verhoeven, Koen J. F., K. L. Simonsen, and L. M. McIntyre. 2005. Implementing false discovery rate control: increasing your power. Oikos 108: 643–647. [online at UNC libraries]
• Waite, Thomas A. and Lesley G. Campbell. 2006. Controlling the false discovery rate and increasing statistical power in ecological studies. Ecoscience 13: 439–442. [UNC botany library]

Lecture: Correlated data

For lecture

• Manly—Chapter 8
• Gelman, A. 2006. Multilevel (hierarchical) modeling: What it can and cannot do. Technometrics 48(3): 432–435.  http://www.stat.columbia.edu/~gelman/research/published/multi2.pdf
• Todd, S. Y., T. R. Crook, & A. G. Barilla. 2005. Hierarchical linear modeling of multilevel data. Journal of Sport Management 19(4): 387–403. [online at UNC libraries]
• Diez-Roux, A. V. 2002. A glossary for multilevel analysis. Journal of Epidemiology and Community Health 56(8): 588–594. [online at UNC libraries]

Apr 16

Lecture: Bayesian statistics

Lab: Bayesian tools in R; WinBUGS

For lecture

• Manly—Chapter 4
• Ellison, Aaron M. 2004. Bayesian inference in ecology. Ecology Letters 7: 509–520. [online at UNC libraries]
• Clark, J. S. 2005. Why environmental scientists are becoming Bayesians. Ecology Letters 8(1): 2–14. [online at UNC libraries]
• Link, W. A., Cam, E., Nichols, J. D., & Cooch, E. G. 2002. Of BUGS and birds: a Markov chain Monte Carlo for hierarchical modeling in wildlife research. Journal of Wildlife Management 66: 277–291.  http://canuck.dnr.cornell.edu/research/pubs/pdf/MCMC_JWM.pdf
• Mila, A. L. & A. L. Carriquiry. 2004. Bayesian analysis in plant pathology. Phytopathology 94(9): 1027–1030. http://www.apsnet.org/phyto/pdfs/2004/0719-07O.pdf

Reference only

• Roff, Derek A. 2006. Introduction to Computer-Intensive Methods of Data Analysis in Biology. Cambridge University Press. Chapter 7 "Bayesian Methods" [UNC Zoology library QH 324.2 .R62 2006]

Apr 23

For lecture

• Manly—Chapter 9

Final Exam