Tentative Syllabus for ENST 562—Spring 2007 (continued)
Tentative Syllabus for ENST 562—Spring 2007
Week / Date / Topic / Readings1 / Jan 10 / Lecture: Overview of basic statistical methods
Lab: The R statistical computing environment / Manly[1]—Chapter 1 & Appendix A
Salsburg, David S. 1985. The religion of statistics as practiced in medical journals. American Statistician 39(3): 220–223.
Kendall, W. L. Gould, W. R. 2002. An appeal to undergraduate wildlife programs: send scientists to learn statistics. Wildlife Society Bulletin 30:623–627.
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 through UNC librarires]
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.
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
2 / Jan 15 / Lecture: Important probability distributions in environmental science
Lab: R graphics / Manly—Appendix A
For lab
Wainer, Howard. 1984. How to display data badly. The American Statistician 38(2): 137–147.
Gelman, A., Pasarica, C., & Dodhia, R. 2002. Let's practice what we preach: Turning tables into graphs. The American Statistician 56:121-130.
Broman, Karl (Biostatistics, John Hopkins) Top Ten Worst Graphs (in the scientific literature) http://www.biostat.jhsph.edu/~kbroman/topten_worstgraphs/
Week / Date / Topic / Readings
3 / Jan 22 / Lecture: Sampling procedures
Lab: R survey package / Manly—Chapter 2
Reference: 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. http://www.fs.fed.us/rm/pubs/rmrs_gtr126.pdf
4 / Jan 29 / Lecture: Regression
Lab: Assessing regression models / Manly—Chapter 3
5 / Feb 5 / Lecture: Regression (continued)
Lab: Regression models (continued) / Manly—Chapter 3
Suits, D. B. 1957. Use of dummy variables in regression equations. Journal of the American Statistical Association 52(280): 548–551.
6 / Feb 12 / Lecture: Likelihood theory
Lab: Maximum likelihood estimation / Myung, In Jae. 2001. Tutorial on maximum likelihood estimation. Journal of Mathematical Psychology 47: 90–100.
7 / Feb 19 / Lecture & Lab: Generalized linear models / 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.)
Atkinson, P., Jiskoot, H., Massari, R., Murray, T. 1998. Generalized linear modelling in geomorphology. Earth Surface Processes and Landforms 23(13): 1185–1195.
8 / Feb 26 / Lecture & Lab: Model comparison / 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
9 / Mar 5 / Lecture & Lab: Statistical modeling / Midterm
10 / Mar 12 / Spring Break
11 / Mar 19 / Lecture & Lab: Randomization tests and the bootstrap / 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.
Week / Date / Topic / Readings
12 / Mar 26 / Lecture & Lab: Multiple testing issues / Manly—Chapter 4
Bender, R. Lange, S. 2001. Adjusting for multiple testing—when and how? Journal of Clinical Epidemiology 54: 343–349.
Garcia, L.V. (2004) Escaping the Bonferroni iron claw in ecological studies. Oikos 105: 657–663.
Morrison, D. 1991. Personal Type I error rates in the ecological sciences. Bulletin of the Ecological Society of Australia Incorporated 21(3): 49–53.
Gelman, Andrew Stern, Hall. 2006. The difference between “significant” and “not significant” is not itself statistically significant. http://www.stat.columbia.edu/~gelman/research/unpublished/signif3.pdf
Krantz, D. H. 1999. The null hypothesis testing controversy in psychology. Journal of the American Statistical Association 94: 1372–1381.
Nester, M. R. 1996. An applied statistician's creed. Applied Statistics 45: 401–410.
13 / Apr 2 / Lecture: Bayesian statistics
Lab: Bayesian tools in R; WinBUGS / Manly—Chapter 4
Ellison, Aaron M. 2004. Bayesian inference in ecology. Ecology Letters 7: 509–520.
Clark, J. S. 2005. Why environmental scientists are becoming Bayesians. Ecology Letters 8(1): 2–14.
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
14 / Apr 9 / Lecture: Environmental monitoring and impact assessment / Manly—Chapters 5 & 6
15 / Apr 16 / Lecture: Correlated data
Lab: nlme package in R / 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., Crook, T.R., Barilla, A. G. 2005. Hierarchical linear modeling of multilevel data. Journal of Sport Management 19(4): 387–403.
Diez-Roux, A. V. 2002. A glossary for multilevel analysis. Journal of Epidemiology and Community Health 56(8): 588–594.
16 / Apr 23 / Lecture: Spatial data analysis / Manly—Chapter 9
Final Exam
[1] Manly, Bryan. 2001. Statistics for Environmental Science and Management. Chapman & Hall/CRC Press: Boca Raton, FL