• Lecture: Tu 3:40 - 5:00, Th 3:40-5:00 MBB 1424
• Instructor: Dr. Philip Dixon, pdixon at iastate.edu, 2121 Snedecor, 4-2142
• Office Hours: TBD
• Review / Help: during office hours or set up a time to meet with me
  
• Questions?: Please feel free to e-mail me anytime (pdixon at iastate.edu) with questions or comments.
• Class Web site: http://www.public.iastate.edu/~pdixon/stat534
  Copies of the syllabus, homework assignments, data sets and example programs will be available here.
• Text: None
  
• Reading materials: Various assigned readings will be available in the Statistics Dept. Office. See Jeanette to borrow the material to make a copy.
• Goals:
  • Learn methods used to answer ecological questions, focusing on population ecology
  • Understand the statistical theory for those methods.
  • Illustrate classical and modern statistical inference using some non-standard problems.
  • Be able to apply statistical methods and theory to novel problems.
• Grading:
  4 Homework sets: 100 points each.
  No tests.

  Homeworks are due at 5pm on the assigned date. Late homework will be penalized 5 points per day late.

• Computing:
  This course emphasizes data analysis to answer questions. This will require using statistical computing packages. Unfortunately, no one package does everything well. You may use whatever tools are appropriate to do the necessary computations. I expect that most of you will use R; I provide help with R. In particular, I will distribute examples of R code (and SAS if helpful) on the class web site. Some of you may be familiar with specialized programs developed for certain parts of the course (e.g. MARK for estimation of population parameters). You are free to use that program, but I will not lecture about it.

Weeks  Dates         Topic

 1-7   Aug 27-Oct 10   Estimation of population parameters
		       (population size, survival, detectability)
		       Mark recapture and related methods
                       
 8-10    Oct 15-Oct 31   Population modeling
		       Matrix models
		       Integral population modeling
		       Stochastic matrix models

11-13   Nov 5-Dec 5    Combining models and data
                       Bayesian hierarchical modeling
                       Models with density dependence

14-15    Dec 3-Dec 19 To be determined by class interest

Possible topics for the last two weeks: 

 Your suggestions, especially methods or problems specific to your research
 Further extensions of topics already discussed.
 Behavioural statistics, especially analysis of event sequences.
 Resource selection / habitat preference analysis
 Home range estimation and related issues

Readings: Assigned readings will provide background, additional details, or another presentation of the lecture material. Citations will be distributed in lecture. Jeanette in the Statistics Department Office will have a master copy that you can borrow. It will help if you read the assigned material soon after the appropriate lectures.

Homework: Statistics is best learnt by doing. The homework problems are chosen to give you practice in using the methods, interpreting the results, and understanding the theory. The intent is to understand and be able to apply lecture concepts, so discussion with friends and classmates is encouraged. However, you must write up your own solutions. Copying papers is not a good way to learn and will not be tolerated.

There will be an assignment every 3 weeks or so, i.e. two on mark-recapture, one on population modeling, and one on combining models and data. Each assignment will have four problems. Two, perhaps three, will focus on data analysis and interpretation. Two, perhaps one, will focus on the theory. If there are two theory problems, one may be replaced by summarizing a paper from a list I provide.

The data analysis problems should be written up as reports to your manager. The report should include a one (perhaps two) paragraph executive summary, a methods section that describes the methods you used, and a results section that includes appropriate tables and figures. For some problems you will need to choose an appropriate analysis. In these cases, only report results from the analysis you believe is most appropriate; your methods section should include a discussion of how you decided what to do.

Disability accommodation: Iowa State University complies with the Americans with Disabilities Act and Sect 504 of the Rehabilitation Act. If you have a disability and anticipate needing accommodation(s) in this course, please contact Philip Dixon within the first two weeks of the semester. Retroactive requests for accommodations will not be honored. Before meeting with me, you need to obtain a SAAR form with recommendations for accommodations from the Disability Resources Office, Room 1076, Student Services Building. Their telephone number is 515-294-7220.

Academic Honesty Policy: The ISU academic honesty policy is printed in the University catalog and available online. To clarify how this applies to your work in this class:
On homework assignments: I encourage you to help each other interpret the homework problems, write code, debug code, and interpret the output. You may share code, but I encourage you to understand that code even if you didn't write it. I do require you to write your answers in your own words.
On the project: I encourage pairs to work together and write a single report. I will ask each person to assess the relative contribution of each person. The grade will be assigned proportionately.