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STAT 574E/MATH 574E/CPH 574E − Environmental Statistics


Description: Statistical methods for environmental and ecological sciences, including nonlinear regression, generalized linear models, temporal analyses, spatial analyses/kriging, quantitative risk assessment.
Prerequisite(s): STAT 571B, or PSY 507C, or equivalent.

This course in the Environmental Statistics studies data analytic methods for problems in the environmental sciences to intermediate graduate students in agriculture, biology, climatology, ecology, engineering, geology, geography, global change, public health, pharmacology, toxicology, and associated disciplines, and to graduate students in statistics and biostatistics, it provides a foundation for applying environmetric approaches in scientific research and policy-driven investigations.

Spring 2012

The course will meet Wednesdays and Fridays from 1:30 pm - 2:45 pm in Drachman Hall room A119.
The textbook is Wiley's Analyzing Environmental Data (2005). Additional online resources are available at the book's Companion/Support Website.
The course also utilizes supplementary material in W. Jason Owen's The R Guide, ver. 2.5.

Material covered in the 2012 offering of the course will include:

The course syllabus gives complete information.

Attendance

Students are expected to attend class. If important circumstances prevent this, it is the student's responsibility to find out what was covered in class, what was assigned for reading or homework, and what special announce­ments (if any) were made. "Excessive absence" in this class will be construed to be absence from more than 10 percent of the scheduled class sessions, whether ex­cused or unexcused, and will be subject to Administrative Drop as per University policies.

Homework Assignments - Spring 2012

Homeworks using exercises from the textbook will typically require computer implementation. The textbook provides examples using the SAS statistical package, although lectures and notes will employ the R statistical language. Students are welcome to employ any suitable statistical software.

Homeworks are due as assigned. No exceptions.
These assignments are subject to revision with prior notice.

           Textbook
Date due   Chapter    Exercises

Feb.  1       1       1.1, 1.4, 1.5ab, 1.5c*, 1.7, 1.8ac, 1.9b, 1.10, 1.14abc

Feb. 15       2       2.4, 2.7, 2.17, 2.23ab, 2.23c*

Feb. 24      1-2      Exam 1

Mar.  9       3       3.1a, 3.5a, 3.6, 3.9, 3.12, 3.17aef(ignore parts c-d), 3.17b*, 3.18

Apr.  6       5       5.8, 5.9, 5.12a, 5.18, 5.20, 5.21, 5.23
                      (wherever backward elimination is mentioned, you can use minimum-AIC instead)

Apr. 11      3,5      Exam 2

May   2       6       6.1, 6.3, 6.6a, 6.7, 6.10, 6.13ae, 6.16, 6.23
                      (if using R for 6.23c, perform the weighted fit via fit.variogram)

May   7               Comprehensive Final Exam

* problem optional

Specialized Downloads - Spring 2012

Display slides:

Data sets:


Documents/sites:

  • Textbook Updates and Errata.


  • Sample R function for Cochran-Armitage trend statistic with proportion data.


  • An illustration of seasonality and trend in environmental data (from Example 5.4): Monthly mean CO2 levels over the Mauna Loa volcano.


  • R language comprehensive archive.


  • R language FAQ page.


  • R language online introduction.


  • Bob Muenchen's R4SAS guide with R-vs.-SAS programming equivalents.


  • A recent computational review article on R programming: Horgan, J.M. (2012), Programming in R. Wiley Interdisciplinary Reviews: Computational Statistics 4, 75-84.


    • Student Responsibilities


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