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COURSE INFORMATION

Instructor:

• Sandra McBride, Visiting Assistant Professor, Institute of Statistics and Decision Sciences, 221 Old Chemistry Building, sandra at stat.duke.edu, 684-4608

Teaching Assistants:

• Fabio Rigat, fabio at stat.duke.edu, 222 Old Chemistry Building, 684-8840

  • Office hours: Fridays and Mondays 10:30am-12:00pm. 222 Old Chemistry Building.

• Neil Carlson, nec at duke.edu

  • Office hours: Mondays, 4-6pm, A336 LSRC

• German Molina, german at stat.duke.edu, 222 Old Chemistry Building, 684-8840

Course Overview:

This course will focus on exploratory analysis of data, graphical methods, building and fitting models, investigating assumptions of models, and interpreting the results of statistical models. A major focus of our work will be to learn to write literate data analysis reports, and to develop skills to critique statistical analyses. Many of the examples in the class will come from the environmental sciences.

The topics will include:

• Univariate and multivariate linear regression: model construction, critique, and correction using graphical residual analysis

• Comparisons of proportions or odds; models for tables of counts

• Logistic regression for binary response variables and binary count data

• Bayesian approaches to model selection

• Log-linear regression for Poisson counts

• Use of a standard statistical software package for applied data analysis: applications and examples emphasizing the biological and environmental sciences

Course Prerequisites

Class Meeting Times: Tuesdays and Thursdays: 12:40 - 1:55. 107 Gross Chem

Computer Lab Sections: As part of the course, students are expected to attend one of the weekly lab sessions, which will emphasize homework questions and computing issues. There are 4 sections of lab on Wednesdays at 130 North Building:

• 8:00-8:50 am
• 9:10-10:00 am
• 10:30-11:20 am
• 11:50am-12:40 pm

Texts:

• Required:

  Fred L. Ramsey and Daniel W. Schafer, The Statistical Sleuth: A Course in Methods of Data Analysis. Duxbury Press, Belmont, CA, 1997. We will cover Chapters 7-12, 18-22.

  • Datasets from the Statistical Sleuth. Case studies at the beginning of each chapter are denoted "Case0102.ASC" (Chapter 1 Case Study 2) and "Ex0102" for exercises.

• Strongly suggested:

  • D. Moore, G. McCabe. Introduction to the Practice of Statistics. W.H. Freeman & Co., 1998.

    • On reserve at Perkins Library and at the Biological Sciences Library.

    • A course at this level is a prerequisite for this course.

  • L. C. Hamilton. Regression with Graphics: A Second Course in Applied Statistics. Duxbury Press, 1992.

Software: