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STA 290 Statistical Laboratory
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Instructor: Merlise Clyde
Office: 223 E Old Chemistry Building Office hours: Mon & Wed 3-4 or by appointment Phone: 681-8440 Email: clyde@stat.duke.edu Lecture:: Tuesday - Thursday @ 4:25-5:40, Room 226 Allen Building
TA: Gavino Puggioni
Office: 214-C Old Chemistry Building Office hours: TBA Phone: 684-4558 Email: gp10@stat.duke.edu
This course provides a broad introduction to a range of topics in statistical science and data analysis. Topics will be selected from:
- data types, data manipulation and analysis, including data sets from a variety of application fields -- see the datasets link
- exploratory data analysis and statistical graphics
- elements of statistical inference using probability models, including basic issues of sampling-theory and Bayesian inference
- models for normal data including ANOVA and regression models
- models for binary and count/categorical data
- introduction to hierarchical models
- introduction to statistical programming environments such as R/S-Plus
- elements of simulation and introduction to Gibbs sampling WinBugs
Though the course does not include rigourous development of statistical theory and methods, we will use and review various concepts and methods of inference, so that some familiarity with basic statistics is desirable. Co-registration in STA 213 or recent experience with similar courses is expected. Students will be expected to become familiar with unix tools, including editors such as emacs and document preparation programs such as LaTeX.
Students may find the following list of texts and references useful.
Grading will be based on homework, and inclass midterm and final, and practical takehome exam. Students may also be requested to make classroom presentations.