2009 Fall STA 121-01L
Bulletin Course Description Extensive study of regression modeling. Multiple regression, weighted least squares, logistic regression, log-linear models, analysis of variance, model diagnostics and selection. Emphasis on applications. Examples drawn from a variety of fields. Instructor: Reiter, Stangl, Clyde
(Instructor named in bulletin description above may not be current. For current instructor, see listing below.)
Title REGRESSION ANALYSIS Department STA Course Number 2009 Fall 121 Section Number 01L Primary Instructor Stangl,Dalene K Primary Instructor Armagan,Artin Prerequisites Prerequisite: 100-level statistics course. Permission of Director of Undergraduate Studies required for courses outside Statistical Science.
Synopsis of course content
This course is the follow-up course to the introductory statistics courses taught at ISDS. It is ideal for undergraduates who ultimately wish to do quantitative analysis for research projects and honors theses.
Topics may include: multivariate data analysis; model construction and critique; inference for discrete and continous regression models; analysis of variance, linear regression, logistic and probit regression, time series, and survival models. Special emphasis on examples drawn from the student's major field, the use of statistical software, the written summary of statistical data analysis, and critical analysis of published quantitative analysis
Textbooks
Ramsey and Schafer (2002), The Statistical Sleuth.
Additional Information
This course is a second course in statistics. It is appropriate for any student who has taken statistics and wants to learn more. The course focuses on methods for analyzing complicated datasets. Students need not be working on an honors thesis to take the course. It is recommended that students taking the course to learn data analysis methods for their honors theses complete it by their junior year.
