Indiana University Bloomington

SPEA V507
Data Analysis And Modeling

Contact: Barry Rubin (rubin@indiana.edu) 11-2-12
Offered: Spring, Every Year
Capacity: 50 per section
Sequence: SPEA V506: Statistical Analysis for Effective Decision-Making
Pre-Requisites: SPEA V506 or E538, or equivalent graduate-level course in introductory statistics with applied focus
Algebra Required: Not used
Calculus Required: Not used
Contact Person for Authorization: None
Instructor: Barry Rubin, Haeil Jung, Evan Ringquist
Days Per Week Offered: Two lectures per week, one computer lab per week
Recommended follow-up classes: Multivariate analysis, general linear models, categorical data analysis, time-series analysis
Syllabus: Download Syllabus
Keywords: applied regression; glm assumptions; correcting multicollinearity, heteroskedasticity, autocorrelation; logit models
Description: Intermediate-level perspective on statistical concepts and techniques for analyzing and modeling complex systems via regression analysis. Includes estimating the parameters of such models based on existing data, testing hypotheses about these systems, forecasting, correcting for violations of assumptions, and dealing with commonly encountered problems such as near multcollinearity. Primarily focused on single equation regression models and the extension of these models to a variety of situations, but includes an introduction to simultaneous equation models. Application of these techniques to problems and policies in public and environmental affairs, as well as general social sciences.
Books: Lecture notes; Gujarati and Porter, Basic Econometrics, 5th ed., 2009
Substantive Orientation: SPEA, Social Sciences, Education, Business, Telecommunications, Anthropology, Biological and Health Sciences (E538), Informatics
Statistical Orientation: Non-experimental
Applied/Theoretical: Applied with explanations (not derivations) of statistical foundations
Software: SAS
How Software is Used: Data analysis, some programming
Problem Sets: Yes, involving analysis and interpretation of data
Data Analysis: Yes, as part of problem sets and in-class exercises
Presentation: Yes, small group term project with student obtained data set; includes presentation to class
Exams: Yes, midterm and final
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