Indiana University Bloomington

Political Science Y576
Political Data Analysis Ii (linear Regression Model)

Contact: Chris DeSante (cdesante@indiana.edu)
Offered: Spring, Every Year
Class Time: 2:30-4:30 pm
Class Days: Tu
Capacity: 10
Pre-Requisites: introductory statistics, high school algebra
Algebra Required: Yes for notation, proofs, homework
Calculus Required: concepts used, but not required
Instructor: Chris DeSante (cdesante@indiana.edu)
Recommended follow-up classes: S503, Y577
Syllabus: No Syllabus Avaliable
Keywords: computational statistics, statistical programming, econometrics, regression
Description: A continuation of the graduate sequence in political science, this class begins with multivariate regression, its assumptions and violations before moving on to models of limited dependent variables (binary, count, duration, etc.), measurement models, and handling complex data structures. Emphasis will be on the interpretation and presentation of substantive results using R. Pre-requisite: PS Y575 or permission of the instructor. Students will be asked to produce an original piece of research that will be presented in a public poster session at the end of the semester.
Books: None required.
Substantive Orientation: Social Sciences
Statistical Orientation: Applied Statistics, Computational Statistics
Applied/Theoretical: This is mostly an applied course, but the applications and programming problems will be driven by statistical theory. Students will be expected to present either an original research study or a replication of a published paper at the end of the semester.
Software: R
How Software is Used: R and R studio will be used to write code for statistical algorithms
Presentation: Students will be required to present an original research study or replication of a published study