Indiana University Bloomington

Political Science Y575
Introduction To Computational Statistics And Programming

Contact: Chris DeSante (cdesante@indiana.edu)
Offered: Fall, 2016
Class Time: 4-6 pm
Class Days: Th
Capacity: 10
Pre-Requisites: None
Algebra Required: High School Algebra
Calculus Required: No
Instructor: Chris DeSante (cdesante@indiana.edu)
Days Per Week Offered: 1
Recommended follow-up classes: POLS Y576
Syllabus: No Syllabus Avaliable
Keywords: computational statistics, introduction to statistics, statistical programming
Description: This course is designed to introduce graduate students to statistical inference with an emphasis on simulation, programming in R and producing replicable research. Students will be able to produce computational algorithms for analysis of social science data. This class should impart a set of skills that are crucial for understanding current quantitative research and enable graduate students to begin producing empirical research. Topics will include: descriptive and summary statistics, probability theory, classical tests of hypotheses (T, Z and Chi-Squared tests), correlation, regression and Monte Carlo simulation.
Books: Statistical Methods for the Social Sciences (4th Edition), by Alan Agresti and Barbara Finlay. Pearson, 2008. ISBN: 0205646417

A Beginner´┐Żs Guide to R, by Zuur et al.. Springer, 2009. ISBN: 9780387938363
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.
Software: R
How Software is Used: R and R studio are used to write statistical algorithms