Y575: Introduction To Computational Statistics And Programming
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.
Semester(s) Offered: Fall
Class time: Thursday : 4-6pm
Instructor: Chris DeSante - email@example.com
Other Contact(s): Chris DeSante - firstname.lastname@example.org
Algebra Required?: High School Algebra
Calculus Required?: No
Day(s) per week offered: 1
Recommended follow-up classes: POLS Y576
Substantive Orientation: Social Sciences
Statistical Orientation: Applied Statistics, Computational Statistics
Books used: Statistical Methods for the Social Sciences (4th Edition), by Alan Agresti and Barbara Finlay. Pearson, 2008. ISBN: 0205646417 A Beginner's Guide to R, by Zuur et al.. Springer, 2009. ISBN: 9780387938363
Applied/Theoretical: This is mostly an applied course, but the applications and programming problems will be driven by statistical theory.
Software Used: R
How the software is used: R and R studio are used to write statistical algorithms