## Y575: Introduction To Computational Statistics And Programming

### Class 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.

### Class Information

**Semester(s): **Fall

**Semester(s) Offered: ** Fall

**Class time: **
Thursday : 4-6pm

**Capacity: ** 10

### Contact Information

**Instructor: **Chris DeSante - cdesante@indiana.edu

**Other Contact(s): ** Chris DeSante - cdesante@indiana.edu

### Other Details

**Prerequisites: ** None

**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