2B Introduction to Quantitative Data Analysis
Sona N. Golder, Pennsylvania State University
22 July - 2 August (two week course / 35 hrs)
Detailed Course Outline [PDF]
Course Content
This course introduces students to quantitative data analysis and can be thought of as two courses in one. In the first week, we will cover the basics of probability theory before moving on to consider different distributions of discrete and random variables. We then turn to hypothesis testing, which ranges from comparing means across two samples in order to draw inferences about the relative population means to determining whether two variables are independent of each other. In the second week, we will cover Ordinary Least Squares regression analysis, which will allow us to build more sophisticated models that allow us to test more complex hypotheses. We will address the Gauss-Markov assumptions and what to do when these assumptions are violated. The primary emphasis is on identifying statistical techniques appropriate to the question being examined, correctly applying those techniques, and then computing quantities of interest (based on estimates of model parameters). Throughout the course, students will become familiar with using STATA.
Course Objectives
The central objective of this course is to learn the foundations of quantitative analysis in general and then to apply that knowledge to the use of linear models.
Course Prerequisites
This is an introductory course. Some knowledge of probability theory and STATA code would be helpful but is not required.
Reading:
Nagler, Jonathan. 1995. Coding Style and Good Computing Practices. PS: Political Politics 28(3): 488-492.
Wonnacott, Thomas H. and Ronald J. Wonnacott. 1990. Introductory Statistics. (5th ed). New York: John Wiley Sons.