Researchers are often interested in using samples of data to investigate relationships between variables. Regression analysis is a process of finding the mathematical model that best fits the data. This class provides an intuitive and practical introduction to linear regression, the workhorse method of applied econometrics and a fundamental statistical technique for research in the quantitative social sciences.
The course covers the fundamentals of regression analysis. We begin with a (very) brief review of the necessary ingredients from probability and statistics. From day one, students will learn the basic functionality of the statistical software package Stata, starting with the generation of descriptive statistics and graphics. Coverage of the linear regression model and regression diagnostics constitute the core of the course. Non-linear regression models and other more advanced regression techniques will also be introduced.
This is a hands-on, applied course where students will analyze data drawn from the fields of economics and political science. There will be applied assignments following each topic, which we will discuss as a class at before moving on the next topic. If you fully apply yourself in this course, you will become proficient with the basic methods of regression analysis and you will become a confident user of statistical software for computing linear regressions and interpreting their results.
This course will be offered online.
About the lecturer
Michael Dorsch is Associate Professor of Economics at Central European University, where he teaches Applied Regression Analysis, Public Choice, and Public Sector Economics within the School of Public Policy. Employing formal theoretical modeling and data-driven empirical investigation, his work has appeared in leading academic journals across the quantitative social sciences.
Please note that other external participants than the one mentioned in the target group above are asked to pay a small course fee.