This course is about regression, a powerful and widely used data analysis technique wherein we seek to understand how different random quantities relate to one another. Students will learn how to use regression to analyze a variety of complex real world problems, with the aim of understanding data and prediction of future events. Focus is placed on understanding of fundamental concepts and development of the skills necessary for robust application of regression techniques. Heavy emphasis will be placed on analysis of financial data. Topics covered include: (i) review of simple linear regression; (ii) multiple regression (understanding the model, inference and interpretation for parameters, model building and selection, diagnostics and prediction); (iii) instrumental variable regression and other advanced topics; (iv) time series (autocorrelation functions, auto-regression, prediction); (v) logistic regression. In-class presentations will be primarily conducted in R, while for homework Excel and Matlab can be used as alternatives.
This course is intended for students with some prior exposure to statistics.
The instructor's lecture notes serve as a self-contained text. All of the instructor's notes will be available on the course website.
Based on homework assignments, and a take-home final exam. Cannot be taken pass/fail.
Description and/or course criteria last updated: November 06 2023
