|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. Examples are used throughout to illustrate application of the tools. 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) time series (autocorrelation functions, auto-regression, prediction); (iv) logistic regression.|
|The instructor's lecture notes serve as a self-contained text. There is no CoursePack. All of the instructor's notes will be available on the course website.|
|Based on homework assignments and group projects, a midterm exam, and a take-home final exam. Cannot be taken pass/fail.|
|Business 41000 or familiarity with the topics covered in Business 41000. This course is intended for students with a solid background in statistics and preferably some prior exposure to linear regression.
Description and/or course criteria last updated: 07/2012
|Sample Exam Questions/Problem Sets:|
|Sample questions available to registered students on course Chalk site.|
|Course Conditions and Course Related Items:|