Diagram showing the difference between statistics and probability. (Image by MIT OpenCourseWare. Based on Gilbert, Norma. Statistics. W.B. Saunders Co., 1976.)
Material in 15.075 is presented through its comprehensive set of lecture notes
. Students receive hands-on experience with statistical software through the assignments
and with examples in the lecture notes.
This course is an introduction to applied statistics and data analysis. Topics include collecting and exploring data, basic inference, simple and multiple linear regression, analysis of variance, nonparametric methods, and statistical computing. It is not a course in mathematical statistics, but provides a balance between statistical theory and application. Prerequisites are calculus, probability, and linear algebra.
We would like to acknowledge the contributions that Prof. Roy Welsch (MIT), Prof. Gordon Kaufman (MIT), Prof. Jacqueline Telford (Johns Hopkins University), and Prof. Ramón León (University of Tennessee) have made to the course material.
*Some translations represent previous versions of courses.