Course Meeting Times

Lectures: 2 sessions / week, 1.5 hours / session


This course is a broad treatment of statistics, concentrating on specific statistical techniques used in science and industry. Topics include: Hypothesis testing and estimation, confidence intervals, chi-square tests, nonparametric statistics, analysis of variance, regression, correlation, decision theory, and Bayesian statistics.


Probability and Random Variables (18.440) or Probabilistic Systems Analysis (6.041) or the equivalent (one semester of calculus-based probability). The prerequisite material is also covered in the required textbook (see below), Chapters 1–5.


Rice, John A. Mathematical Statistics and Data Analysis. Duxbury Press, 2006. ISBN: 9780534399429. [Preview with Google Books ]

Statistics Package

We will use the R, a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. For more information and to download the software, visit The R Project for Statistical Computing site .

We will also use RStudio , a free and open source integrated development environment (IDE) for R. RStudio Desktop , allows the program to run locally as a regular desktop application.

Assignments & Exams

There are 10 problem sets. None will be due in weeks when there are exams. During the term there are three exams, equally spaced during the course. There is no final exam.


Participation 10%
Assignments 45%
Three Exams (15% each) 45%

Course Info

Learning Resource Types

assignment_turned_in Problem Sets with Solutions
notes Lecture Notes
group_work Projects