Course Meeting Times

Lectures: 2 sessions / week, 1.5 hours / session

Recitations: 1 session / week, 2 hours / session

Course Description

This course gives an introduction to probability and statistics, introducing students to quantitative uncertainty analysis and risk assessment with emphasis on engineering applications. The course focuses on probability theory and its applications, with a smaller module at the end covering basic topics in statistics (parameter estimation, hypothesis testing and regression analysis). The probability modules cover events and their probability, the total probability and Bayes’ theorems, discrete and continuous random variables and vectors, the Bernoulli trial sequence and Poisson process models, conditional distributions, functions of random variables and vectors, statistical moments, second-moment uncertainty propagation and second-moment conditional analysis, and various probability models such as the exponential, gamma, normal, lognormal, uniform, beta and extreme-type distributions. Throughout the subjects, concepts are illustrated with examples from various areas of engineering and everyday life.


None in probability and statistics, but familiarity with elementary linear algebra (vectors, matrices) and calculus (derivatives, integrals) is assumed.


Ang, Alfredo H-S., and Wilson H. Tang. Probability Concepts in Engineering: Emphasis on Applications to Civil and Environmental Engineering. 2nd ed. New York, NY: John Wiley & Sons, 2006. ISBN: 9780471720645.


Homework 1/3
Mini quizzes (open books and notes) 1/3
Final exam (open books and notes) 1/3

Group Work

Students are encouraged to discuss all course material with one another except for homework assignments, which should be individually done. Interaction to solve suggested problems is appropriate.

Use of Old Solutions

Students should not seek to obtain previous years’ solutions to homeworks. If they have knowledge of such solutions, they should so indicate for each homework problem. Problems for which students had previous knowledge of the solutions will not be used for grading.

Course Info

Learning Resource Types

assignment Problem Sets
grading Exams
notes Lecture Notes