Course Overview
This page focuses on the course 18.440 Probability and Random Variables as it was taught by Prof. Scott Sheffield in Spring 2014.
This course introduces students to probability and random variables. Topics include distribution functions, binomial, geometric, hypergeometric, and Poisson distributions. The other topics covered are uniform, exponential, normal, gamma and beta distributions; conditional probability; Bayes theorem; joint distributions; Chebyshev inequality; law of large numbers; and central limit theorem.
Course Outcomes
Course Goals for Students
- Define key probability and random variable concepts.
- Understand the relationships between key concepts.
- Apply understanding of probability and random variables in problem solving.
Curriculum Information
Prerequisites
Requirements Satisfied
REST 
Offered
Every fall and spring semester
Assessment
The students’ grades were based on the following activities:
- 20% Homework problems
- 40% Midterm exam
- 40% Final exam
Student Information
Enrollment
107 students
Breakdown by Year
Mostly juniors and seniors
Breakdown by Major
1/2 math majors, 1/2 non-math majors
How Student Time Was Spent
During an average week, students were expected to spend 12 hours on the course, roughly divided as follows:
In Class/Lecture
- Met 3 times per week for 1 hour per session; 38 sessions total.
- The instructor used both slides and the blackboard to engage students with the material during lectures.
Out of Class
- Problem sets
- Exam preparation
- Optional office hours
