STA 230: Probability:Spring 2017

Prof:Sayan Mukherjee OH: Wednesday 2:15-3:15112 Old Chem
Yimeng Jia OH: Wed. 12-1, Fri 9-10, 221A Old Chem
Liyu GongOH: Mon 11:30-12:30, Fri 11:30-12:30, 221A Old CHem
Wenli ShiOH: Tue 11:30-12:30, Th 11:30-12:30 221A Old Chem
Class:T/Th 1:25-2:40pm Old Chem 116


This is a basic calculus-based first course in the theory and application of probability. It develops quantitative methods for solving problems that involve uncertainty, and provides a foundation for the further study of statistics or random processes. Many probability calculations are based on summing infinite series or on evaluating integrals so calculus at the level of MTH112* is a prerequisite for this course. If you are unsure about your calculus preparation, try this diagnostic quiz.

The course text is Jim Pitman, Probability. You will also have access to Elementary Probability for Applications by Rick Durrett on Sakai.

In the syllabus below I have posted for each lecture what section in Pitman or Durrett the material is covered in.

I have also posted in the syllabus below links to video lectures by Jonathan Mattingly and Joe Blitzstein that cover the lecture material as well.

For each lecture (typically one lecture will cover two classes). I have posted where in both books the material is covered, links to videos covering the material, a short note on the key topics/ideas, in these lectures, and the homework.

The homework, quiz, and exam scores will be reported through Sakai. Also before every class you will be expected to take a short quiz. This is to make sure you have looked at the material before class.

Inverted classroom

This course uses the inverted classroom approach. This means that students will either watch video lectures and/or read book chapters before class. Class will be used for exercises and examples that highlight the core concepts in the material. To ensure that students go through materials before class there is quiz to take online that needs to be completed the day before each lecture. The class exercises will be group exercises to help solidify concepts in the lectures and give some examples of how these concepts are used. The exercises will be done in small groups (3-4) and will typically involve pencil-and-paper work. Sometimes you will also run some code (in Rstudio) which will be provided in advance to help illustrate ideas.


The course grade is based on two midterms (20%) each, quizes (15%), homeworks (15%), final (30%). The exams are in class and closed books. The quizes will be completed online via Sakai.

No make-up exams will be given and there will be no make-ups for homeworks or quizes. You cannot pass the class if you do not take the final.

Homework is graded out of 100. Late work will receive no credit. Lowest homework score will be dropped. Even if you have an excused absence you must turn in your homework.

There is a Piazza course discussion page. Please direct questions about homeworks and other matters to that page. Otherwise, you can email the instructors (TAs and professor) at Note that we are more likely to respond to the Piazza questions than to the email, and your classmates may respond too, so that is a good place to start.


  1. (Jan 12, 17) Outcomes and events:

  2. (Jan 19, 24) Conditional probability:

  3. (Jan 26, 31) Distributions I: Binomial, Poisson, Normal:

  4. (Feb 2, 7) Distributions II: Hypergeometric, Multinomial, Counting:

  5. (Feb 9, 14) Random variables: Expectations, Variances, Moments:

  6. (Feb 16, 21) Continuous random variables: Cummulative distributions, Probability densities, Change of variables, Order statistics:

  7. (Feb 23) Review

  8. (Feb 28) Exam 1: Histogram of results

  9. (March 2, 7, 9) Joint distributions: Marginals, Covariance, and Correlation

  10. (March 21, 23) Conditional distributions and expectations:

  11. (Mar 28, 30) Law of large numbers:

  12. (Apr 4)) Review: Problems 2-7 and Solutions and Another exam

  13. (Apr 6) Exam 2 Histogram of results Mean: 42.5

  14. (April 11,13) Central limit theorem:

  15. (April 18, 20) Markov chains:

  16. (April 25) Review: Final and Solutions and Another exam and Another exam

  17. (May 2: 7-10pm) Final exam Histogram for Sta230 Histogram for Math230