1 Thank you

This course is FINISHED. Thank you everyone! Best of luck!

1.1 Information of the course

The purpose of this page is to hold some of the additional materials provided by myself. Students should consult UW Learn system.

1.1.1 Course description

This course provides an introduction to probability models including sample spaces, mutually exclusive and independent events, conditional probability and Bayes’ Theorem. The named distributions (Discrete Uniform, Hypergeometric, Binomial, Negative Binomial, Geometric, Poisson, Continuous Uniform, Exponential, Normal (Gaussian), and Multinomial) are used to model real phenomena. Discrete and continuous univariate random variables and their distributions are discussed. Joint probability functions, marginal probability functions, and conditional probability functions of two or more discrete random variables and functions of random variables are also discussed. Students learn how to calculate and interpret means, variances and covariances particularly for the named distributions. The Central Limit Theorem is used to approximate probabilities.

1.1.2 Instructor

Chi-Kuang Yeh, I am a postdoc at the Department of Statistics and Actuarial Science.

  • Office: M3–3102 Desk 10, but I hold office hour at M3 - 2101 Desk 1, 9:30 – 10:30 on Tuesday.
  • Email: chi-kuang.yeh@uwaterloo.ca

1.1.3 Course Coordinator

Dr. Erik Hintz.

1.1.4 Logistic Issue

Contact Divya Lala

1.1.5 Assessments and Dates

Tutorial Quiz (3% each)

Tutorial Test (5% each)

Midterm (12.5% each)

Final (50%)

Real World Assignment (1%)

BONUS: iClicker Participation (2%)

1.2 Chapters and associated Lectures

Those chapters are based on the lecture notes. The lecture covered is based on Section 002. This part will be updated frequently.

Chapter Lecture Covered
1. Introduction to Probability L1
2. Mathematical Probability Models L2–3
3. Probability and Counting Techniques L3–6
4. Probability rules and Conditional Probability 6–9
5. Discrete Random Variable L10 –16
6. Computational Methods and the Statistical Software R In tutorial (not testable)
7. Expected Value and Variance L16–20
8. Continuous Random Variable L20–27
9. Multivariate Distributions L27–33
10. Central Limit Theorem and Moment Generating Function L33–35