General Information #
This is the course website for STAT 3150: Statistical Computing. This course aims to provide students with a broad overview of computational techniques used in modern statistical analysis. Throughout the course, students will:
- Become proficient in
R, to the level that they can analyse data using the tools from this class.
- Learn how to sample from various distributions, directly and indirectly.
- Design and conduct a simulation to study a particular statistical question.
- Become familiar with several resampling techniques and know which one to use for a particular problem.
- Be introduced to numerical methods and optimisation techniques.
Course Details #
- Instructor: Max Turgeon
- Email: email@example.com
- Office: 373 Machray Hall
- Website: https://maxturgeon.ca/f21-stat3150/
- Lectures: TR 10:00 AM–11:15 AM, via Zoom
- Office Hours:
- By appointment only
STAT 2150 (Statistics and Computing) and STAT 2400 (Introduction to Probability 1)
Statistical Computing with R (2nd ed.) by Maria L. Rizzo, CRC Press, 2019.
The textbook is not required but strongly recommended.
The assessments for this course include:
- Six (6) assignments.
- Two (2) midterm tests.
- One (1) final exam.
Outline of Topics #
The course is expected to cover the following topics:
- Numerical Methods (Chapter 13)
- Introduction to Optimisation (Chapter 14)
- Generating Random Variables (Chapter 3)
- Monte Carlo Integration (Chapter 6)
- Importance Sampling (Chapter 6)
- Monte Carlo Methods for Inference (Chapter 7)
- Bootstrap and Jackknife (Chapter 8)
- Resampling Applications (Chapter 9)
- Permutation Tests (Chapter 10)
Statistical Software #
The course requires you to make extensive use of the R statistical software for your assignments and final data project. Sample codes will be provided to students.
You can download R for free (for Windows, Mac, Linux, and Solaris) from the Comprehensive R Archive Network at: https://cran.r-project.org/
For additional resources on R, see here.