STAT 3150 - Fall 2020

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.
  • Be able to choose and produce an appropriate data visualization given the context.
  • Learn how to sample from various distributions, directly and indirectly.
  • 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 #

Prerequisites #

STAT 2150 (Statistics and Computing) and STAT 2400 (Introduction to Probability 1)

Textbook #

Statistical Computing with R (2nd ed.) by Maria L. Rizzo, CRC Press, 2019.

The textbook is not required but strongly recommended.

Assessments #

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:

  • Data Visualization (Chapter 5)
  • 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)
  • Numerical Methods (Chapter 13)
  • Introduction to Optimisation (Chapter 14)

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:

For additional resources on R, see here.