Statistics

Immerse yourself in the world of equations, predictive modeling, and computation in our statistics program.

Statistics studies the collection, description, analysis, and interpretation of data. It is a data-driven science about how numbers help us understand the world, predict future events, and analyze information. Statistics uses mathematics to understand practical problems, as well as specialized tools for prediction and forecasting using data and models.

Statistics is heavily used in scientific fields and research contexts.

It is also used in areas like business, social sciences, government, and manufacturing.

At the U of R, you can take one of four bachelor’s degree programs and one certificate program in statistics:

Bachelor of Science in Statistics

In this program you will take courses in statistics and probability, as well as theoretical and applied courses. Statisticians at the Bachelor of Science level are needed in industry, government, and academics (colleges, technical schools). The Bachelor of Science in Statistics is also a pathway to graduate studies, which can lead to a variety of skilled careers such as a statistician or data scientist.

Bachelor of Science Honours in Statistics

In the honours program, you will learn to be proficient in high level statistical reasoning that prepares you for careers in the non-academic sector and for continued studies at the masters and doctoral level.

Bachelor of Science in Applied Mathematics and Statistics

Graduates of this program will be proficient in the uses of mathematics, especially calculus, matrix algebra, differential equations, probability, and statistics. You will also be exposed to computer science and higher level mathematical reasoning in the form of mathematical proof.

Bachelor of Science in Statistics and Economics (combined major)

The combined degree in Statistics and Economics is designed for students with an interest in both fields. This program provides a broad understanding of the fundamental concepts involved in both subjects.

Certificate in Statistics

The two-year certificate is ideal for students who are majoring in another subject, but wish to acquire this formal credential to recognize their foundational knowledge of statistics. Because of the prerequisite structures of the program, the certificate requires at least four semesters to complete. Completion of the certificate can be a ladder into the Bachelor of Science in Statistics.

What is Statistics

Statistics is an academic subject that collects data for research purposes and provides theories and methods to analyze the data and make scientific judgments about uncertain outcomes that are of interest. It is a branch of applied mathematics. Statistics has two main sub-categories: descriptive statistics and inferential statistics. Statistical models and methods can be used in almost all industries and academic research disciplines that seek meaningful results from data. Here are a few examples of practical projects:

• Predict full season results of Major League Baseball players based on their historical performance measures (e.g., hitting averages)
• Classify customers based on spending behaviour (e.g., the number of shops to use, the average time to spend in a shop, the average amount to spend, etc.)
• Predict whether someone will have a certain type of cancer based on demographic, diet and clinical measurements
• Identify the economic impact of epidemic animal diseases and the control programs

The rapid development and increased use of information and communication technologies produce a vast amount of data that can be used for many purposes. Recently, there has been an increased demand for statistical specialists, such as statisticians or data scientists, who analyze and use large amounts of data. Statisticians apply their knowledge to solve problems in almost every field of science, technology, and business.

Some courses offered in statistics at the U of R include:

Introduction to Statistical Inference

This course includes sampling distribution theory and the central limit theorem; large sample theory; methods of estimation and hypothesis testing including maximum likelihood estimation, likelihood ratio testing, and confidence interval construction.

Statistical Learning and Predictive Modeling

This course examines selected topics and techniques in statistical learning and predictive modeling, including linear models, logistic regression models, regression trees, classification models and statistical software.

Sampling Theory

Sampling Theory examines simple random sampling; systematic sampling; stratified and cluster sampling; ratio and regression estimators.

Time Series Analysis and Forecasting

This course introduces various statistical models for time series and covers the main methods for analysis and forecasting.