Statistics
The Statistics Department offers a range of courses that build on a foundation in probability and statistical theory to provide practical training in statistical methods, study design, and data analysis.
Check the Directory of Classes for the most up-to-date course information.
Summer 2022 Session Information
- SESSION A (First Half Term) courses are May 23–July 1, 2022
- SESSION B (Second Half Term) courses are July 5–August 12, 2022
- SESSION X (Full Term) courses are May 23–August 12, 2022
Visit our calendar for a complete list of Summer dates.
Courses
INTRO TO STATISTICAL REASONING
STAT1001W001 3 points.
A friendly introduction to statistical concepts and reasoning with emphasis on developing statistical intuition rather than on mathematical rigor. Topics include design of experiments, descriptive statistics, correlation and regression, probability, chance variability, sampling, chance models, and tests of significance.
Course Number | Section/Call Number | Session | Times/Location | |
---|---|---|---|---|
STAT1001W001 | 001/10120 | Session B |
Mo 10:45 AM–12:20 PM Tu 10:45 AM–12:20 PM We 10:45 AM–12:20 PM Th 10:45 AM–12:20 PM |
|
Instructor | Points | Enrollment | Method of Instruction | |
Gabriel Young |
3 |
Open for Enrollment (auto-fill Wait List) |
In-Person |
INTRODUCTION TO STATISTICS
STAT1101S001 3 points.
Course Number | Section/Call Number | Session | Times/Location | |
---|---|---|---|---|
STAT1101S001 | 001/10121 | Session A |
Mo 10:45 AM–12:20 PM Tu 10:45 AM–12:20 PM We 10:45 AM–12:20 PM Th 10:45 AM–12:20 PM |
|
Instructor | Points | Enrollment | Method of Instruction | |
Anthony Donoghue |
3 |
Open for Enrollment (auto-fill Wait List) |
In-Person |
INTRODUCTION TO STATISTICS
STAT1101S002 3 points.
Course Number | Section/Call Number | Session | Times/Location | |
---|---|---|---|---|
STAT1101S002 | 002/10122 | Session B |
Mo 06:15 PM–07:50 PM Tu 06:15 PM–07:50 PM We 06:15 PM–07:50 PM Th 06:15 PM–07:50 PM |
|
Instructor | Points | Enrollment | Method of Instruction | |
Arnab Auddy |
3 |
Open for Enrollment (auto-fill Wait List) |
In-Person |
CALC-BASED INTRO TO STATISTICS
STAT1201S001 3 points.
Course Number | Section/Call Number | Session | Times/Location | |
---|---|---|---|---|
STAT1201S001 | 001/10123 | Session A |
Mo 10:45 AM–12:20 PM Tu 10:45 AM–12:20 PM We 10:45 AM–12:20 PM Th 10:45 AM–12:20 PM |
|
Instructor | Points | Enrollment | Method of Instruction | |
Ji Meng Loh |
3 |
Open for Enrollment (auto-fill Wait List) |
In-Person |
CALC-BASED INTRO TO STATISTICS
STAT1201S002 3 points.
Course Number | Section/Call Number | Session | Times/Location | |
---|---|---|---|---|
STAT1201S002 | 002/10124 | Session B |
Mo 06:15 PM–07:50 PM Tu 06:15 PM–07:50 PM We 06:15 PM–07:50 PM Th 06:15 PM–07:50 PM |
|
Instructor | Points | Enrollment | Method of Instruction | |
Ye Tian |
3 |
Open for Enrollment (auto-fill Wait List) |
In-Person |
Undergraduate Mentored Research
STAT3107W001 3 points.
Prerequisites: the project mentors permission. This course provides a mechanism for students who undertake research with a faculty member from the Department of Statistics to receive academic credit. Students seeking research opportunities should be proactive and entrepreneurial: identify congenial faculty whose research is appealing, let them know of your interest and your background and skills.
Course Number | Section/Call Number | Session | Times/Location | |
---|---|---|---|---|
STAT3107W001 | 001/10459 | Session A |
|
|
Instructor | Points | Enrollment | Method of Instruction | |
Ronald Neath |
3 |
Open for Enrollment (auto-fill Wait List) |
In-Person |
INTRODUCTION TO PROBABILITY AND STATISTICS
STAT4001S001 3 points.
Prerequisites: A good working knowledge of calculus, including derivatives, single and double, limits, sums and series. Life is a gamble and with some knowledge of probability / statistics is easier evaluate the risks and rewards involved. Probability theory allows us take a known underlying model and estimate how likely will we be able to see future events. Statistical Inference allows us to take data we have seen and estimate the missing parts of an unknown model. The first part of the course focus on the former and the second part the latter.
Course Number | Section/Call Number | Session | Times/Location | |
---|---|---|---|---|
STAT4001S001 | 001/10125 | Session A |
Mo 06:15 PM–07:50 PM Tu 06:15 PM–07:50 PM We 06:15 PM–07:50 PM Th 06:15 PM–07:50 PM |
|
Instructor | Points | Enrollment | Method of Instruction | |
Tat Sang Fung |
3 |
Open for Enrollment (auto-fill Wait List) |
In-Person |
PROBABILITY THEORY
STAT4203S001 3 points.
Course Number | Section/Call Number | Session | Times/Location | |
---|---|---|---|---|
STAT4203S001 | 001/10126 | Session A |
Mo 04:30 PM–06:05 PM Tu 04:30 PM–06:05 PM We 04:30 PM–06:05 PM Th 04:30 PM–06:05 PM |
|
Instructor | Points | Enrollment | Method of Instruction | |
Young Kim |
3 |
Open for Enrollment (auto-fill Wait List) |
In-Person |
STATISTICAL INFERENCE
STAT4204S001 3 points.
Course Number | Section/Call Number | Session | Times/Location | |
---|---|---|---|---|
STAT4204S001 | 001/10127 | Session A |
Mo 06:15 PM–07:50 PM Tu 06:15 PM–07:50 PM We 06:15 PM–07:50 PM Th 06:15 PM–07:50 PM |
|
Instructor | Points | Enrollment | Method of Instruction | |
David Rios |
3 |
Open for Enrollment (auto-fill Wait List) |
In-Person |
Linear Regression Models
STAT4205S001 3 points.
Prerequisites: STAT GU4204 or the equivalent, and a course in linear algebra. Theory and practice of regression analysis. Simple and multiple regression, testing, estimation, prediction, and confidence procedures, modeling, regression diagnostics and plots, polynomial regression, colinearity and confounding, model selection, geometry of least squares. Extensive use of the computer to analyse data.
Course Number | Section/Call Number | Session | Times/Location | |
---|---|---|---|---|
STAT4205S001 | 001/10128 | Session A |
Mo 06:15 PM–07:50 PM Tu 06:15 PM–07:50 PM We 06:15 PM–07:50 PM Th 06:15 PM–07:50 PM |
|
Instructor | Points | Enrollment | Method of Instruction | |
Daniel Rabinowitz |
3 |
Open for Enrollment (auto-fill Wait List) |
In-Person |
STAT COMP & INTRO DATA SCIENCE
STAT4206S001 3 points.
Course Number | Section/Call Number | Session | Times/Location | |
---|---|---|---|---|
STAT4206S001 | 001/10129 | Session A |
Mo 04:30 PM–06:05 PM Tu 04:30 PM–06:05 PM We 04:30 PM–06:05 PM Th 04:30 PM–06:05 PM |
|
Instructor | Points | Enrollment | Method of Instruction | |
Gabriel Young |
3 |
Open for Enrollment (auto-fill Wait List) |
In-Person |
Time Series Analysis
STAT4221S001 3 points.
Course Number | Section/Call Number | Session | Times/Location | |
---|---|---|---|---|
STAT4221S001 | 001/10130 | Session A |
Mo 01:00 PM–04:10 PM We 01:00 PM–04:10 PM |
|
Instructor | Points | Enrollment | Method of Instruction | |
Rongning Wu |
3 |
Open for Enrollment (auto-fill Wait List) |
In-Person |
BAYESIAN STATISTICS
STAT4224W001 3 points.
This course introduces the Bayesian paradigm for statistical inference. Topics covered include prior and posterior distributions: conjugate priors, informative and non-informative priors; one- and two-sample problems; models for normal data, models for binary data, Bayesian linear models; Bayesian computation: MCMC algorithms, the Gibbs sampler; hierarchical models; hypothesis testing, Bayes factors, model selection; use of statistical software.
Prerequisites: A course in the theory of statistical inference, such as STAT GU4204 a course in statistical modeling and data analysis, such as STAT GU4205.
Course Number | Section/Call Number | Session | Times/Location | |
---|---|---|---|---|
STAT4224W001 | 001/10131 | Session B |
Mo 06:15 PM–07:50 PM Tu 06:15 PM–07:50 PM We 06:15 PM–07:50 PM Th 06:15 PM–07:50 PM |
|
Instructor | Points | Enrollment | Method of Instruction | |
Daniel Rabinowitz |
3 |
Open for Enrollment (auto-fill Wait List) |
In-Person |
STATISTICAL MACHINE LEARNING
STAT4241S001 3 points.
Course Number | Section/Call Number | Session | Times/Location | |
---|---|---|---|---|
STAT4241S001 | 001/10132 | Session A |
Mo 02:45 PM–04:20 PM Tu 02:45 PM–04:20 PM We 02:45 PM–04:20 PM Th 02:45 PM–04:20 PM |
|
Instructor | Points | Enrollment | Method of Instruction | |
Banu Baydil |
3 |
Open for Enrollment (auto-fill Wait List) |
In-Person |
STATISTICAL METHODS FOR FINANCE
STAT4261S001 3 points.
Course Number | Section/Call Number | Session | Times/Location | |
---|---|---|---|---|
STAT4261S001 | 001/10133 | Session A |
Tu 09:00 AM–12:10 PM Th 09:00 AM–12:10 PM |
|
Instructor | Points | Enrollment | Method of Instruction | |
Hammou El Barmi |
3 |
Open for Enrollment (auto-fill Wait List) |
In-Person |
PROBABILITY
STAT5203W001 3 points.
Prerequisites: At least one semester of calculus. A calculus-based introduction to probability theory. Topics covered include random variables, conditional probability, expectation, independence, Bayes rule, important distributions, joint distributions, moment generating functions, central limit theorem, laws of large numbers and Markovs inequality.
Course Number | Section/Call Number | Session | Times/Location | |
---|---|---|---|---|
STAT5203W001 | 001/10134 | Session A |
Mo 04:30 PM–06:05 PM Tu 04:30 PM–06:05 PM We 04:30 PM–06:05 PM Th 04:30 PM–06:05 PM |
|
Instructor | Points | Enrollment | Method of Instruction | |
Young Kim |
3 |
Open for Enrollment (auto-fill Wait List) |
In-Person |
STATISTICAL INFERENCE
STAT5204W001 3 points.
Prerequisites: STAT GR5203 or the equivalent, and two semesters of calculus. Calculus-based introduction to the theory of statistics. Useful distributions, law of large numbers and central limit theorem, point estimation, hypothesis testing, confidence intervals, maximum likelihood, likelihood ratio tests, nonparametric procedures, theory of least squares and analysis of variance.
Course Number | Section/Call Number | Session | Times/Location | |
---|---|---|---|---|
STAT5204W001 | 001/10135 | Session A |
Mo 06:15 PM–07:50 PM Tu 06:15 PM–07:50 PM We 06:15 PM–07:50 PM Th 06:15 PM–07:50 PM |
|
Instructor | Points | Enrollment | Method of Instruction | |
David Rios |
3 |
Open for Enrollment (auto-fill Wait List) |
In-Person |
LINEAR REGRESSION MODELS
STAT5205W001 3 points.
Prerequisites: STAT GR5203 and GR5204 or the equivalent. Theory and practice of regression analysis, Simple and multiple regression, including testing, estimation, and confidence procedures, modeling, regression diagnostics and plots, polynomial regression, colinearity and confounding, model selection, geometry of least squares. Extensive use of the computer to analyse data.
Course Number | Section/Call Number | Session | Times/Location | |
---|---|---|---|---|
STAT5205W001 | 001/10440 | Session A |
Mo 06:15 PM–07:50 PM Tu 06:15 PM–07:50 PM We 06:15 PM–07:50 PM Th 06:15 PM–07:50 PM |
|
Instructor | Points | Enrollment | Method of Instruction | |
Daniel Rabinowitz |
3 |
Open for Enrollment (auto-fill Wait List) |
In-Person |
STAT COMP & INTRO DATA SCIENCE
STAT5206S001 3 points.
Course Number | Section/Call Number | Session | Times/Location | |
---|---|---|---|---|
STAT5206S001 | 001/10136 | Session A |
Mo 04:30 PM–06:05 PM Tu 04:30 PM–06:05 PM We 04:30 PM–06:05 PM Th 04:30 PM–06:05 PM |
|
Instructor | Points | Enrollment | Method of Instruction | |
Gabriel Young |
3 |
Open for Enrollment (auto-fill Wait List) |
In-Person |
Time Series Analysis
STAT5221S001 3 points.
Course Number | Section/Call Number | Session | Times/Location | |
---|---|---|---|---|
STAT5221S001 | 001/10137 | Session A |
Mo 01:00 PM–04:10 PM We 01:00 PM–04:10 PM |
|
Instructor | Points | Enrollment | Method of Instruction | |
Rongning Wu |
3 |
Open for Enrollment (auto-fill Wait List) |
In-Person |
BAYESIAN STATISTICS
STAT5224W001 3 points.
This course introduces the Bayesian paradigm for statistical inference. Topics covered include prior and posterior distributions: conjugate priors, informative and non-informative priors; one- and two-sample problems; models for normal data, models for binary data, Bayesian linear models, Bayesian computation: MCMC algorithms, the Gibbs sampler; hierarchical models; hypothesis testing, Bayes factors, model selection; use of statistical software.
Prerequisites: A course in the theory of statistical inference, such as STAT GU4204/GR5204 a course in statistical modeling and data analysis such as STAT GU4205/GR5205.
Course Number | Section/Call Number | Session | Times/Location | |
---|---|---|---|---|
STAT5224W001 | 001/10138 | Session B |
Mo 06:15 PM–07:50 PM Tu 06:15 PM–07:50 PM We 06:15 PM–07:50 PM Th 06:15 PM–07:50 PM |
|
Instructor | Points | Enrollment | Method of Instruction | |
Daniel Rabinowitz |
3 |
Open for Enrollment (auto-fill Wait List) |
In-Person |
STATISTICAL MACHINE LEARNING
STAT5241S001 3 points.
Course Number | Section/Call Number | Session | Times/Location | |
---|---|---|---|---|
STAT5241S001 | 001/10139 | Session A |
Mo 02:45 PM–04:20 PM Tu 02:45 PM–04:20 PM We 02:45 PM–04:20 PM Th 02:45 PM–04:20 PM |
|
Instructor | Points | Enrollment | Method of Instruction | |
Banu Baydil |
3 |
Open for Enrollment (auto-fill Wait List) |
In-Person |
STATISTICAL METHODS FOR FINANCE
STAT5261S001 3 points.
Course Number | Section/Call Number | Session | Times/Location | |
---|---|---|---|---|
STAT5261S001 | 001/10140 | Session A |
Tu 09:00 AM–12:10 PM Th 09:00 AM–12:10 PM |
|
Instructor | Points | Enrollment | Method of Instruction | |
Hammou El Barmi |
3 |
Open for Enrollment (auto-fill Wait List) |
In-Person |
TOPICS IN MODERN STATISTICS
STAT5293G001 3 points.
Topics in Modern Statistics will provide MA Statistics students with an opportunity to study a specialized area of statistics in more depth and to meet the educational needs of a rapidly growing field.
Course Number | Section/Call Number | Session | Times/Location | |
---|---|---|---|---|
STAT5293G001 | 001/12408 | Session B |
|
|
Instructor | Points | Enrollment | Method of Instruction | |
Emanuel Ben-David |
3 |
Open for Enrollment (auto-fill Wait List) |
On-Line Only |
MA Mentored Research
STAT5398G002 1 points.
This course is intended to provide a mechanism to MA students in Statistics who undertake on-campus project work or research. The course may be signed up with a faculty member from the Department of Statistics for academic credit. Students seeking to enroll in the course should identify an on-campus project and a congenial faculty member whose research is appealing to them, and who are able to serve as their mentor. Students should then submit an application to enroll in this course, which will be reviewed and approved by the Faculty Director of the MA in Statistics program.
Course Number | Section/Call Number | Session | Times/Location | |
---|---|---|---|---|
STAT5398G002 | 002/11262 | X Summer Session |
|
|
Instructor | Points | Enrollment | Method of Instruction | |
Xiaofei Shi |
1 |
Registration Block (no Adds) (self-man. Wait List) |
On-Line Only |
MA Mentored Research
STAT5398G003 1 points.
This course is intended to provide a mechanism to MA students in Statistics who undertake on-campus project work or research. The course may be signed up with a faculty member from the Department of Statistics for academic credit. Students seeking to enroll in the course should identify an on-campus project and a congenial faculty member whose research is appealing to them, and who are able to serve as their mentor. Students should then submit an application to enroll in this course, which will be reviewed and approved by the Faculty Director of the MA in Statistics program.
Course Number | Section/Call Number | Session | Times/Location | |
---|---|---|---|---|
STAT5398G003 | 003/13284 | R Summer Session |
|
|
Instructor | Points | Enrollment | Method of Instruction | |
Demissie Alemayehu |
1 |
Registration Block (no Adds) (self-man. Wait List) |
On-Line Only |