Preliminary Summer Term 2020

If you are planning to enrol in a graduate course at the Department of Statistical Sciences, we recommend you read through this page carefully. Please also note, that this course schedule is subject to changes. We will post changes and updates here, so check back frequently.

Enrolment Dates

Students from the University of Toronto Mississauga (UTM) and University of Toronto Scarborough (UTSC) can enrol in Arts & Science courses starting Wed, April 15, 2020. Students from the Downtown Toronto Campus (St. George) can enrol in Arts & Science courses offered at UTM and UTSC on the same date.

If you have any questions regarding enrolment dates for our graduate courses, please contact our graduate administrator.

 

 

Start and End Dates of Classes & Final Examination Period

Section Code

Classes Start

Classes End

Final Examination

F

Monday, May 4, 2020 June 15, 2020 TBD
Y Monday, May 4, 2020 August 17, 2020 TBD
S Monday, July 6, 2020 August 17, 2020 TBD

 

For dates regarding university closures, course drop and registration deadlines, and tuition payment deadlines, please have a look at the School of Graduate Studies sessional dates calendar.

 

Course List Legend

Summer Session 2020: Course Listings

  • F = a half-year course in the first term (September – December)
  • S = a half-year course in the second term (January– April)
  • Y = a full-year course (September – April)
  • (J) indicates a a cross-listed course (a joint graduate/undergraduate course)
  • M = Monday
  • T = Tuesday
  • W = Wednesday
  • R = Thursday
  • F = Friday
  • L0101 or L0201 = 9:00 am to 5:00 pm
  • L5101 = 5:00 pm onwards

 

 

2020 Summer Term Course Listings

You can also find a list of graduate courses at the School of Graduate Studies page for our department.

 

Course
Title (click for descripiton)
Session
Section/Time
Location
Instructor

STA1001H/STA302H1

Introduction to data analysis with a focus on regression. Initial Examination of data. Correlation. Simple and multiple regression models using least squares. Inference for regression parameters, confidence and prediction intervals. Diagnostics and remedial measures. Interactions and dummy variables. Variable selection. Least squares estimation and inference for non-linear regression.

Prerequisite:

STA238H1/STA248H1/STA255H1/STA261H1/ECO227Y1

CSC108H1/CSC120H1/CSC121H1/CSC148H1

MAT221H1(70%)/MAT223H1/MAT240H1

F

L0101: T2-5, R2-5

BA 1160 TBA
STA1001H/STA302H1

Introduction to data analysis with a focus on regression. Initial Examination of data. Correlation. Simple and multiple regression models using least squares. Inference for regression parameters, confidence and prediction intervals. Diagnostics and remedial measures. Interactions and dummy variables. Variable selection. Least squares estimation and inference for non-linear regression.

Prerequisite:

STA238H1/STA248H1/STA255H1/STA261H1/ECO227Y1

CSC108H1/CSC120H1/CSC121H1/CSC148H1

MAT221H1(70%)/MAT223H1/MAT240H1

S

L5101: M6-9, W6-9

BA 1160 TBA
STA1002H/STA303H1

Analysis of variance for one-and two-way layouts, logistic regression, loglinear models, longitudinal data, introduction to time series.

Prerequisite: STA1001H or equivalent

S L0101: M9-12, W9-12 ES 1050 TBA
STA1003H/STA304H1

Design of surveys, sources of bias, randomized response surveys. Techniques of sampling; stratification, clustering, unequal probability selection. Sampling inference, estimates of population mean and variances, ratio estimation., observational data; correlation vs. causation, missing data, sources of bias.

Exclusion: STA322H1

Prerequisite: ECO220Y1/ECO227Y1/GGR270Y1 / PSY202H1/SOC300Y1/STA221H1/STA255H1/261H1/248H1

F L0101: T2-5, R2-5 PB B150 TBA
STA1004H/STA305H1

This cross-listed course covers a number of topics used in the design and analysis of experiments. The course is intended for students of statistics as well as students of other disciplines (eg. engineering, experimental science, etc.) who will use experimental design and analysis in their work.

The course will cover the following topics: randomization, blocking Latin squares, balanced incomplete block designs, factorial experiments, confounding and fractional replication, components of variance, orthogonal polynomials, response surface methods. Additional topics will be covered based on students’ interest as time permits.

Prerequisite: STA302H/352Y/ECO327Y/ECO357Y or permission of instructor

S L5101: T2-5, R2-5 BA 1160 TBA
STA2202H/STA457H1

An overview of methods and problems in the analysis of time series data. Topics include: descriptive methods, filtering and adjustment, spectral estimation, bivariate time series models.

The course will cover the following topics:

Theory of stationary processes, linear processes

Elements of inference in time domain with applications

Spectral representation of stationary processes

Elements of inference in frequency domain with applications

Theory of prediction (forecasting) with applications > ARMA processes, inference and forecasting

Non-stationarity and seasonality, ARIMA and SARIMA processes

Further topics, time permitting: multivariate models; GARCH models; state-space models

F L5101: W2-5, F2-5 BA 1160 TBA