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Department of Computer and Mathematical Sciences

STAC51: Categorical Data Analysis

Winter 2021

Instructor: Sohee Kang

E-mail: sohee.kang@utoronto.ca

Office: IC 483

Online Office Hours: Monday 5-6 pm and Wednesday 5-6 pm

(416) 208-4749

TA: Bo Chen TA: Lehang Zhong

E-mail: bojacob.chen@mail.utoronto.ca E-mail: lehang.zhong@mail.utoronto.ca

Course Description: In this course we discuss statistical models for categorical data. Contingency

tables, generalized linear models, logistic regression, multinomial responses, logit models for

nominal responses, log-linear models for two-way tables, three-way tables and higher dimensions,

models for matched pairs, repeated categorical response data, correlated and clustered responses

and statistical analyses using R. The students will be expected to interpret R codes and outputs

on tests and the exam.

Prerequisite(s): STAB27H3 or STAB57H3 or MGEB12H3 or PSYC08H3

Credit Hours: 3

Required Text: An Introduction to Categorical Data Analysis, 3rd Edition

Author(s): Alan Agresti

WebLink for 2nd edition: https://search.library.utoronto.ca/details?7961944

Sub-text1: Categorical Data with R, 3rd edition

Author: Alan Agresti

Sub-text2: Analysis of Categorical Data with R (2014)

Author:Bilder C. and Loughin T.

Course Objectives:

At the completion of this course, students will be able to:

1. use R software to conduct categorical data analysis.

2. identify designs of contingency tables and recommend appropriate measures of association

and statistical tests.

3. develop models for binary response and polytomous categorical responses, interpret results

and diagnose model fits.

4. interpret and communicate categorical data methods to a technical audience.

1

Grade Components:

Case Study and Presentation 15%

Assignments 15%

Quizzes 15%

Midterm Exam 20%

Final Exam 30%

Attendance 5 %

Course Policy:

• Communication

– Important announcements, lecture notes, additional material, and other course info will

be posted on Quercus. Check it regularly. You are responsible for keeping up with

announcements from instructors on Quercus and via e-mail.

– Check “Piazza” before you send an e-mail, make sure that you are not asking for

information that is already on “Piazza”. In general, I will not answer questions about

the course material by e-mail. Such questions are more appropriately discussed during

office hours of me or TAs.

– E-mail is appropriate for private communication. Use your utoronto.ca account and

include STAC51 in the subject line.

• Oral Assessment

If the instructor has a suspicion on your assessment result (the deviance is great) then she

will conduct an oral assessment after. If the oral assessment result confirms the suspicion

then the previous assessment score will be replaced to 0.

• No makeup quizzes or exams will be given.

Learning Components:

• Tutorial

Students are expected to attend the weekly tutorial to gain practical R programming experience.

Quizzes will be conducted in tutorial. You need to turn on videos so that TAs can

invigilate.

• Assignments

Three assignments (each 5%) will be distributed. All assignments are group works (two team

members) unless you prefer individual work.

• Quiz

Three quizzes (each 5%) will take place after the assignments handed in.

• Case Study and Presentation

Students will be required to work on a case study as a group and to submit a report. The

size of the group is maximum of FOUR. You can choose your group members. For a report,

students will write R codes and interpret R outputs and will use R Markdown (R package).

More details, such as the content and deadline, will be communicated later. No late report

will be accepted. Each group will present the case study (5 minutes) at the last day of

lecture.

2

• Attendance Attendance is expected and will be taken each class and tutorial.

• Computing Statistical computing is a key part of the class. In-class analysis will be conducted

in R and all course material (code and data) is in R format. R is free and available for

download at http://www.r-project.org, and you can find manuals and installation guidelines

on this site.

For basics in R, here are suggested documents: R for beginners by Emanuel Paradis, An

Introduction to R by W. N. Venables, D. M. Smith, and the R Core Team, A (very) short

introduction to R by Paul Torfs and Claudia Brauer. More information and documentation

are available on The R Project website. Students are expected to write R codes and interpret

R outputs on assignments, tests, and the exam.

Outline of Topics:

Chapter Content

Ch. 1

• Introduction

• Distributions for categorical data

• Statistical inference for categorical data

Ch. 2 • Describing contingency tables, independence of categorical variables

• Comparing proportions, Relative risk, Odds ratio

Ch. 2 • Inference for contingency tables, Chi-squared tests of independence

• Exact tests for small samples

Ch. 3 • Introduction to Generalized Linear Models: Generalized linear models for binary

data, Poisson log linear models, Negative binomial GLMs

Ch. 4 • Logistic Regression

Ch. 5 • Building, Checking, and applying logistic regression models.

Ch. 6 • Models for multinomial responses.

Ch. 7 • Loglinear models for two-way tables, Loglinear models for three-way tables,

Inference for loglinear models.

Ch 8 • Models for matched pairs.

3

University Policies

• Academic Integrity:

Academic integrity is essential to the pursuit of learning and scholarship in a university,

and to ensuring that a degree from the University of Toronto is a strong signal of each students

individual academic achievement. As a result, the University treats cases of cheating

and plagiarism very seriously. The University of Torontos Code of Behaviour on Academic

Matters (http://www.governingcouncil.utoronto.ca/policies/behaveac.htm) outlines the behaviours

that constitute academic dishonesty and the processes for addressing academic offences.

Potential offences include, but are not limited to:

In papers and assignments:

– Using someone elses ideas or words without appropriate acknowledgment.

– Submitting your own work in more than one course without the permission of the instructor.

– Making up sources or facts.

– Obtaining or providing unauthorized assistance on any assignment.

On tests and exams:

– Using or possessing unauthorized aids.

– Looking at someone elses answers during an exam or test.

– Misrepresenting your identity.

In academic work:

– Falsifying institutional documents or grades.

– Falsifying or altering any documentation required by the University, including (but not

limited to) doctors notes.

All suspected cases of academic dishonesty will be investigated following procedures outlined

in the Code of Behaviour on Academic Matters. If you have questions or concerns about what

constitutes appropriate academic behaviour or appropriate research and citation methods, you

are expected to seek out additional information on academic integrity from your instructor

or from other institutional resources (see http://www.utoronto.ca/academicintegrity/).

• Accessibility:

Students with diverse learning styles and needs are welcome in this course. In particular,

if you have a disability/health consideration that may require accommodations, please feel

free to approach me and/or the AccessAbility Services Office as soon as possible. I will

work with you and AccessAbility Services to ensure you can achieve your learning goals in

this course. Enquiries are confidential. The UTSC AccessAbility Services staff (located in

S302) are available by appointment to assess specific needs, provide referrals and arrange

appropriate accommodations (416) 287-7560 or ability@utsc.utoronto.ca.

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