University of Toronto
Major Modification – Type B: Undergraduate
(Proposal for a Specialist or Major where there is an existing Specialist or
Major, and Minors where there is no existing Specialist or Major)
Section 1
What is being proposed:
Minor Program in Applied Statistics
(New Freestanding Minor)
Department where the program will be housed:
Department of Computer & Mathematical
Sciences
Faculty / Academic Division:
University of Toronto Scarborough
Faculty / Academic Division Contact:
Annette Knott, Academic Programs Officer
aknott@utsc.utoronto.ca
Department/Unit Contact:
Michael Evans
mevans@utstat.utoronto.ca
Anticipated start date of the program:
Fall 2013
Version Date:
June 18 2012
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Major Modification Proposal - Type B
Section 2
1. Executive Summary
We propose a new freestanding Minor program in Applied Statistics. The program is targeted at
students in non-mathematical disciplines, for example in the life sciences or social sciences, who
want, or need, a more thorough statistical training. The proposed program contains a suite of
courses that are primarily application oriented. The courses in the program are focused on
methods and interpretation as opposed to mathematics or theory. The skills imparted by the
program will allow students to conduct statistical studies and properly analyse data relevant to
their fields.
2. Program Rationale
Statistical training is important to an increasing number of fields, especially in the life sciences
and the social sciences (including management). Students in these fields, either during their
studies or later in their professional careers, are often required to understand and even produce
statistical analyses of data relevant to their subject. Unfortunately, many of these students do not
have the mathematical background necessary to study statistics at the level that Specialist and
Major programs (or even our existing Statistics Minor program) require. Yet, equipped with
suitable courses, it is possible to teach such students to follow and even conduct statistical
studies. The proposed freestanding Minor is targeted at this group of students.
Notably, the proposed program does not require calculus. It requires an elementary course on
computer programming (increasingly a key skill for statistical analyses), a two-course sequence
on basic principles of statistics (using either generic courses offered by the Department of
Computer and Mathematical Sciences, or discipline-specific statistics courses offered by other
Departments), three new courses (a case-based course in which students learn to write statistical
reports, a course on the principles underlying the proper collection of data, and a course
addressing more advanced statistical techniques such as regression and factor analysis), and
electives from a broad collection of relevant courses. The skills imparted through these courses
will allow students in the life sciences, social sciences, and management to properly analyse data
relevant to their fields.
Currently approximately one thousand students take STAB22H each year but only a few of them
go on to take follow-up statistics courses. In part this is because the subsequent statistics courses
offered to these students are not part of any program. For this reason we are proposing to offer a
freestanding Minor program targeted at these students.
The mode of delivery will be through traditional classes. One of the proposed courses designed
in part to support this program is case-oriented.
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Major Modification Proposal - Type B
3. Need and Demand.
The need for social scientists, biologists, psychologists, and managers to properly interpret data
is widely acknowledged. For this reason, these disciplines often require (or strongly
recommend) statistical training for their students. Indeed, elementary statistics courses for
students in these disciplines are overflowing. We believe, however, that these courses don’t go
far enough. They provide the necessary foundation, but they don’t provide the opportunity for
the students to apply their knowledge and hone their statistical skills in settings similar to those
they will encounter in their academic or professional careers. They also don’t provide other
skills that are increasingly important in applying statistical analyses, notably computational
skills. The proposed program seeks to fill this gap.
The proposed program provides a coherent framework for statistical training. Because of its
modest expectations regarding mathematical background, we believe that it will appeal to a
healthy fraction of the many hundreds of students who take elementary statistics every year but
do not pursue the subject further.
We circulated a draft of the proposed program to the chairs of the following departments:
Biological Sciences, Management, Physical and Environmental Sciences, Psychology, and
Social Sciences. Their responses ranged from support to enthusiastic endorsement, and we take
this as another encouraging sign that the proposed program will be attractive to students.
Table 1: Undergraduate Enrolment Projections
Level of
study
2013
2014
2015
1st year
2nd year
3rd year
4th year
Total
enrolment
0
25
0
0
25
0
25
25
0
50
0
25
25
25
75
2016
(steadystate)
0
25
25
25
75
4. Admission / Eligibility Requirements
Students can enter the program after completing STAB27H. There is no enrolment limit.
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Major Modification Proposal - Type B
5. Program Requirements
Program Requirements:
MINOR PROGRAM IN APPLIED STATISTICS (SCIENCE)
This program requires a total of 4.0 credits as follows:
One (0.5 credits) of:
CSCA08H
CSCA20H
One (0.5 credits) of:
STAB22H
ECMB11H
PSYB07H
SOCB06H
One (0.5 credits) of:
STAB27H
ECMB12H
PSYC08H
Introduction to Computer Programming
Computer Science for the Sciences
Statistics I
Quantitative Methods in Economics I
Data Analysis in Psychology
Social Statistics
Statistics II
Quantitative Methods in Economics II
Advanced Data Analysis in Psychology
All of the following (1.5 credits):
STAC32H
Applications of Statistical Methods (*)
STAC50H
Data Collection (*)
STAD29H
Statistics for Life and Social Scientists
Two (1.0 credits) of:
any ACT, CSC, MAT, STA course
ECMA04, ECMA06H, ECMB02H, ECMB06H, ECMC11H, ECMD10H, ECMD70H
GGRB02H
HLTB15H, HLTC15H
MGTB09H, MGTC71H, MGTHC74H, MGTD07H, MGTD30H
POLB11H
(*) Forms for the new required courses STAC32H – Applications of Statistical Methods and
STAC50H – Data Collection are being submitted alongside this proposal. These courses will also
service our STA major and minor programs, and other proposed programs (specialist in statistics,
and health informatics stream of the computer science specialist).
List of Courses:
CSCA08H3 Introduction to Computer Programming
Structure of computers; the computing environment. Programming in an object-oriented
language such as Python. Program structure: elementary data types, statements, control flow,
functions, classes, objects, methods, fields. Lists; searching, sorting and
complexity.
Prerequisite: Any Grade 12 mathematics course. Note: This course is intended for
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Major Modification Proposal - Type B
students with no prior exposure to computer programming. Students who have sufficient
programming experience may enrol directly in CSCA48H3; consult the instructor or the
Supervisor of Studies for guidance. Exclusion: CSCA20H3, CSC108H, CSC120H.
CSCA08H3 may not be taken after or concurrently with CSCA48H3.
Breadth Requirement:
Quantitative Reasoning
CSCA20H3 Computer Science for the Sciences
An introduction to computer science for students in other sciences, with an emphasis on gaining
practical skills. Introduction to programming; web programming; database design; software
tools; examples and exercises taken from the sciences. At the end of this course you will be able
to develop computer tools for scientific applications, such as the structuring and analysis of
experimental data.
Exclusion: CSCA08H3, CSC108H, CSC120H
Breadth Requirement:
Quantitative Reasoning
ECMB11H3 Quantitative Methods in Economics I
An introduction to probability and statistics as used in economic analysis. Topics to be covered
include: descriptive statistics, probability, special probability distributions, sampling theory,
confidence intervals. Enrolment is limited to students registered in programs requiring this
course.
Prerequisite: ECMA04H3 & ECMA06H3 & [MATA32H3 & MATA33H3] (or
equivalents). Students who have completed ECMA01H3 & ECMA05H3 & [MATA32H3 &
MATA33H3] (or equivalents) may be admitted with the permission of the Supervisor of Studies.
Exclusion: ANTC35H3, ECO220Y, ECO227Y, PSYB07H3, SOCB06H3, STAB22H3,
STAB52H3, STAB57H3
Enrolment Limits: 120 per section
Breadth Requirement:
Quantitative Reasoning
ECMB12H3 Quantitative Methods in Economics II
A second course in probability and statistics as used in economic analysis. Topics to be covered
include: confidence intervals, hypothesis testing, simple and multiple regression. Enrolment is
limited to students registered in programs requiring this course.
Prerequisite: [ECMB11H3 or
[STAB52H3 & STAB57H3]] & [MATA32H3 & MATA33H3] (or equivalents) Exclusion:
ECO220Y, ECO227Y, STAB27H3, STAC67H3. Note: STAB27H3 is not equivalent to
ECMB12H3
Enrolment Limits: 80 per section
Breadth Requirement: Quantitative
Reasoning
PSYB07H3 Data Analysis in Psychology
This course focuses on the fundamentals of the theory and the application of statistical
procedures used in research in the field of psychology. Topics will range from descriptive
statistics to simple tests of significance, such as Chi-Square, t-tests, and one-way Analysis-ofVariance. A working knowledge of algebra is assumed. Students in the Specialist programs in
Psychology, Psycholinguistics or Neuroscience will be given priority for this course.
Exclusion:
ANTC35H3,ECMB11H3, ECMB12H3, PSY201H, SOCB06H3, STAB22H3, STA220H,
STA221H, STA250H, STA257H
Breadth Requirement: Quantitative Reasoning
PSYC08H3 Advanced Data Analysis in Psychology
This course is a continuation of PSYB07H3. The primary focus of this course is on the
understanding of Analysis-of-Variance and its application to various research designs. Examples
will include a priori and post hoc tests. Finally, there will be an introduction to multiple
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Major Modification Proposal - Type B
regression, including discussions of design issues and interpretation problems. Prerequisite:
[PSYB07H3 or SOCB06H3 or STAB22H3] & one additional B-level half-credit in
Psychology
Exclusion: STAC52H3, PSY202H
Breadth Requirement: Quantitative Reasoning
SOCB06H3 Social Statistics
A consideration of elementary statistics including the summarizing of data, the logic of statistical
decision-making and a number of common statistical tests. Statistics is a basic tool used by
sociologists. An understanding of statistics is necessary for the student who wants to become an
informed reader of social research. A working knowledge of elementary algebra is required.
However, the lecturer will undertake brief reviews of mathematics as the need
arises. Prerequisite: SOCA01H3 & SOCA02H3
Exclusion: ANTC35H3, ECMB11H3,
POLB11H3, PSYB07H3, SOC202H, (SOC300Y), STAB22H3
Enrolment Limits:
170
Breadth Requirement: Quantitative Reasoning
STAB22H3 Statistics I
This course is a basic introduction to statistical
reasoning and methodology, with a minimal amount of mathematics and calculation. The course
covers descriptive statistics, populations, sampling, confidence intervals, tests of significance,
correlation, regression and experimental design. A computer package is used for calculations.
Exclusion: ANTC35H3, ECMB11H3, POLB11H3, PSYB07H3, SOCB06H3, STAB52H3,
STAB57H3, STA220H, STA250H
Breadth Requirement: Quantitative Reasoning
STAB27H3 Statistics II
This course follows STAB22H3, and gives an introduction to regression and analysis of
variance techniques as they are used in practice. The emphasis is on the use of software to
perform the calculations and the interpretation of output from the software. The course reviews
statistical inference, then treats simple and multiple regression and the analysis of some standard
experimental designs.
Prerequisite: STAB22H3
Exclusion: ECMB12H3, STAB57H3, STA221H, STA250H
Breadth Requirement: Quantitative Reasoning
STAC32H3 Applications of Statistical Methods [NEW COURSE]
A case-study based course, aimed at developing students’ applied statistical skills beyond the
basic techniques. Students will be required to write statistical reports. Statistical software, such
as SAS and R, will be taught and used for all statistical analyses.
Prerequisite: STAB27H3 or STAB57H3 or equivalents
Breadth Requirement: Quantitative Reasoning
STAC50H3 Data Collection [NEW COURSE, REPLACES STAC52H3]
The principles of proper collection of data for statistical analysis, and techniques to adjust
statistical analyses when these principles cannot be implemented. Topics include: relationships
among variables, causal relationships, confounding, random sampling, experimental designs,
observational studies, experiments, causal inference, meta-analysis. Statistical analyses using
SAS or R.
Prerequisite: STAB27H3 or STAB57H3
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Major Modification Proposal - Type B
Breadth Requirement: Quantitative Reasoning
STAD29H3 Statistics for Life & Social Scientists
The course discusses many advanced statistical methods used in the life and social sciences.
Emphasis is on learning how to become a critical interpreter of these methodologies while
keeping mathematical requirements low. Topics covered include multiple regression, logistic
regression, discriminant and cluster analysis, principal components and factor analysis.
Prerequisite: STAC32H3
Exclusion: All C-level or D-level STA courses except STAC32H3, STAC50H3, and STA322H3.
Breadth Requirement: Quantitative Reasoning
6. Learning Outcomes
Students will learn how to understand and carry out statistical analyses for quite sophisticated
problems, and how to communicate the results effectively. This includes ensuring that data are
both collected and analyzed appropriately. Students in this program will be able to apply these
statistical skills in a variety of disciplines.
7. Program Structure and Degree Level Expectations
Majors and Specialists: N/A
Minors:
Degree Level Expectations
How does the program link with
scholarship and rigour in the discipline?
Does it address the current state of the
area of study?
How will students gain a knowledge of
methodologies?
What are the connections, if any, with
activities outside the classroom?
What skills, competencies or expertise
will students completing the program
have gained?
Will the program prepare students for
further study? Please elaborate.
How the program design / structure supports the degree level
expectations
This program is not intended to train students to become statistical
professionals, but to equip them with the tools that will enable them to
understand and carry out statistical analyses. The program will reflect
the state of the art of statistical methodology.
Through the material they will be taught in their courses and the
associated assignments and projects they will be required to carry out.
The development of statistical skills will help students in developing
research skills in their individual disciplines.
The ability to use current statistical software to carry out statistical
analyses.
Mostly in the primary disciplines with which students will complement
this program. Students wishing to pursue further statistical studies
would have followed a major or specialist in statistics.
8. Assessment of Teaching and Learning
.
Assessing students’ progress will be based on methods that are standard in the mathematical
sciences. These include quizzes, tests, exams, at-home assignments, and individual or group
projects. STAC32H will require student to prepare statistical reports.
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Major Modification Proposal - Type B
9. Consultation
The proposed program relies only on courses offered by CMS and has no impact on other
Departments’ resources. We circulated a draft of this proposal to the Chairs of the Departments
of Biological Sciences, Management, Psychology, Physical and Environmental Sciences, and
Social Sciences; all expressed support for the proposal.
10. Resources:
Participants in this program include all the Statistics faculty, namely Michael Evans, Ken Butler,
Mahinda Samarakoon, Mike Moras, Russ Salakhutdinov, Sotirios Damouras. All have been
involved in the preparation of this proposed program. Professor Ken Butler will serve as
Program Supervisor.
Table 2: Detailed listing of committed faculty
Faculty name and rank
Home unit
Area(s) of Specialization
Ken Butler – Lect.
Sotirios Damouras – Lect.
Michael Evans – Prof.
Michael Moras – Lect.
Russ Salakhutdinov – Asst. Prof.
Mahinda Samarakoon – Lect.
Computer and Math. Sc.
“
“
“
“
“
Statistics
“
“
“
“
“
a.
Space/Infrastructure
There are no special infrastructure requirements for this proposed program. It can be delivered
with the present faculty complement in statistics.
11. Governance Process:
Levels of Approval Required
Departmental Curriculum Committee
Dean’s Office Sign Off
UTSC Divisional Governance
Submission to Provost’s Office
Report to AP&P
Report to Ontario Quality Council
Dates
May 11, 2012
August 15, 2012
Developed by the Office of the Vice-Provost, Academic Programs: April 4, 2011
Revised by the Office of the Dean and VP (Academic): 24 February, 2012
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Major Modification Proposal - Type B