STAB57H3F: An Introduction to Statistics
Winter 2025 (last updated on Jan 04, 2025)
1 Course Description
Mathematical treatment of the theory of statistics. The topics covered include: the statisti- cal model, data collection, descriptive statistics, estimation, confidence intervals and P-values, likelihood inference methods, distribution-free methods, bootstrapping, Bayesian methods, re- lationship among variables, contingency tables, regression, ANOVA, logistic regression, appli- cations. Statistical software R will be used.
Contents, emphasis, etc. of the course is defined by means of the lecture materials - not only the texts. Table 1 shows the tentative lecture guide. Lecture slides will be uploaded every week. However, they are just rough, point-form notes, with no guarantee of completeness or accuracy. They should in no way be regarded as a substitute for attending the lectures, or for doing the weekly non-credit homework.
Important announcements, problem sets, additional examples, and other course info will be posted on the course web page on Quercus. Check it regularly.
Prerequisite: STAB52H3 or STAB53H3
Exclusion: MGEB11H3, PSYB07H3, STAB22H3, STAB23H3, STA220H1, STA261H
Breadth Requirements: Quantitative Reasoning
2 Course Schedule
• Lec 01: TUE 9 - 11am, THURS 11am - 12pm
• Lec 02: TUE 1 - 3pm, THURS 1 - 2pm
• Instructor: Shahriar Shams,
Assistant Professor (teaching stream), Department of Computer and Mathematical Sci- ences, University of Toronto Scarborough.
• Email: [email protected] (Please add “STAB57” at the beginning of the sub- ject of your email. PLEASE!)
• O ce hours: time and details to be announced later.
3 Textbooks
1. Mathematical Statistics and Data Analysis, 3rd Edition, John A. Rice
2. Probability and Statistics: The Science of Uncertainty, Second Edition, by Michael J. Evans and Jefrey S. Rosenthal
Available online on the web-page of Professors Evans and Rosenthal
http://www.utstat.toronto.edu/mikevans/jeffrosenthal/
4 Homework
Every week after the lecture a set of exercises will be provided. This homework is not for credit. This is only meant to give students opportunities to learn the materials and prepare themselves for the tests and exam. TAs will solve some of the harder problems in the tutorials. Tutorials will start from week-2.
5 Tutorials
The tutorials will start on the second week and run until the last week of class. Tutorials will cover topics taught in the previous week’s lecture. In preparation for the tutorials, you should do weekly non-credit homeworks. There will be short quizzes every week starting from week 3 based on previous week’s lectures and non-credit homeworks. You have to write the quizzes in your assigned tutorial. Quizzes are open-book (students are also allowed to use lecture notes). Default score for a missed quiz is zero. Out of the 10 quiz scores, your best 5 scores will be worth 5% of the course grade. Quiz marks cannot be shifted to other assessments.
6 Assignments for credit
There will be two assignments (each worth 10%) in the course. Both the assignments are take home and will require some hand calculations and some coding in R. The tentative assignment release dates are : mid-February and mid-March. Students will be given reasonable time to finish each of the assignments. Clear instructions will be given on how to complete and submit your work. Crowdmark will be used for all the assessments in the course.
7 Evaluation
• Midterm test: outside of lecture hours, will be scheduled by the office of registrar.
• Final exam: everyone registered in the course will be required to write the exam, will cover everything taught in the course, date and time will be fixed by the office of registrar and will be announced later.
Grades will be calculated using two schemes. The final course grade will be the larger of the two grades.
|
Assessment
|
Scheme 1
|
Scheme 2
|
|
Weekly Quizzes
|
5%
|
5%
|
|
Assignment-1
|
10%
|
10%
|
|
Assignment-2
|
10%
|
10%
|
|
Midterm
|
30%
|
0%
|
|
Final
|
45%
|
75%
|
8 Missed assessment
There are NO make-up assessments of any form in this course.
• Taking the final exam and submitting both the assignments are mandatory for every student in order to pass this course.
• Students are not required to submit any doctor’s note for missing the midterm.
9 Computing
Statistical software R will be used extensively. Students will learn solving probability problems using simulations in R. No previous exposure is expected and R will be introduced starting from the basics. Any code used in the lectures will be available on the course web-page for students to practice at their own time.