Course Outline

- email: salt@yorku.ca
- Office: N536 Ross Building
- personal homepage
- Phone: (416) 736-2100 extension 33921

- Departmental office: N520 Ross Building, (416) 736-5250, FAX: (416) 736-5757
- Undergraduate Program office: N502/503 Ross Building, (416) 736-5902
- Math/Stat lab: S525 Ross Building

I will try to post a notice on the course webpage if other commitments make it necessary to reschedule one or more office hour. If you need to see me outside these hours, you are welcome to e-mail or call me to try to arrange an appointment.

We will cover the first four chapters in detail. If time permits we will cover selected topics from the last two chapters.

- 20% Midterm exam (Tentative date: Friday Feb 11)
- 20% Midterm exam (Tentative date: Friday Mar 18)
- 15% Assignments (between 7 and 9)
- 45% Final exam
- Restrictions on TA hours mean that only a selection of the assigned problems will be marked.
- No late assignments will normally be accepted, but I will drop everybody's worst assignment mark.
- Assignments may be handed in in class or dropped in the course mailbox (one of the brown boxes by the north elevator of the 5th floor of Ross will soon have our course number on it).
- All assignment and exam marks should be interpreted as raw scores and not "percentages". Cutoffs will be announced for converting midterm scores into letter grades. The distribution of scores will be announced for both the midterms.
- There will be no makeup midterm examinations. If you miss ar midterm exam due to illness, and can supply an acceptable note from your doctor, then I will give more weight to your final examination results. This will be done by calculating an equivalent midterm score based on your ranking on the final.
- Students are responsible for reviewing the Student Information Sheet maintained by the university, which outlines policies on academic honesty, access and disability, religious observance accommodation, and student conduct.

The course will introduce the basic mathematical model of randomness, and will examine the fundamental notions of independence and conditional probability. It covers the mathematics used to calculate probabilities and expectations, and discusses how random variables can be used to pose and answer interesting problems arising in nature. Calculations will be based both on combinatorial methods and on integral calculus. A variety of concrete distributions will be studied (Normal, Binomial, Poisson, etc, together with their multivariate generalizations), using density functions, distribution functions, and moment-generating functions. Prior exposure to statistics or combinatorics would be useful, but is not assumed.