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York University

AK/AS/SC/MATH 2030.03AF, Fall 1999

ELEMENTARY PROBABILITY
Course Outline


Prerequisites:

Single variable calculus (MATH 1300.03/1310.03 or equivalent).

Degree credit exclusions:

MATH2030.06
(Prior to 1993-1994, the course number MATH2030 was assigned to a 6 credit introductory statistics course, which overlapped significantly with the contents of our course)

Instructor:

Tom Salisbury

Lectures:

MWF 8:30-9:20 in CLH K
Atkinson students should note that Faculty of Arts calendar dates apply to this course.

Course Webpage

Office hours:

Monday 10:30-11:20, Friday 12:30-1:20.
If you need to see me outside these hours, you are welcome to drop by my office. If I am able to talk to you then, I will; if not we can arrange another time. Or you can e-mail to arrange an appointment.

Text:

Probability by Pitman; 1st edition, Springer Verlag 1993.
We will cover the first four chapters in detail. If time permits we will cover selected topics from the last two chapters.

Problem session:

Thursday, 12:30-1:20, in N501 Ross (starts 2nd week of classes)

TA:

Adrienne Groulx

Grading:

Course description:

Probability theory is the mathematical underpinning of Statistics, as well as of many areas of physics, finance, and other disciplines. The mathematics of probability will be the topic of this course. The course can be followed by other courses in statistics or application areas, or the mathematics can be pursued further, through more advanced courses in stochastic processes or probability theory. Students contemplating taking actuarial examinations are strongly advised to take this course.

The course will introduce the basic mathematical model of randomness, and will examine the fundamental notions of independence and conditional probability. 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.