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MATH 2030 3.0MW (Winter 2011)
Elementary Probability
Course Outline


Single variable integral calculus (MATH 1014 3.0 or MATH 1310 3.0 or equivalent).

Course Webpage


MWF 8:30-9:20 in CB121 (Chemistry building)

Instructor/Contact Information:

Tom Salisbury Department of Mathematics and Statistics

Office hours:

Wednesday 10:00-11:00, Friday 11:00-12:00.
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.


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.


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 such as Operations Research or Actuarial Science. Alternatively, the mathematical component 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, as it is one of the courses that "Exam P" of the Society of Actuaries is based on. The course is required for most programs at York involving mathematics, statistics, or computer science.

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.