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York University

AS/SC/AK/MATH 2030 3.0AF (Fall 2008)

Course Outline


Single variable integral calculus (MATH 1010 3.0 or MATH 1014 3.0 or MATH 1310 3.0 or equivalent).
Integration is used in mainly in the 2nd half of MATH 2030, so in special circumstances students may request permission to take MATH 2030 concurrently with integral calculus.

Instructor/Contact Information:

Tom Salisbury Department of Mathematics and Statistics


MWF 8:30-9:20 in Curtis Lecture Hall C

Course Webpage:

Office hours:

Monday 1:00-2:00, Wednesday 12:00-1:00 (subject to change).
If you need to see me outside these hours, you are welcome to try dropping 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 me to arrange an appointment.

There are no formal tutorials scheduled for this section of the course. But prior to tests I will hold problem sessions in lieu of an office hour.


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.


To be announced


There will be a 20 minute class quiz, two 50 minute midterm test, and a 3 hour final exam (during the university examination period). Homework will be assigned for credit. Other information about grading:

Course description:

Probability theory is the mathematical underpinning of Statistics, as well as many areas of physics, computer science, 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 - this course plus parts of MATH 2131 3.0 will prepare students for the Society of Actuaries "Exam P". The course is required for honours programs in mathematics, applied mathematics, computational mathematics, mathematics for commerce, statistics, and 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.

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