Back to 2030 Home Page
York University
AS/SC/AK/MATH 2030 3.0AF (Fall 2008)
ELEMENTARY PROBABILITY
Course Outline
Prerequisites:
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
- 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
Lectures:
MWF 8:30-9:20 in Curtis Lecture Hall C
Course Webpage:
www.math.yorku.ca/~salt/courses/2030f08/2030.html
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.
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.
TA:
To be announced
Grading:
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.
- 5% Class quiz (Tentative date: Friday September 26))
- 20% Midterm test (Tentative date: Friday October 17)
- 20% Midterm test (Tentative date: Monday November 10)
- 15% Assignments (between 7 and 9)
- 40% Final exam
Other information about grading:
- I will mark the midterm and finals. Our TA will mark the
quiz and assignments. Restrictions on TA hours mean that only a
selection of the assignment 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, quizz, 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 a
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.
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.
External resources