and describing distributions, normal distibution. Relationships
between variables, regression and correlation. The need for design,
experimental design and sampling design. Sampling distributions, bias,
variability. Probability models, random variables, probability laws.
Statistics is a collection of methods for observing and analyzing
numerical data in order to make sensible decisions about them.
In these courses the basic ideas of the analysis of data and of
statistical inference will be introduced.
Little mathematical background is required; high school algebra
is sufficient. Mathematical proofs will be minimal; reasoning
and explanations will be based mostly on intuition, verbal
arguments, figures, or numerical examples. Most of the examples
will be taken from our daily life; many deal with the behavioural
sciences, while others come from business, the life sciences, the
physical sciences, and engineering.
Although students will be making some use of the computer to
calculate statistics, to create statistical plots, and to obtain
a better appreciation of statistical concepts, no
experience in computing is required. Students will receive in
class all the necessary instruction about how to use the
statistical computer package Minitab.
Students who have taken MATH 2560 3.0 will normally take
MATH 2570 3.0 in the second semester, where they will continue
to investigate many basic statistical methods.
The text will be D.S. Moore and G.P. McCabe, .
The grading scheme has not yet been determined.
Prerequisite:Ontario Grade 12 Advanced Mathematics.
Exclusions:AS/SC/MATH 1131 3.0,
SC/BIOL 3080 3.0, SC/BIOL 3090 3.0, AS/ECON 2500 3.0,
AS/SC/GEOG 2420 3.0, AS/SC/KINE 2050 3.0,
AS/SC/PHED 2050 3.0, AS/SC/PSYC 2020 6.0,
AS/SC/PSYC 2021 3.0, AS/SOCI3030 6.0,
AK/MATH 1720 6.0, AK/MATH 2430 6.0,
AK/BIOL 3080 6.0, AK/BIOL 3080 3.0, AK/PSYC 2510 3.0.
Not open to any student who
has successfully completed AS/SC/MATH 2030 6.0.
Coordinator: Fall: M. Asgharian. Winter: T.B.A.