Undergraduate Degree Programs:
The Honours in
Statistics Program (4 years) will
provide students with a comprehensive knowledge of both theoretical
and applied statistics and will enable students to move easily into
challenging employment or graduate work in statistics. The
Combined Honours Program (4 years)
allows students to study Statistics and another scientific discipline
at an advanced level. The Advanced Major in Statistics Program (4 years) provides a broad
coverage of the discipline but is slightly less demanding than the
Honours program. The Advanced Double Major Program
(4 years) allows specialization in two subjects at a slightly less
intense level than does the Combined Honours Program. The
Major in Statistics Program (3 years)
provides a student with a basic background in statistics. Co-operative
Education Program. Available as an option with any degree other
than the 3-year Major degree. Please click
here for
additional information.
Contact Information:
Statistics Director
Dalhousie University
Halifax, N.S., Canada B3H 3J5
Phone: (902)494-2572
FAX: (902)494-5130
E-mail:
statdir@mathstat.dal.ca
Additional
information is available on the
Registrar's Office
Statistics Page
Registration information, university calendar and timetable are
available at the
Registrar's Office Home Page
Courses:
STAT 1060.03 : Introductory Statistics for Science and Health
Sciences. This class gives an introduction to the basic concepts
of statistics through extensive use of real-life examples drawn from a
variety of disciplines. The first part of the class is about designing
experiments properly and then describing and summarizing the results
of the studies by using descriptive statistics. From there we move to
analyzing relationships between variables. In the final part of the
class, we develop the basics of statistical inference explaining how
to make valid generalizations from samples to populations. Both
estimation and hypothesis testing are carried out for one and two
sample problems for both means and proportions as well as for simple
linear regression. Students will learn to use the statistical package
MINITAB. The natural sequel for this class is STAT 2080.03. Other
possibilities are STAT 2060.03 and STAT 2050.03. Credit will not be
given for STAT 1060.03 if credit has previously been obtained for STAT
2060.03.
STAT 2060.03 : Introduction to Probability and Statistics.
Rigorous introduction to probability and statistical theory. Subject
matter is developed systematically beginning with the fundamentals of
probability and following with statistical estimation and testing. The
interrelationship between probability theory, mathematical statistics
and data analysis will be emphasized. Topics covered include
elementary probability, random variables, distributions, estimation
and hypothesis testing. Estimation and testing are introduced using
maximum likelihood and the generalized likelihood ratio. Natural
sequels for this class are STAT 2080.03 and 3360.03
STAT 2080.03 : Statistical Methods for Data Analysis and
Inference. The usual sequel to STAT 1060.03 or STAT 2060.03. This
class introduces a number of techniques for data analysis and
inference commonly used in the experimental sciences. The class begins
with an introduction to model building in linear models and develops
the techniques required for multiple regression. From here we consider
analysis of variance, factorial designs, analysis of covariance using
the general techniques for linear models. The last part of the class
will include techniques for two and three way tables along with
logistic regression. The use of a computer package for carrying out
the computations will be an integral part of the class. Students will
design and carry out a simple experiment as part of this class. A
natural sequel for this class is STAT 3340.03 or STAT 3345.03.
STAT 2050.03 : Exploratory Data Analysis. This class is
designed to introduce the student to exploratory data analysis and
graphical techniques making extensive use of statistical software such
as S-plus. Data sets from both experimental and observational studies
will be used extensively and the emphasis will be on finding patterns
and structure in the data. The student completing the class will be
able to do sophisticated graphing, data reduction and data handling.
The skills learned will be very useful in several of the advanced
statistics classes.
STAT 3340.03 : Regression and Analysis of Variance. A
thorough treatment of the theory and practice of regression analysis.
Topics include: fitting general linear models using matrices,
optimality of least squares estimators (Gauss-Markov theorem),
inferences, simple and partial correlation, analysis of residuals,
case-deletion diagnostics, polynomial regression, transformations, use
of indicator variables for analysis of variance and covariance
problems, model selection, and an introduction to nonlinear least
squares. This class makes extensive use of computer packages.
STAT 3345.03 : Environmental Risk Assessment. Statistical
methods for assessing risk are discussed, including dose-response
models, survival analysis, relative risk analysis, bioassay,
estimating methods for zero risk trend analysis and association risks.
Case studies are used to illustrate the methods.
STAT 3350.03 : Design of Experiments. The aim of the class
is to develop the fundamental statistical concepts required for
designing efficient experiments to answer real questions. The first
main subject is unit variation and control. The basic concepts of
replication, blocking and randomization are each examined. The second
main subject is treatment questions and structure. The ideas of
factorial designs, split-plot and incomplete plot designs are
presented. We conclude with a look at response surface methodology.
STAT 3360.03 : Probability. The concepts and application of
probability. Topics include the classical discrete and continuous
distributions, including the binomial, hypergeometric, multinomial,
Poisson, uniform, exponential and normal; definitions and properties
of random variables; independence; sums of independent random
variables, including the law of large numbers and central limit
theorem; conditional probability; and the bivariate normal
distribution. Examples will be taken from the natural and physical
sciences.
STAT 3380.03 : Sample Survey Methods. The development of
design and analysis techniques for sample surveys. Topics include
simple, stratified and systematic random sampling, ratio and
regression estimation, sub-sampling with units of equal and unequal
size, double-multistage and multiphase sampling, non-sample errors and
non-respondents.
STAT 3460.03 : Intermediate Statistical Theory. This class
provides an intermediate level coverage of statistical theory to
provide a framework for valid inferences from sample data. The methods
developed are based on the likelihood function and are discussed from
the frequentist, likelihood, and Bayesian approaches |