Taking a statistics class opens a window to understanding the world, says Hong Gu, Director of the Statistics Program. "Through this window you can look into anything you want. You use our philosophy and methodology to get an insight into the other sciences. Indeed, without statistics, a lot of scientific progress would not have been possible."
"Data drives science, especially now with the internet," agrees Joanna Mills-Flemming, an assistant professor of statistics.
Statistics provides you with a methodology and numeracy to meet any academic goal. We offer a well-rounded set of courses that builds a solid quantitative base for any student, whether they major in statistics, economics, psychology, biology, sociology or chemistry.
We teach you how to apply data to research. You'll have opportunities to work on special projects and write code that can run powerful statistical software right from your laptop. At the advanced level, we cross-list our fourth year courses with graduate courses, so our senior students can study alongside grad students.
"The profs are really approachable the classes are really small," says student Sarah Ambrose. "I have one course with three people so that’s pretty amazing. They’ll email you back within a day or you can just go and knock on their office door and they’re there. That’s really helpful."
STAT 3340Regression 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.
Prerequisites: STAT 2080.03, MATH 2030.03 and either MATH 1010.03 or STAT 2060.03
STAT 4350Applied Multivariate Analysis
The class deals with the stochastic behaviour of several variables in systems where their interdependence is the object of analysis. Greater emphasis is placed on practical application than on mathematical refinement. Topics include classification, cluster analysis, categorized data, analysis of interdependence, structural simplification by transformation or modelling and hypothesis construction and testing.
Prerequisites: STAT 3340.03 and MATH 2135.03 or 2040.03
STAT 4620Data Analysis
A variety of statistical models which are useful for the analysis of real data are discussed. Topics may include: generalized linear models, such as logistic regression and Poisson regression, models for multidimensional contingency tables, ordered categories and survival data.
Prerequisites: STAT 3340.03, 3460.03, or instructor's consent.