* Offered in 2007-2010 (others not offered by Prof. Spence during his tenure as Director of the Cognitive Science Program)

* PSY 201F Statistics I
(to access this link you must have a UTORid and also be registered in the course)
An introduction to the uses of statistics in Psychology. Descriptive statistics, exploratory data analysis, estimation, hypothesis testing, and an elementary introduction to experimental design are covered.

PSY 201F Statistics II
An introduction to analyzing expeimental data using a computer. Students use SPSS to perform descriptive and exploratory data analyses, regression, and analysis of variance. The classical designs are covered: randomized, blocking, factorial, repeated measures, and incomplete designs.

PSY 299Y Research Opportunities Program
This provides an opportunity for students in their second year to work on a research project. Students learn research methods and share in the excitement of discovering new knowledge while earning course credit toward their degree.

PSY 305F Treatment of Psychological Data
Students with a previous full-year course in statistics learn how to use SAS, a statistical analysis system, to perform descriptive and exploratory data analyses, as well as regression and analysis of variance. We also treat the analysis of counted data and the creation and interpretation of statistical graphs.

PSY 378F Engineering Psychology
The focus is on the principles that underlie the design of human-machine interfaces. Examples are drawn from aviation, display design, and human-computer interaction. Students work in small teams and are required to complete a major project in aviation safety.

PSY 2001F Design of Experiments I
After a review of basic concepts in probability and statistics, we treat the classic designs and the standard algebraic methods of analysis for orthogonal layouts. We emphasize design and interpretation rather than the mathematical or computational aspects. The SAS sytem is used for all statistical analysis.

PSY 2002F Design of Experiments II
The general linear model (GLM) approach to regression and ANOVA/ANCOVA/MANOVA is introduced and covered in detail. We discuss the classical designs as well as incomplete and non-orthogonal designs. Computational issues receive exended treatment.

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