
SCS CoursesData Analysis Using SASIntroduction to SPSS for Windows Exploratory and Confirmatory Factor Analysis An Introduction to R for Regression, Linear Models, and Generalized Linear Models 
Instructor:  Ernest Kwan, MA 
Dates:  Thursdays, February 8, 15, 22, and March 1, 2001 
Time:  8:30 am  11:30 am 
Location:  Room T107 (PC lab) Steacie Science Library 
Enrolment Limit:  25 
Further topics: Sessions Three and Four will concentrate on SAS programming techniques to modify data and enhance SAS output. More statistical procedures will be introduced for general linear models.
Instructor:  Mirka Ondrack, MA 
Dates:  Wednesdays, February 7, 14, 21 and 28, 2001 
Time:  10 am to 1:30 pm 
Location:  Room T107 Steacie Science Library 
Enrolment Limit:  25 
Session One will introduce the computing concepts of SPSS, the different facilities for reading data into an SPSS spreadsheet, and saving SPSS data files for future use. At the end of the first session, participants should be able to run simple programs, including some statistical procedures.
Sessions Two and Three will cover basic data modifications, transformations and other functions available in SPSS. More statistical procedures will be introduced, with an emphasis on the use of graphical methods to examine univariate and bivariate relationships.
Session Four will cover Analysis of Variance and Least Squares Regression. Graphical techniques will be demonstrated.
The following zip file: spss.zip contains SPSS data sets and scripts used in the course. Copy this file (in binary mode) to an appropriate directory and use a zip extractor (such as pkunzip or WinZip) to expand the zip archive into the original files.
Instructor:  Professor Michael Friendly 
Dates:  Wednesday, March 7, 14, 21 and 28, 2001. 
Time:  2:30pm to 4:30pm 
Location:  Schulich School of Business Room 224 
Enrolment Limit:  25 
Instructor:  Professor John Fox 
Dates:  Friday, March 16, 23 and 30, 2001 
Time:  2:30pm to 4:30pm 
Location:  TBA 
Enrolment Limit:  25 
The purpose of this short course is to introduce R, and to illustrate its use for regression analysis, linear models, and generalized linear models (such as logistic regression), with a focus on regression diagnostics. I will cover both builtin capabilities in R in these areas and some of my own extensions (programmed in collaboration with Georges Monette).
Attendees of the short course should be at least somewhat familiar with regression analysis, linear models, and generalized linear models. Previous exposure to the S language is an asset, but is not essential.
I encourage participants to download and install R on their own computers. R, and information about it, are available on the web at http://cran.rproject.org/ and http://www.Rproject.org/.