Instructor: | Ernest Kwan |
Dates: | Tuesdays, October 3, 10, 17, 24, 2000 |
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, October 4, 11, 18, and 25, 2000 |
Time: | 12 noon - 3:30 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: f99spss.zip (last updated October 1, 1999) 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 Georges Monette |
Dates: | Mondays, October 30, November 6, 13, 20, 2000 |
Time: |
Lectures: 9 am - 12 noon Lab: 1 pm - 3 pm |
Location: |
Lectures: 224 Schulich School of Business Lab: Room 110 South Ross (Gauss Lab) |
Enrolment Limit: | 25 |
This course will present a brief review of linear models and an overview of the basic theory and applications of mixed models. The fitting of mixed models will be illustrated with a number of examples covering a range of applications. The NLME version 3.3 software of Douglas Bates and Jose Pinheiro will be used for its flexibility in visualizing data for mixed models. NLME is written in S-Plus. Every model fitted with NLME will be translated into PROC MIXED or PROC NLMIXED in SAS.
The course will consist of lectures supplemented with hands-on exercises in a computer lab to allow participants to gain experience in the use of these methods.
The following topics will be covered:
Hierarchical models: Introduction to hierarchical data structures. Why use hierarchical models? The structure of the linear mixed model: fixed effects, random effects, variance and covariance components. Some applications: one-way ANOVA, regression with means as outcomes, random coefficient model, intercept and slopes as outcomes. Key concepts: shrinkage, borrowing strength, avoiding bias, contextual versus compositional effects. Model building and diagnostics. Consequences of measurement error and how to adjust for it. Introduction to non-linear hierarchical models and hierarchical models for categorical responses.
Longitudinal models: The classical starting points: univariate and multivariate repeated measures. Why do we need more? How mixed models are used to fit longitudinal data. Modeling correlation. Model construction and diagnostics. Missing data patterns. Modeling panel attrition. Logistic regression for binary responses. Non-linear models for binary and categorical responses. Markov transition models.
Instructor: | Professor Georges Monette |
Dates: | Monday, October 2, 16, 23, 2000 |
Time: | 9 am to 12 noon |
Location: | Room 110 South Ross (Gauss Lab) |
Enrolment Limit: | 25 |
The purpose of this course is to learn how to use S in a Unix or in a Windows environment. The course will have both lecture and hands-on components. Participants will have access to an account to run S-Plus. The three sessions of the course will cover approximately the following material: