Instructor: | Gigi Luk, MA |
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Dates: | Thursdays: February 3, 10, 24, and March 3, 2005 |

Time: | 9:30 a.m. - 12:30 p.m. |

Location: | Room 021 (Steacie Instructional Lab) Steacie Science Library |

Enrolment Limit: | 35 |

Sessions One and Two provide an overview of SAS and its underlying logic; an explanation of the use of the Display Manager System to run a SAS job; an introduction to the SAS Data step for reading, transforming, and storing data; and a demonstration of how statistical analyses may be performed in SAS Insight.

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: | Lisa Fiksenbaum, MSc |
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Dates: | Wednesdays: February 2, 9, 23, and March 2, 2005 |

Time: | 9:00 a.m. - 12:30 p.m. |

Location: | Room 021 (Steacie Instructional Lab) Steacie Science Library |

Enrolment Limit: | 35 |

This course presents the basics of the Statistical Package for the Social Sciences (SPSS). 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 including the uses of SPSS system files. More statistical procedures will also be introduced, with an emphasis on the use of graphical methods for examining univariate and bivariate relationships. Session Four will cover Analysis of Variance and Least Squares Regression. As with previous sessions, graphical techniques will be demonstrated. For maximum benefit, participants should have an understanding of basic statistics, up to the level of general linear models.

Instructor: | Professor Georges Monette |
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Dates: | March 2, 9, 16, 23, 30 and April 6, 2005 (tentative) |

Time: | Wednesdays, 9:30 to 11:30 a.m. |

Location: | 5022 TEL Building |

Enrolment Limit: | 40 |

This course uses classical repeated measure, univariate and multivariate, as a point of departure for studying methods for the analysis of longitudinal and hierarchical data using mixed models. Mixed models allow the analysis of repeated measures data when the data are ‘unbalanced’ and classical models do not work, e.g. subjects are observed at different times or time-varying covariates are included in the model. The ability to analyze a wider range of data comes at a price. Not only do data analysts need to learn new techniques, they also need to become aware of concepts that are not as salient in the analysis of "balanced" data. The course will emphasize the visualization of the basic concepts to help participants develop a strong understanding of the strengths and limitations of these methods. The proposed list of topics includes:Participants should have some familiarity with multiple regression.

- Classical univariate and multivariate repeated measures models, extensions to mixed models.
- The structure of the linear mixed model: fixed effects, random effects, variance and covariance components.
- How mixed models are used to fit longitudinal data. Statistical control with observational data.
- Borrowing strength, shrinkage and bias in random effects models.
- Contextual versus compositional effects.
- Model building and diagnostics.
- Consequences of measurement error and approaches to adjustment.
- Modelling correlation.
- Missing data patterns.
- Modelling panel attrition.
- Logistic regression for binary responses.
- Non-linear models for binary and categorical responses.

Instructor: | Professor John Fox |
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Dates: | FRIDAYS-- January 28, February 4, 11, March 4, 11, 18, and April 1, 2005 |

Time: | 3:00 - 5:00 p.m. |

Location: | 305 York Lanes (January 28); 159 BSB (February 4, 11, March 4, 11, 18, and April 1) |

Enrolment Limit: | 25 |

The statistical programming language and computing environment S has become the de-facto standard among statisticians. The S language has two major implementations: the commercial product S-PLUS, and the free, open-source R. Both are available for Windows and Unix/Linux systems; R, in addition, runs on Macintoshes. Although I will briefly introduce S-PLUS, the major emphasis will be on R. (A slightly longer abstract is available as a Word document.)While some statistical packages make it difficult to undertake analyses that are non-standard or to add to the built-in capabilities of the package, S supports innovative programming; in this regard, statisticians have contributed literally dozens of freely available statistical "libraries" of R and S-PLUS programs. S is also particularly capable in the area of statistical graphics.

The purpose of this short course is to show participants how to accomplish a variety of tasks in S, including the tasks of writing programs and constructing non-standard graphs. The statistical content is assumed known or taught in other courses.

The seven sessions in this short course will cover (with chapter references to Fox, 2002): There is also a web site for the course: http://socserv.socsci.mcmaster.ca/jfox/Courses/S-course/.

It is recommended that you buy one of:

- 1. Getting started with R and S-PLUS (Ch. 1)
- 2. Reading and manipulating data (Ch. 2 & 3)
- 3. Linear regression and linear models in S (Ch. 4)
- 4-5. An introduction to programming in S (Ch. 8)
- 6. S graphics (Ch. 7)
- 7. Generalized linear models in S (Ch. 5)

- J. Fox,
An R and S-PLUS Companion to Applied Regression.Sage, 2002. Available in the York Bookstore, filed under code YDPSCH.0000-03. Additional materials are available at http://socserv.socsci.mcmaster.ca/jfox/Books/Companion/index.html.- W. N. Venables and B. D. Ripley,
Modern Applied Statistics with S-PLUS, Third Edition. New York: Springer, 1999.

Instructor: | Yong Ge (PhD, Postdoctoral Fellow) |
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Dates: | THURSDAYS, Feb 24, Mar 3, 10, and Mar 17, 2005 |

Time: | 9:00 am - 12:00 noon |

Location: | Room N302 (GIS lab), Ross Building |

Enrolment Limit: | 25 |

Geographic Information System (GIS) is a method to visualize, manipulate, analyze, and display spatial data. For example, it can combine layers of information about a place to give you a better understanding of that place, find the best location for new store or the shortcut/best route to the destination, analyzing environmental damage, and so on.The primary objective of this short course is to introduce the technology of spatial data acquisition, presentation, spatial analysis, spatial query and its applications. Hands-on labs exercises with ArcView 3.2 are offered. The class will be convened in the GIS computer lab.

The four three-hour sessions in this short course will cover:

- Introduction to concepts of a GIS and application examples with ArcView
- Spatial Data Collection and Management
- Spatial Query and application examples with ArcView
- Spatial Analysis and application examples with ArcView

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