Instructor: | Ernest Kwan, MA |
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Dates: | Thursdays: February 6, 13, 27, March 6, 2003 |

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

Location: | Room T128 (PC lab) Steacie Science Library |

Enrolment Limit: | 25 |

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: | Mirka Ondrack, MSc |
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Dates: | Wednesdays: February 5, 12, 26, March 5, 2003 |

Time: | 10:00 a.m. - 1:30 p.m. |

Location: |
Room CS139 Scott Library (Central Square) on February 5, 12, and March 5,
2003 Room T128 Steacie Science Library on February 26 |

Enrolment Limit: | 20 |

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.

Instructor: | Professor Robert Cribbie |
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Dates: | Wednesdays, January 22, 29, February 5 and 12, 2003 |

Time: | 1:30 - 4:00 p.m. |

Location: | BSB 061-- 1:30-3:00, BSB 159 (Hebb Computer Lab)-- 3:00-4:00 |

Enrolment Limit: 20 | 30 |

This course will provide a general introduction to the methods of structural equation modeling (SEM), including a discussion of developing models, evaluating the fit of models to data, evaluating the significance of model parameters and performing model modification. The primary objectives of this class will be to provide:

- the ability to recognize situations where these techniques may be useful in research;
- an appreciation for the roles of sound theory in making these techniques useful;
- an understanding of the limitations of these methods; and
- the ability to use available software for analyzing data.

Instructor: | Lu Wang (MSc, PhD candidate in Geography) |
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Dates: | Tuesday, February 4, 11, 25 and March 4, 2003 |

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

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

Enrolment Limit: | 25 |

Geographic Information System (GIS) is a computer tool for visualizing, processing and analyzing data with a spatial dimension such as population distribution and facility location. This course provides hands-on experience with ArcView 3.2 and participants will learn how to construct, plot and edit maps, perform queries and basic spatial analyses. Examples will be drawn from Canadian census and other social science data. The class will be convened in the GIS computer lab.The four three-hour sessions in this short course will cover:

- introducing basic GIS concepts and getting started with ArcView
- mapping census data
- working with attribute table and spatial queries
- geocoding street addresses

Instructor: | Professor Jeff Gill, University of Florida |
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Dates: | January 17, 2003 |

Time: | 9:00 - 12:00 noon, 1:00 - 4:00 p.m. |

Location: | Room 3009, Vari Hall |

Enrolment Limit: | 40 |

Historically, a major historical problem with the Bayesian approach is that sometimes realistic models lead to posterior calculations that are difficult or impossible to perform analytically. Suppose that instead of performing difficult analytical calculations, one could produce a large number of simulations from the posterior and describe statistics of interest empirically. This is what Markov chain Monte Carlo methods provide for the applied Bayesian researcher. This seminar will review MCMC from the ground up: basics and theory through applications and diagnostics. Contents:SCS home pageCourse notes may be found at:

- Summary of Bayesian Inference
- Troublesome Posteriors and Other Annoyances
- Markov Chain Theory
- Metropolis-Hastings and Related Algorithms
- The Gibbs Sampler
- Applications with Real Data
- Using WinBUGS and R for MCMC
- Convergence and Additional Worries
- Advanced Tools: Simulated Annealing, Coupling From the Past

http://www.clas.ufl.edu/~jgill/MCMC.Talk/