Instructor: | Ernest Kwan |

Dates: | TUESDAYS, May 6, 13, 20, 27, 2003 |

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

Location: | Room CS130 Scott Library |

Enrolment Limit: | 19 |

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 |

Dates: | FRIDAYS, May 2, 9, 16, 23, 2003 |

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

Location: | Room CS130 Scott Library |

Enrolment Limit: | 19 |

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.

Instructor: | Professor Michael Friendly |

Dates: | WEDNESDAYS, May 7, 14, 21, 2003 |

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

Location: | Room S203 South Ross Building |

Enrolment Limit: | 30 |

Statistical methods for categorical data, such as log-linear models and logistic regression, represent discrete analogs of the analysis of variance and regression methods for continuous response variables.

While graphical display techniques are common adjuncts to analysis of variance and regression, methods for plotting contingency table data are not as widely used. Moreover, while statistical methods can be used to determine which variables are related, the numerical summaries and parameter estimates do not provide easy ways to show how those variables are related.

This workshop provides a brief introduction to statistical methods for analysing discrete data and frequency data, together with some of the graphical methods which are useful for understanding the pattern of association among categorical variables. These methods can be helpful for both data exploration and for communicating results to others. Some of the methods described include:

- methods for discrete frequency distributions,
- association plots for two-way tables,
- mosaic displays,
- effects plots for log-linear models and logistic regression, and
- correspondence analysis.
- models for repeated measures

This course is an updated version of Graphical Methods for Categorical Data, with somewhat greater introductory material on logistic regression and loglinear models. These techniques are all described and illustrated in my book, Visualizing Categorical Data, which will be available in the York Bookstore. Another useful reference is Categorical Data Analysis Using the SAS(R) System by Stokes, Davis, and Koch (SAS Institute, 1995, ISBN 1-55544-219-6) which will also be available in the York bookstore.

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