## SCS CoursesIntroduction to SPSS for WindowsData Analysis Using SAS Assumption Violation in ANOVA Models: Problems and Solutions An Introduction to R |

Instructor: | Ernest Kwan, MA with Sophia Lee |

Dates: | Thursdays: February 7, 21, 28, March 7, 2002 |

Time: | 9:00 a.m. - 12:00 noon |

Location: | Room T107 (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 with Alina Rivilis |

Dates: | SPSS Tuesdays: February 5, 19, 26, March 5, 2002 |

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

Location: | Room T107 (PC lab) Steacie Science Library |

Enrolment Limit: | 25 |

- 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.

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 Robert Cribbie |

Dates: | Thursdays, February 28, March 7, 14, & 21, 2002 |

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

Location: | TBA |

Enrolment Limit: | 25 |

- The purpose of this course is to present an overview
of the importance of examining the validity assumptions of the parametric
F test in analysis of variance designs. Although researchers are often
aware of the assumptions of the F test (e.g., normality, variance homogeneity),
less often are they aware of the frequency with which social science data
violate these assumptions, the problems that arise from using the F test
when assumptions are violated, and the solutions that are available for
dealing with assumption violation. The course will cover both one-way and
factorial univariate designs.

Instructor: | Professor John Fox |

Dates: | Wednesdays, March 6, 13, 20, and 27, 2002 |

Time: | 2:30 pm to 4:30 pm |

Location: | TBA |

Enrolment Limit: | 25 |

- The statistical programming language and computing
environment S has become the de-facto standard among statisticians. S has
great built-in statistical functionality, including excellent facilities
for drawing statistical graphs and for fitting linear, generalized linear,
and nonlinear models. A wide range of "libraries" extends the capabilities
of S to such areas as mixed models, survival analysis, and nonparametric
regression. New statistical methods are often first made available in S.
- Getting started with R; reading, manipulating, examining, and transforming data
- Fitting linear and generalized linear models in R
- Regression diagnostics with car (the Companion to Applied Regression library)
- Writing programs and drawing graphs in R

The S language has two major implementations: the commercial product S-PLUS, and the free, open-source R (also called "GNU S"). R, which is the focus of this short course, runs on Unix and Linux systems, Windows PCs, and Macintoshes. Although R is free, it is very high-quality software, to which many of the leading experts on statistical computing have contributed.

A statistical package, such as SPSS, makes
routine data analysis relatively easy, but it is relatively difficult to
do things that are

innovative or nonstandard, or to add to the built-in capabilities of
the package. In contrast, a good statistical computing environment also
makes routine data analysis easy, but additionally supports convenient
programming; this means that users can extend the already impressive facilities
of R.

The purpose of this short course is to show you how to do data analysis in R, including writing programs and constructing non-standard graphs. I assume that you are familiar with the statistical content of the course.

The following topics will be covered:

The text for the short course is J. Fox,
An R and S-PLUS Companion to Applied Regression, Sage (in press).

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