Applied Statistics, Spring 2003

Abstract:: Introduces statistical data analysis, concentrating on techniques used in management science and finance. Topics chosen from: statistical graphics, basics of sampling, estimation, hypothesis testing, linear and logistic regression, analysis of variance, contingency tables, forecasting, statistical quality control, principal components, and factor analysis. SAS or similar package used for data analysis. This course is an introduction to applied statistics and data analysis. Topics include collecting and exploring data, basic inference, simple and multiple linear regression, analysis of variance, nonparametric methods, and statistical computing. It is not a course in mathematical statistics, but provides a balance between statistical theory and application. Prerequisites are calculus, probability, and linear algebra. We would like to acknowledge the contributions that Prof. Roy Welsch (MIT), Prof. Gordon Kaufman (MIT), Prof. Jacqueline Telford (Johns Hopkins University), and Prof. Ramón León (University of Tennessee) have made to the course material.

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A Handbook for Collecting Releve Data in Minnesota

Abstract:: The exercise describes releve field methods and discusses the multivariate analysis of releve data. The application of releve data to problems of habitat evaluation, forest inventory, and research projects in Minnesota was presented by experts from the Minnesota Natural Heritage Program, the Minnesota County Biological Survey, and the Univ. of Minnesota. All participants received a releve handbook that covered the history of the method, field instructions, data-entry forms, and an overview of a menu-driven computer system SAS Version 5 used to manage a releve database.