LISA Statistics Short Course: Designing Experiments and Collecting Useful Data
LISA SHORT COURSES IN STATISTICS
LISA (Virginia Tech's Laboratory for Interdisciplinary Statistical Analysis) is providing a series of evening short courses to help graduate students use statistics in their research. The focus of these two-hour courses is on teaching practical statistical techniques for analyzing or collecting data. See www.lisa.stat.vt.edu/?q=short_courses for instructions on how to REGISTER and to learn more.
Spring 2013 Schedule:
January 29: Designing Experiments and Collecting Useful Data*;
February 5: A Tutorial in T-Tests and ANOVA in JMP*;
February 12: Regression Analysis Using JMP*;
February 18: Introduction to R, Part I;
February 19: Introduction to R, Part I;
February 25: Introduction to R, Part II;
February 26: Introduction to R, Part III;
March 5: How to make plots to tell the story of your data;
March 18: Data Mining Basics;
March 19: Data Mining Basics;
March 26: Classification and Regression Trees (CART);
*These courses will be held in the GLC Room G, all other courses are in 3060 Torgersen Hall.
Tuesday, February 12; 4:00-6:00 pm;
Instructor: Jonathan Stallings;
Title: Designing Experiments and Collecting Useful Data;
Across all disciplines, the ability to test theories by experimentation is vital for validation and discovery. When designing an experiment, the researcher hopes to maximize the obtained information by reducing wasted resources and allocating treatments in such a way as to minimize variances. Ideally, a design will account for major sources of variation so that the researcher can be confident the effects of treatments are not confounded with extraneous factors. In this course, the basic principles of experimental design will be given and specific designs discussed. The first designs introduced will be completely randomized designs, the most straightforward design when a researcher wants to test for differences among multiple treatments. Optimal blocking strategies will then be presented as a variance-reducing technique, e.g. perhaps the researcher feels a subject's gender may significantly affect observations. For each design we will discuss implementation, appropriate analysis and provide examples in SAS. If time permits we may also introduce more complicated designs tailored specifically to the researchers attending the course.
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