JSM 2005 - Toronto

Abstract #303475

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

To View the Program:
You may choose to view all activities of the program or just parts of it at any one time. All activities are arranged by date and time.



The views expressed here are those of the individual authors
and not necessarily those of the ASA or its board, officers, or staff.

The Program has labeled the meeting rooms with "letters" preceding the name of the room, designating in which facility the room is located:

Minneapolis Convention Center = “MCC” Hilton Minneapolis Hotel = “H” Hyatt Regency Minneapolis = “HY”

Back to main JSM 2005 Program page
Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 74
Type: Contributed
Date/Time: Sunday, August 7, 2005 : 8:00 PM to 9:50 PM
Sponsor: Section on Statistical Education
Abstract - #303475
Title: Understanding the One-way, Random Effect ANOVA
Author(s): Eric Suess*+ and Bruce E. Trumbo and Antonio P. Curtis and Yun Jiang
Companies: California State University, East Bay (Hayward Hills Campus) and California State University, East Bay (Hayward Hills Campus) and California State University, Hayward and California State University, Eastbay
Address: Department of Statistics, Hayward, CA, 94542, United States
Keywords: Analysis of variance ; random effects model ; estimation of variance components ; teaching ; Bayesian estimation ; Gibbs sampler
Abstract:

The one-way, random-effect ANOVA model is presented and several classical datasets are analyzed and discussed from three points of view: the standard ANOVA table, F-test, and method-of-moments estimates of variance components, which can lead to negative estimates; maximum likelihood estimates of variance components; and Bayesian probability intervals for variance components based on flat priors and computed using a Gibbs sampler. All computations are done in S. Level and methods are appropriate for presentation to advanced undergraduate and first-year MS students.

  • The address information is for the authors that have a + after their name.
  • Authors who are presenting talks have a * after their name.

Back to the full JSM 2005 program

JSM 2005 For information, contact jsm@amstat.org or phone (888) 231-3473. If you have questions about the Continuing Education program, please contact the Education Department.
Revised March 2005