Chapter 3. Designs for Multiple Linear Regression Analysis

  1. Martijn P. F. Berger1 and
  2. Weng Kee Wong2

Published Online: 27 MAY 2009

DOI: 10.1002/9780470746912.ch3

An Introduction to Optimal Designs for Social and Biomedical Research

An Introduction to Optimal Designs for Social and Biomedical Research

How to Cite

Berger, M. P. F. and Wong, W. K. (2009) Designs for Multiple Linear Regression Analysis, in An Introduction to Optimal Designs for Social and Biomedical Research, John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470746912.ch3

Author Information

  1. 1

    Department of Methodology and Statistics, Maastricht University, The Netherlands

  2. 2

    Department of Biostatistics, School of Public Health, University of California, Los Angeles, USA

Publication History

  1. Published Online: 27 MAY 2009
  2. Published Print: 29 MAY 2009

ISBN Information

Print ISBN: 9780470694503

Online ISBN: 9780470746912

SEARCH

Keywords:

  • designs for multiple linear regression analysis;
  • Bock, reporting Poggendorff and Ponzo illusion experiments;
  • A-optimal designs - efficient in terms of D- and E- criteria;
  • polynomial models are used after an appropriate transformation;
  • D-optimal design points - for polynomial regression models with independent errors;
  • polynomial models - special class of multiple regression models;
  • relative efficiency (RE), comparing criterion value of the design with Ds-optimal design

Summary

This chapter contains sections titled:

  • Design problem for multiple linear regression

  • Designs for vocabulary-growth study

  • Relative efficiency and sample size

  • Simultaneous inference

  • Optimality criteria for a subset of parameters

  • Relative efficiency

  • Designs for polynomial regression model

  • The Poggendorff and Ponzo illusion study

  • Uncertainty about best fitting regression models

  • Matrix notation of designs for multiple regression models

  • Summary