ONE. Review of Ordinary Linear Regression and Its Assumptions

  1. Mari Palta

Published Online: 11 AUG 2003

DOI: 10.1002/0471467979.ch1

Quantitative Methods in Population Health: Extensions of Ordinary Regression

Quantitative Methods in Population Health: Extensions of Ordinary Regression

How to Cite

Palta, M. (2003) Review of Ordinary Linear Regression and Its Assumptions, in Quantitative Methods in Population Health: Extensions of Ordinary Regression, John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/0471467979.ch1

Author Information

  1. Madison, Wisconsin, USA

Publication History

  1. Published Online: 11 AUG 2003
  2. Published Print: 15 AUG 2003

Book Series:

  1. Wiley Series in Probability and Statistics

Book Series Editors:

  1. Walter A. Shewhart and
  2. Samuel S. Wilks

ISBN Information

Print ISBN: 9780471455059

Online ISBN: 9780471467977

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Keywords:

  • linearity;
  • bias;
  • efficiency;
  • least squares;
  • equal variance;
  • independence;
  • causality;
  • confounding;
  • endogeneity;
  • PROC REG

Summary

We review ordinary regression analysis, its notation and assumptions. Show how each assumption enters in the analysis. Comment on related issues of bias, efficiency, causal interpretation. A brief calculus based derivation of the least squares estimators of slope and intercept. Includes SAS commands for running ordinary regression analysis and producing a scatter plot. Examples regress systolic blood pressure on age and glycosylated hemoglobin on diabetes duration. Number of days very low birth weight neonates spend in the NICU is regressed on birth weight to illustrate distribution of residuals.