Chapter ONE. Review of Ordinary Linear Regression and Its Assumptions
Published Online: 11 AUG 2003
DOI: 10.1002/0471467979.ch1
Copyright © 2003 John Wiley & Sons, Inc.
Book Title
Quantitative Methods in Population Health: Extensions of Ordinary Regression
Additional Information
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
Publication History
- Published Online: 11 AUG 2003
- Published Print: 15 AUG 2003
Book Series:
Book Series Editors:
- Walter A. Shewhart,
- Samuel S. Wilks
ISBN Information
Print ISBN: 9780471455059
Online ISBN: 9780471467977
- Summary
- Chapter
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.