Case Study in Least Squares Fitting and Interpretation of a Linear Model

  • Frank E. HarrellJr.
Part of the Springer Series in Statistics book series (SSS)

Abstract

This chapter presents some of the stages of modeling, using a linear multiple regression model whose coefficients are estimated using ordinary least squares. The data are taken from the 1994 version of the City and County Databook compiled by the Geospatial and Statistical Data Center of the University of Virginia Library and available at fisher.lib.virginia.edu/ccdb. Most of the variables come from the U.S. Censusa. Variables related to the 1992 U.S. presidential election were originally provided and copyrighted by the Elections Research Center and are taken from [365], with permission from the Copyright Clearance Center. The data extract analyzed here is available from this text’s Web site (see Appendix). The data did not contain election results from the 25 counties of Alaska. In addition, two other counties had zero voters in 1992. For these the percent voting for each of the candidates was also set to missing. The 27 counties with missing percent votes were excluded when fitting the multivariable model.

Keywords

Voter Turnout Total Hospital Cost Median Family Income Influential Observation Design Library 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • Frank E. HarrellJr.
    • 1
  1. 1.Department of BiostatisticsVanderbilt University School of MedicineNashvilleUSA

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