Regression and Correlation

  • Jay L. Devore
  • Kenneth N. Berk
Part of the Springer Texts in Statistics book series (STS)


The general objective of a regression analysis is to determine the relationship between two (or more) variables so that we can gain information about one of them through knowing values of the other(s). Much of mathematics is devoted to studying variables that are deterministically related. Saying that x and y are related in this manner means that once we are told the value of x, the value of y is completely specified.


  1. Chatterjee, Samprit, Ali Hadi, and Bertram Price, Regression Analysis by Example (4th ed.), Wiley, New York, 2006. A brief but informative discussion of selected topics.CrossRefGoogle Scholar
  2. Daniel, Cuthbert, and Fred Wood, Fitting Equations to Data (2nd ed.), Wiley, New York, 1980. Contains many insights and methods that evolved from the authors’ extensive consulting experience.Google Scholar
  3. Draper, Norman, and Harry Smith, Applied Regression Analysis (3rd ed.), Wiley, New York, 1998. A comprehensive and authoritative book on regression.CrossRefGoogle Scholar
  4. Hoaglin, David, and Roy Welsch, “The Hat Matrix in Regression and ANOVA,” American Statistician, 1978: 17–23. Describes methods for detecting influential observations in a regression data set.zbMATHGoogle Scholar
  5. Kutner, Michael, Christopher Nachtsheim, John Neter, and William Li, Applied Linear Statistical Models (5th ed.), McGraw-Hill, New York, 2005. The first 14 chapters constitute an extremely readable and informative survey of regression analysis.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Statistics DepartmentCalifornia Polytechnic State UniversitySan Luis ObispoUSA
  2. 2.Department of MathematicsIllinois State UniversityNormalUSA

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