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Correlation and Regression

In: A Primer of Permutation Statistical Methods

Author

Listed:
  • Kenneth J. Berry

    (Colorado State University, Department of Sociology)

  • Janis E. Johnston

    (Alexandria)

  • Paul W. Mielke Jr.

    (Colorado State University, Department of Statistics)

Abstract

This chapter introduces permutation methods for measures of correlation and regression, the best-known of which is Pearson’s product-moment correlation coefficient. Included in this chapter are six example analyses illustrating computation of exact permutation probability values for correlation and regression, calculation of measures of effect size for measures of correlation and regression, the effects of extreme values on conventional (ordinary least squares) and permutation (least absolute deviation) correlation and regression, exact and Monte Carlo permutation procedures for measures of correlation and regression, application of permutation methods to correlation and regression with rank-score data, and analysis of multiple correlation and regression. Included in this chapter are permutation versions of ordinary least squares correlation and regression, least absolute deviation correlation and regression, Spearman’s rank-order correlation coefficient, Kendall’s rank-order correlation coefficient, Spearman’s footrule measure of correlation, and a permutation-based alternative for the conventional measures of effect size for correlation and regression: Pearson’s r 2.

Suggested Citation

  • Kenneth J. Berry & Janis E. Johnston & Paul W. Mielke Jr., 2019. "Correlation and Regression," Springer Books, in: A Primer of Permutation Statistical Methods, chapter 0, pages 361-407, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-20933-9_10
    DOI: 10.1007/978-3-030-20933-9_10
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