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Correlation, Association, Regression, Likelihood, and Prediction

In: Using R for Biostatistics

Author

Listed:
  • Thomas W. MacFarland

    (Nova Southeastern University Fort Lauderdale, Senior Research Associate, Office of Institutional Effectiveness
    Nova Southeastern University Fort Lauderdale, Associate Professor, College of Computing and Engineering)

  • Jan M. Yates

    (Nova Southeastern University Fort Lauderdale, Professor Emerita, Abraham S. Fischler College of Education)

Abstract

The purpose of this lesson on correlation, association, regression, likelihood, and prediction is to provide guidance on how R can be used to first determine the association between two variables and to then use this degree of association, if any, to predict future outcomes. Past behavior is the best predictor of future behavior. This concept applies in the biological sciences, physical sciences, social sciences, and also in economics. Often, by knowing past relationships between and among variables it is then possible to build a prediction equation to make a reasonable estimate of future values for selected variables. This lesson will focus on Pearson’s Product Moment Coefficient of Correlation (Pearson’s r, perhaps the most common test for determining if there is an association between phenomena that display normal distribution), Spearman’s Rank Correlation Coefficient (Spearman’s rho, perhaps the most common test for determining if there is an association between phenomena that do not display normal distribution), different types of regression analyses for prediction, as well as concepts such as probability, likelihood, odds, and odds ratio.

Suggested Citation

  • Thomas W. MacFarland & Jan M. Yates, 2021. "Correlation, Association, Regression, Likelihood, and Prediction," Springer Books, in: Using R for Biostatistics, chapter 0, pages 427-584, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-62404-0_7
    DOI: 10.1007/978-3-030-62404-0_7
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