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Twoway Analysis of Variance (ANOVA)

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 Twoway Analysis of Variance (ANOVA) is to provide guidance on how R can be used to see if two or more group means (e.g., Mean of High-Density Lipoprotein (HDL) Cholesterol for Gender breakouts (e.g., Female and Male) and Race-Ethnicity breakouts (e.g., Asian, Black, Hispanic, Indigenous, Unidentified, and White); Mean Degree of Marbling for Breed breakouts (e.g., Hereford, Limousin, and Simmental) and Feeding Program Breakouts (e.g., Pasture with Supplemental Grain and Pasture Only); Mean of Soybean Yield for Tillage Program (e.g., No-Till and Conventional) and Seasonal Rainfall (e.g., Dry, Normal, Wet), etc.) differ due to chance, or if observed differences are indeed the result of true difference and/or interactions between phenomena. Specifically, the Twoway ANOVA statistical test has been structured to examine differences and possible interactions when multiple variables have two or more separate categories. The degree of complexity supported by Twoway ANOVA begins to model real-world concerns for the many ways variables, and possible interaction between and among variables, impact outcomes.

Suggested Citation

  • Thomas W. MacFarland & Jan M. Yates, 2021. "Twoway Analysis of Variance (ANOVA)," Springer Books, in: Using R for Biostatistics, chapter 0, pages 361-426, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-62404-0_6
    DOI: 10.1007/978-3-030-62404-0_6
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