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One-Way Analysis of Variance (ANOVA)

  • Thomas J. Quirk
Chapter
Part of the Excel for Statistics book series (EXCELSTAT)

Abstract

So far in this book, you have learned how to test for the difference within one group of data between the mean of the group and the hypothesized population mean for the data using either the 95 % confidence interval about the mean (Chap.  3 of this book) or the one-group t-test of the mean (Chap.  4 of this book). You have also learned how to test for the difference between the means for two groups of to determine if this difference was a “significant” difference (Chap.  5 of this book). In this chapter, you will learn how to test for the difference between groups on a single variable when you have three or more groups of data. A practical engineering problem is presented that walks you through the Excel steps needed to generate the output for a one-way ANOVA test. You will learn how to interpret the Summary Output table correctly, and how to test the hypotheses comparing the population means of the three or more groups to see if they are “significantly different from each other.” If this overall ANOVA test is produces a significant result, you will learn how to test the hypotheses comparing any two groups using an ANOVA t-test formula. This formula is presented, explained, and a practical engineering example is used delineating the five steps needed to perform this test using a calculator. Then, the Excel steps for using this formula are presented and explained. Three practice problems are given at the end of the chapter to test your Excel skills, and the answers to these problems appear in Appendix A of this book. An additional practice problem is presented in the Practice Test given in Appendix B along with its answer in Appendix C of this book.

Keywords

Null Hypothesis Decision Rule Fuel Injector Research Hypothesis Decimal Place 
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.

References

  1. Black K. Business statistics: for contemporary decision making. 6th ed. Hoboken: John Wiley & Sons, Inc.; 2010.Google Scholar
  2. Gould J, Gould G. Biostats basics: a student handbook. New York: W.H. Freeman and Company; 2002.Google Scholar
  3. Hibbert D, Gooding J. Data analysis for chemistry: an introductory guide for students and laboratory scientists. New York: Oxford University Press; 2006.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Thomas J. Quirk
    • 1
  1. 1.Webster UniversitySt. LouisUSA

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