Skip to main content

Confidence Intervals and Hypothesis Testing

  • Chapter
  • First Online:
  • 11k Accesses

Part of the book series: Statistics and Computing ((SCO,volume 40))

Abstract

This chapter is a catalogue of R functions commonly used to get confidence intervals for usual parameters: mean, proportion, variance, median and correlation. We also present a catalogue of R functions to perform standard hypothesis testing. Furthermore, a few practical worksheets will help the reader understand how to interpret confidence intervals, as well as the various errors related to hypothesis testing.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Adler J. R in a Nutshell. O’Reilly, 2010.

    Google Scholar 

  2. Akaike H. Information Theory and an Extension of the Maximum Likelihood Principle. In Petrov B. N. and Csaki F., editors, Proc. of the 2nd Int. Symp. on Information Theory, pages 267–81, 1973.

    Google Scholar 

  3. Becker R. A., Chambers J. M. and Wilks A. R. The New S Language: A Programming Environment for Data Analysis and Graphics. Chapman & Hall, 1988.

    Google Scholar 

  4. Belsley D. A., Kuh E. and Welsch R. E. Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. Wiley Series in Probability and Mathematical Statistics. John Wiley & Sons, New York-Chichester-Brisbane, 1980.

    Google Scholar 

  5. Bilodeau M. and Lafaye de Micheaux P. A-dependence Statistics for Mutual and Serial Independence of Categorical Variables. Journal of Statistical Planning and Inference, 139:2407–19, 2009.

    Google Scholar 

  6. Braun J. W. and Murdoch D. J. A First Course in Statistical Programming with R. Cambridge University Press, 1st edition, January 2008.

    Google Scholar 

  7. Burns P. The R inferno. Unpublished manual, Amazon, 2011.

    Google Scholar 

  8. Chambers J.M. Software for Data Analysis: Programming with R. Statistics and Computing. Springer, June 2008.

    Google Scholar 

  9. Chivers I. and Sleightholme J. Introduction to Programming with Fortran: With Coverage of Fortran 90, 95, 2003, 2008 and 77. Springer, 2nd edition, 2012.

    Google Scholar 

  10. Cohen Y. and Cohen J. Statistics and Data with R: An Applied Approach Through Examples. Wiley, 2008.

    Google Scholar 

  11. Cook R.D. and Weisberg S. Residuals and Influence in Regression. Monographs on Statistics and Applied Probability. Chapman & Hall, London, 1982.

    MATH  Google Scholar 

  12. Crawley M.J.The R Book. Wiley, Chichester, June 2007.

    Google Scholar 

  13. Dalgaard P. Introductory Statistics with R (Statistics and Computing). Springer, 2nd edition, August 2008.

    Google Scholar 

  14. Davison A. C. and Hinkley D. V. Bootstrap Methods and their Applications, volume 1 of Cambridge Series in Statistical and Probabilistic Mathematics. Cambridge University Press, Cambridge, 1997. With 1 IBM-PC floppy disk (3.5 inch; HD).

    Google Scholar 

  15. Everitt B. S. and Hothorn T. A Handbook of Statistical Analyses Using R. Chapman & Hall/CRC, 1st edition, February 2006.

    Google Scholar 

  16. Fisher R. A. Statistical Methods for Research Workers, 4th edition §21.1. Oliver & Boyd, Edinburgh, 1932.

    Google Scholar 

  17. Fox J. Extending the R Commander by “plug in” Packages. R News, 7(3):46–52, 2007.

    Google Scholar 

  18. Furnival G. M. and Jr. Wilson R. W. Regression by Leaps and Bounds. Technometrics, 16:499–511, 1974.

    Google Scholar 

  19. Hand D.J. Branch and Bounds in Statistical Data Analysis. The Statistician, 30:1–13, 1981.

    Article  Google Scholar 

  20. Heiberger R. M. and Neuwirth E. R Through Excel. Use R! Springer, 2009.

    Google Scholar 

  21. Ihaka R. and Gentleman R. R: A Language for Data Analysis and Graphics. Journal of Computational and Graphical Statistics, 5(3):299–314, 1996.

    Google Scholar 

  22. Kernighan B. W. and Ritchie D. M. The C Programming Language, Second Edition. Prentice Hall, 1988.

    Google Scholar 

  23. Lafaye de Micheaux P. and Liquet B. ConvergenceConcepts: An R Package to Investigate Various Modes of Convergence. R Journal, 1(2):18–26, 2009.

    Google Scholar 

  24. Lafaye de Micheaux P. and Liquet B. Understanding Convergence Concepts: A Visual-minded and Graphical Simulation-based Approach. The American Statistician, 63(2):173–8, 2009.

    Google Scholar 

  25. Levene H. Robust Tests for Equality of Variances. In Contributions to probability and statistics, pages 278–92. Stanford Univ. Press, Stanford, Calif., 1960.

    Google Scholar 

  26. Maindonald J. and Braun J. Data Analysis and Graphics Using R: An Example-based Approach (Cambridge Series in Statistical and Probabilistic Mathematics). Cambridge University Press, December 2006.

    Google Scholar 

  27. Mallows C.L. Some Comments on c p . Technometrics, 15:661–75, 1973.

    MATH  Google Scholar 

  28. Matloff N. The Art of R Programming. No Starch Press, 2011.

    Google Scholar 

  29. Meyer C. Matrix Analysis and Applied Linear Algebra. Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA, 2000. With 1 CD-ROM (Windows, Macintosh and UNIX) and a solutions manual (iv+171 pp.).

    Google Scholar 

  30. Jr. Miller and Rupert G. Simultaneous Statistical Inference. Springer Series in Statistics. Springer-Verlag, New York, 2nd edition, 1981.

    Google Scholar 

  31. Montgomery D. C. Design and Analysis of Experiments. Wiley, 7th edition, 2008.

    Google Scholar 

  32. Muenchen R. A. R for SAS and SPSS Users. Springer Series in Statistics and Computing. Springer, 2009.

    Google Scholar 

  33. Muenchen R. A. and Hilbe J. M. R for Stata Users. Statistics and Computing. Springer, 2010.

    Google Scholar 

  34. Park S.K. and Miller K.W. Random Number Generators: Good Ones are Hard to Find. Commun. ACM, 31(10):1192–1201, 1988.

    Article  MathSciNet  Google Scholar 

  35. Robert C. and Casella G. Introducing Monte Carlo Methods with R (Use R!). Springer, 2009.

    Google Scholar 

  36. Sarkar D. Lattice: Multivariate Data Visualization with R. Use R! Springer, March 2008.

    Google Scholar 

  37. Schwarz G. Estimating the Dimension of a Model. Annals of statistics, 6:461–64, 1978.

    Article  MATH  MathSciNet  Google Scholar 

  38. Stroustrup B. The C++ Programming Language, Third edition. Addison-Wesley, 1997.

    Google Scholar 

  39. Teetor P. R Cookbook. O’Reilly, 2011.

    Google Scholar 

  40. Vinod H.D. Hands-on Intermediate Econometrics Using R: Templates for Extending Dozens of Practical Examples. World Scientific, Hackensack, NJ, 2008.

    Google Scholar 

  41. Weisberg S. Applied Linear Regression, 3rd edition. Wiley-Interscience, 2005.

    Google Scholar 

  42. Zuur A. F., Ieno E. N. and Meesters E. H.W.G. A Beginner’s Guide to R. Springer, 2009.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer Science+Business Media New York

About this chapter

Cite this chapter

de Micheaux, P.L., Drouilhet, R., Liquet, B. (2013). Confidence Intervals and Hypothesis Testing. In: The R Software. Statistics and Computing, vol 40. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-9020-3_13

Download citation

Publish with us

Policies and ethics