Why One Needs to Analyze Data

  • Manfred Denker
  • Wojbor A. Woyczyński
  • Bernard Ycart
Part of the Statistics for Industry and Technology book series (SIT)


In this chapter you will find a collection of examples of phenomena where the randomness plays an essential role. Browse through them at your leisure, experiment with the data provided, and use this opportunity to ease your way into Mathematica. The idea is to get a general feel for the issues to be discussed later in the book in greater detail.


Fatigue Income Sarcoma Vorticity Sine 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Bibliographical notes

  1. [1]
    S. Wolfram, The Mathematica Book, Wolfram Media, Champaign, IL, 1996.MATHGoogle Scholar
  2. [2]
    W.T. Shaw and J. Tigg, Applied Mathematica, Addison-Wesley, Reading, MA, 1994.Google Scholar
  3. [3]
    R. Maeder, Programming in Mathematica, Addison-Wesley, Reading, MA, 1991.Google Scholar
  4. [4]
    T.B. Bahder, Mathematica for Scientists and Engineers, Addison-Wesley, reading, MA, 1995.Google Scholar
  5. [5]
    E. Martin, Ed., Mathematica 3.0 Standard Add-on Packages, Wolfram Media, Cambridge University Press, Champaign, IL, 1996.Google Scholar
  6. [6]
    D. Hoffleit, The Fourth Revised Edition of The Bright Star Catalogue, Yale University Observatory, 1982.Google Scholar
  7. [7]
    S.F. Shandarin, Three-dimensional Burgers’ equation as a model for the large-scale structure formation in the universe, Stochastic Models in Geosystems, S.A. Molchanov and W.A. Woyczynski, Eds., Springer-Verlag, New York, 1997.Google Scholar
  8. [8]
    W.A. Woyczynski, Göttingen Lectures on Burgers Turbulence, Springer-Verlag, New York, 1998.Google Scholar
  9. [9]
    C.H. Bennet, G. Brassard, and A.K. Ekert, Quantum Cryptography, Scientific American, October 1992, pp. 50–57.Google Scholar
  10. [10]
    M.F. Barnsley and L.P. Hurd, Fractal Image Compression, AKPeters Ltd., Wellesley, MA. 1993.)Google Scholar
  11. [11]
    H. Niederreiter, Random Number Generation and Quasi-Monte Carlo Methods, SIAM, Philadelphia, 1992.MATHCrossRefGoogle Scholar
  12. [12]
    J.M. Hammersley and D.C. Hanscomb, Monte Carlo Methods, Chapman and Hall, London, 1964.MATHCrossRefGoogle Scholar
  13. [13]
    D. Huff, How to Lie with Statistics, Norton, New York, 1954Google Scholar

Copyright information

© Springer Science+Business Media New York 1998

Authors and Affiliations

  • Manfred Denker
    • 1
  • Wojbor A. Woyczyński
    • 2
  • Bernard Ycart
    • 3
  1. 1.Georg-August-UniversitätGöttingenGermany
  2. 2.Case Western Reserve UniversityCleveland
  3. 3.Université Joseph FourierGrenobleFrance

Personalised recommendations