Joint Probability Distributions

  • Jay L. Devore
  • Kenneth N. Berk
Part of the Springer Texts in Statistics book series (STS)


In Chapters 3 and 4, we studied probability models for a single random variable. Many problems in probability and statistics lead to models involving several random variables simultaneously. In this chapter, we first discuss probability models for the joint behavior of several random variables, putting special emphasis on the case in which the variables are independent of each other. We then study expected values of functions of several random variables, including covariance and correlation as measures of the degree of association between two variables.


  1. Larsen, Richard, and Morris Marx, An Introduction to Mathematical Statistics and Its Applications (4th ed.), Prentice Hall, Englewood Cliffs, NJ, 2005. More limited coverage than in the book by Olkin et al., but well written and readable.Google Scholar
  2. Olkin, Ingram, Cyrus Derman, and Leon Gleser, Probability Models and Applications (2nd ed.), Macmillan, New York, 1994. Contains a careful and comprehensive exposition of joint distributions and rules of expectation.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Statistics DepartmentCalifornia Polytechnic State UniversitySan Luis ObispoUSA
  2. 2.Department of MathematicsIllinois State UniversityNormalUSA

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