Abstract
The work in Part II was concerned mostly with one random variable. Actual experiments usually involve more than one outcome; an obvious case is the yield of a corn field which depends on (the random) rainfall, ground conditions, sunlight even though (the non–random) seed, fertilizer, irrigation might be used. Here we continue to explain some probabilistic concepts for multiple or joint random variables (random vectors) concentrating, in this lesson and the next, on the discrete case. Following that we shall treat a continuous case.
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© 1989 Springer Science+Business Media New York
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Nguyen, H.T., Rogers, G.S. (1989). Joint Distributions: Discrete. In: Fundamentals of Mathematical Statistics. Springer Texts in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-1013-9_35
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DOI: https://doi.org/10.1007/978-1-4612-1013-9_35
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4612-6984-7
Online ISBN: 978-1-4612-1013-9
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