By Garrett Birkhoff

ISBN-10: 0023100702

ISBN-13: 9780023100703

This vintage, written via younger teachers who turned giants of their box, has formed the certainty of recent algebra for generations of mathematicians and continues to be a invaluable reference and textual content for self research and faculty classes.

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As a multinomial, we have 52 people cross-classiﬁed by type of injury and result of surgery. However, we can also think of the three types of injuries as deﬁning diﬀerent populations. Each person sampled from a population is given arthroscopic surgery and then the result is classiﬁed. Because our primary interest is in the result of surgery, we choose to think of the sampling as product-multinomial. The form of the analysis is identical for both sampling schemes. 229 and df = (3 − 1)(3 − 1) = 4 .

10! The fact that this is a very small number is not surprising. There are a lot of possible tables, so the probability of getting any particular one is small. 000142. Although this probability is also very small, it is more than 20 times larger than the probability of the table given above. Product-Multinomial Distributions For i = 1, . . , (ni1 , . . , nisi ) ∼ Mult(Ni , pi1 , . . , pisi ) ; then we say that the nij ’s have a product-multinomial distribution. By independence, the probability of any set of outcomes, say Pr(nij = rij all i, j), is the product of the multinomial probabilities for each i.

Introduction Substituting these limits into the right-hand side of (2) gives the probability displayed in (1). Using (1), we can compute the expected value and the variance of n. It is not diﬃcult to show that E(n) = λ and Var(n) = λ . Lindgren (1993) gives a more detailed discussion of the assumptions behind the Poisson model. Fisher (1925) gives a nice discussion of the uses of the Poisson model and the analysis of Poisson data. We close with two facts about independent Poisson random variables.

### A survey of modern algebra by Garrett Birkhoff

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