By F.M. Dekking, C. Kraaikamp, H.P. Lopuhaä, L.E. Meester

ISBN-10: 1852338962

ISBN-13: 9781852338961

Regrettably this publication significantly lacks step-by-step examples and makes many assumptions approximately what the reader does and doesn't understand. i do know calculus yet lots of the steps within the instance difficulties are left out. each one bankruptcy is split into 4 or 5 sections yet each one bankruptcy is just round ten pages lengthy. which means a complete component of wisdom is stuffed into pages. upload in that half a web page is mostly used for an image and also you prove with a ebook filled with theorems yet missing in substance. those are not even formulation according to say yet as an alternative are chapters choked with beginning issues. To tricky approximately how undesirable this publication is; i purchased a learn consultant which has extra complete distinctive step by step solutions than this e-book. actually the "full solutions" within the again in general encompass one sentence solutions yet there aren't any graphs or step by step information.

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Extra info for A Modern Introduction to Probability and Statistics: Understanding Why and How (Springer Texts in Statistics)

Sample text

1 2 3 4 5 a Fig. 1. Probability mass function and distribution function of M . 3 The Bernoulli and binomial distributions 45 We end this section with three properties of the distribution function F of a random variable X: 1. For a ≤ b one has that F (a) ≤ F (b). This property is an immediate consequence of the fact that a ≤ b implies that the event {X ≤ a} is contained in the event {X ≤ b}. 2. Since F (a) is a probability, the value of the distribution function is always between 0 and 1.

What is the probability that the series ends on the next Sunday? 1 The sample space is Ω = {1234, 1243, 1324, 1342, . , 4321}. The best way to count its elements is by noting that for each of the 6 outcomes of the threeenvelope experiment we can put a fourth envelope in any of 4 positions. Hence Ω has 4 · 6 = 24 elements. 2 The statement “It is certainly not true that neither John nor Mary is to blame” corresponds to the event (J c ∩ M c )c . The statement “John or Mary is to blame, or both” corresponds to the event J ∪ M .

A. Compute P(W | R) and P(W | Rc ). b. Compute P(W ) using the law of total probability. 15 Two independent events A and B are given, and P(B | A ∪ B) = 2/3, P(A | B) = 1/2. What is P(B)? 16 You are diagnosed with an uncommon disease. You know that there only is a 1% chance of getting it. 95. a. Given that you test positive, what is the probability that you really have the disease? b. You obtain a second opinion: an independent repetition of the test. You test positive again. Given this, what is the probability that you really have the disease?

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A Modern Introduction to Probability and Statistics: Understanding Why and How (Springer Texts in Statistics) by F.M. Dekking, C. Kraaikamp, H.P. Lopuhaä, L.E. Meester


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