Introduction to the Practice of Statistics: w/CrunchIt/EESEE Access Card
8th Edition
ISBN: 9781464158933
Author: David S. Moore, George P. McCabe, Bruce A. Craig
Publisher: W. H. Freeman
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Chapter 5.1, Problem 6UYK
(a)
To determine
To find: The population distribution by using the Central Limit Theorem Applet.
(b)
To determine
To find: The estimates of the mean of population and standard deviation based on 10,000 simple random samples.
(c)
To determine
To find: The shape of the histogram and compare it with the normal plot.
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7. (a) Show that if A,, is an increasing sequence of measurable sets with limit A =
Un An, then P(A) is an increasing sequence converging to P(A).
(b) Repeat the same for a decreasing sequence.
(c) Show that the following inequalities hold:
P (lim inf An) lim inf P(A) ≤ lim sup P(A) ≤ P(lim sup A).
(d) Using the above inequalities, show that if A, A, then P(A) + P(A).
19. (a) Define the joint distribution and joint distribution function of a bivariate ran-
dom variable.
(b) Define its marginal distributions and marginal distribution functions.
(c) Explain how to compute the marginal distribution functions from the joint
distribution function.
18. Define a bivariate random variable. Provide an
example.
Chapter 5 Solutions
Introduction to the Practice of Statistics: w/CrunchIt/EESEE Access Card
Ch. 5.1 - Prob. 1UYKCh. 5.1 - Prob. 2UYKCh. 5.1 - Prob. 3UYKCh. 5.1 - Prob. 4UYKCh. 5.1 - Prob. 5UYKCh. 5.1 - Prob. 6UYKCh. 5.1 - Prob. 7UYKCh. 5.1 - Prob. 8UYKCh. 5.1 - Prob. 9ECh. 5.1 - Prob. 10E
Ch. 5.1 - Prob. 11ECh. 5.1 - Prob. 12ECh. 5.1 - Prob. 13ECh. 5.1 - Prob. 14ECh. 5.1 - Prob. 15ECh. 5.1 - Prob. 16ECh. 5.1 - Prob. 17ECh. 5.1 - Prob. 18ECh. 5.1 - Prob. 19ECh. 5.1 - Prob. 20ECh. 5.1 - Prob. 21ECh. 5.1 - Prob. 22ECh. 5.1 - Prob. 23ECh. 5.1 - Prob. 24ECh. 5.1 - Prob. 25ECh. 5.1 - Prob. 26ECh. 5.1 - Prob. 27ECh. 5.1 - Prob. 28ECh. 5.1 - Prob. 29ECh. 5.1 - Prob. 30ECh. 5.1 - Prob. 31ECh. 5.1 - Prob. 32ECh. 5.1 - Prob. 33ECh. 5.1 - Prob. 34ECh. 5.2 - Prob. 35UYKCh. 5.2 - Prob. 36UYKCh. 5.2 - Prob. 37UYKCh. 5.2 - Prob. 38UYKCh. 5.2 - Prob. 39UYKCh. 5.2 - Prob. 40UYKCh. 5.2 - Prob. 41UYKCh. 5.2 - Prob. 42UYKCh. 5.2 - Prob. 43UYKCh. 5.2 - Prob. 44UYKCh. 5.2 - Prob. 45UYKCh. 5.2 - Prob. 46ECh. 5.2 - Prob. 47ECh. 5.2 - Prob. 48ECh. 5.2 - Prob. 49ECh. 5.2 - Prob. 50ECh. 5.2 - Prob. 51ECh. 5.2 - Prob. 52ECh. 5.2 - Prob. 53ECh. 5.2 - Prob. 54ECh. 5.2 - Prob. 55ECh. 5.2 - Prob. 56ECh. 5.2 - Prob. 57ECh. 5.2 - Prob. 58ECh. 5.2 - Prob. 59ECh. 5.2 - Prob. 60ECh. 5.2 - Prob. 61ECh. 5.2 - Prob. 62ECh. 5.2 - Prob. 63ECh. 5.2 - Prob. 64ECh. 5.2 - Prob. 65ECh. 5.2 - Prob. 66ECh. 5.2 - Prob. 67ECh. 5.2 - Prob. 68ECh. 5.2 - Prob. 69ECh. 5.2 - Prob. 70ECh. 5.2 - Prob. 71ECh. 5.2 - Prob. 72ECh. 5.2 - Prob. 73ECh. 5.2 - Prob. 74ECh. 5.2 - Prob. 75ECh. 5 - Prob. 76ECh. 5 - Prob. 77ECh. 5 - Prob. 78ECh. 5 - Prob. 79ECh. 5 - Prob. 80ECh. 5 - Prob. 81ECh. 5 - Prob. 82ECh. 5 - Prob. 83ECh. 5 - Prob. 84ECh. 5 - Prob. 85ECh. 5 - Prob. 86ECh. 5 - Prob. 87ECh. 5 - Prob. 88ECh. 5 - Prob. 89ECh. 5 - Prob. 90E
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- 6. (a) Let (, F, P) be a probability space. Explain when a subset of ?? is measurable and why. (b) Define a probability measure. (c) Using the probability axioms, show that if AC B, then P(A) < P(B). (d) Show that P(AUB) + P(A) + P(B) in general. Write down and prove the formula for the probability of the union of two sets.arrow_forward21. Prove that: {(a, b), - sa≤barrow_forward10. (a) Define the independence of sets A, B, C. (b) Provide an example where A, B, C are pairwise independent but not mutually independent. (c) Give an example where P(AnBnC) = P(A)P(B)P(C), but the sets are not pairwise independent.arrow_forward23. State Bayes' formula. Jaching R. Machine.arrow_forward(d) Show that A, and A' are tail events.arrow_forward11. (a) Define the (mathematical and conceptual) definition of conditional probability P(A|B). (b) Explain the product law in conditional probability. (c) Explain the relation between independence and the conditional probability of two sets.arrow_forward25. Show that if X is a random variable and g(.) is a Borel measurable function, then Y = g(X) is a random variable.arrow_forward24. A factory produces items from two machines: Machine A and Machine B. Machine A produces 60% of the total items, while Machine B produces 40%. The probability that an item produced by Machine A is defective is P(D|A)=0.03. The probability that an item produced by Machine B is defective is P(D|B) = 0.05. (a) What is the probability that a randomly selected product be defective, P(D)? (b) If a randomly selected item from the production line is defective, calculate the probability that it was produced by Machine A, P(A|D).arrow_forward13. Let (, F, P) be a probability space and X a function from 2 to R. Explain when X is a random variable.arrow_forwardarrow_back_iosSEE MORE QUESTIONSarrow_forward_ios
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