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 13, Problem 36E
(a)
To determine
To find: The
(b)
To determine
To graph: The means.
(c)
To determine
To test: The ANOVA.
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16. (a) Show that IA(w) is a random variable if and only if A E F
15. Let 2 {1, 2,..., 6} and Fo({1, 2, 3, 4), (3, 4, 5, 6}).
(a) Is the function X (w) = 21(3, 4) (w)+711.2,5,6) (w) a random variable? Explain.
(b) Provide a function from 2 to R that is not a random variable with respect to
(N, F).
(c) Write the distribution of X.
(d) Write and plot the distribution function of X.
20. Define the o-field R2. Explain its relation to the o-field R.
Chapter 13 Solutions
Introduction to the Practice of Statistics: w/CrunchIt/EESEE Access Card
Ch. 13.1 - Prob. 1UYKCh. 13.1 - Prob. 2UYKCh. 13 - Prob. 3ECh. 13 - Prob. 4ECh. 13 - Prob. 5ECh. 13 - Prob. 6ECh. 13 - Prob. 7ECh. 13 - Prob. 8ECh. 13 - Prob. 9ECh. 13 - Prob. 10E
Ch. 13 - Prob. 11ECh. 13 - Prob. 13ECh. 13 - Prob. 14ECh. 13 - Prob. 15ECh. 13 - Prob. 16ECh. 13 - Prob. 17ECh. 13 - Prob. 18ECh. 13 - Prob. 19ECh. 13 - Prob. 20ECh. 13 - Prob. 21ECh. 13 - Prob. 22ECh. 13 - Prob. 23ECh. 13 - Prob. 24ECh. 13 - Prob. 25ECh. 13 - Prob. 26ECh. 13 - Prob. 29ECh. 13 - Prob. 30ECh. 13 - Prob. 31ECh. 13 - Prob. 32ECh. 13 - Prob. 33ECh. 13 - Prob. 34ECh. 13 - Prob. 35ECh. 13 - Prob. 36ECh. 13 - Prob. 37ECh. 13 - Prob. 38ECh. 13 - Prob. 39ECh. 13 - Prob. 40ECh. 13 - Prob. 41ECh. 13 - Prob. 42ECh. 13 - Prob. 43ECh. 13 - Prob. 44ECh. 13 - Prob. 45ECh. 13 - Prob. 46ECh. 13 - Prob. 47ECh. 13 - Prob. 48ECh. 13 - Prob. 49ECh. 13 - Prob. 50ECh. 13 - Prob. 51ECh. 13 - Prob. 52ECh. 13 - Prob. 53ECh. 13 - Prob. 54ECh. 13 - Prob. 55ECh. 13 - Prob. 56ECh. 13 - Prob. 12ECh. 13 - Prob. 28ECh. 13 - Prob. 27E
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- 7. Show that An → A as n→∞ I{An} - → I{A} as n→ ∞.arrow_forward7. (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).arrow_forward19. (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.arrow_forward
- 18. Define a bivariate random variable. Provide an example.arrow_forward6. (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_forward12. (a) Explain tail events and the tail o-field. Give an example. (b) State (without proof) the Kolmogorov zero-one law.arrow_forward14. Define X-¹(H) for a given HER. Provide a simple example.arrow_forwardarrow_back_iosSEE MORE QUESTIONSarrow_forward_ios
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