Excursions in Modern Mathematics, Books a la carte edition (9th Edition)
9th Edition
ISBN: 9780134469041
Author: Peter Tannenbaum
Publisher: PEARSON
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Textbook Question
Chapter 7, Problem 28E
Consider the network shown in
a. Find a spanning tree of the network.
b. Calculate the redundancy of the network.
c. What is the diameter of the network?
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29
Suppose that a mound-shaped data set has a
must mean of 10 and standard deviation of 2.
a. About what percentage of the data should
lie between 6 and 12?
b. About what percentage of the data should
lie between 4 and 6?
c. About what percentage of the data should
lie below 4?
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28 Suppose that a mound-shaped data set has a
mean of 10 and standard deviation of 2.
a. About what percentage of the data should
lie between 8 and 12?
b. About what percentage of the data should
lie above 10?
c. About what percentage of the data should
lie above 12?
27 Suppose that you have a data set of 1, 2, 2, 3,
3, 3, 4, 4, 5, and you assume that this sample
represents a population. The mean is 3 and g
the standard deviation is 1.225.10
a. Explain why you can apply the empirical
rule to this data set.
b. Where would "most of the values" in the
population fall, based on this data set?
Chapter 7 Solutions
Excursions in Modern Mathematics, Books a la carte edition (9th Edition)
Ch. 7 - A computer lab has seven computers labeled A...Ch. 7 - The following is a list of the electrical power...Ch. 7 - Consider the network shown in Fig.720_. a. How...Ch. 7 - Consider the network shown in Fig.721_. a. How...Ch. 7 - Consider once again the network shown in. Fig720_....Ch. 7 - Consider once again the network shown in. Fig721_....Ch. 7 - Consider the network shown in. Fig722. This is the...Ch. 7 - Consider the network shown in. Fig723_. This is...Ch. 7 - Consider the tree shown in. Fig724_. a. How many...Ch. 7 - Consider the tree shown in. Fig725. a. How many...
Ch. 7 - In Exercises 11 through 24 you are given...Ch. 7 - Prob. 12ECh. 7 - Prob. 13ECh. 7 - Prob. 14ECh. 7 - In Exercises 11 through 24 you are given...Ch. 7 - Prob. 16ECh. 7 - Prob. 17ECh. 7 - Prob. 18ECh. 7 - Prob. 19ECh. 7 - In Exercises 11 through 24 you are given...Ch. 7 - Prob. 21ECh. 7 - Prob. 22ECh. 7 - Prob. 23ECh. 7 - Prob. 24ECh. 7 - Prob. 25ECh. 7 - Consider the network shown in Fig.727_. a. Find a...Ch. 7 - Prob. 27ECh. 7 - Consider the network shown in Fig.729_. a. Find a...Ch. 7 - Prob. 29ECh. 7 - Prob. 30ECh. 7 - Prob. 31ECh. 7 - Prob. 32ECh. 7 - Prob. 33ECh. 7 - Prob. 34ECh. 7 - Prob. 35ECh. 7 - The 4 by 5 grid shown in Fig. 7-37 represents a...Ch. 7 - Prob. 37ECh. 7 - Find the MST of the network shown in Fig. 7-39...Ch. 7 - Find the MST of the network shown in Fig. 7-40...Ch. 7 - Find the MST of the network shown in Fig. 7-41...Ch. 7 - Prob. 41ECh. 7 - Find the MaxST of the network shown in Fig. 7-39...Ch. 7 - Find the MaxST of the network shown in Fig. 7-40...Ch. 7 - Prob. 44ECh. 7 - The mileage chart in Fig. 742 shows the distances...Ch. 7 - Figure 7-43a shows a network of roads connecting...Ch. 7 - Prob. 47ECh. 7 - Prob. 48ECh. 7 - Prob. 49ECh. 7 - This exercise refers to weighted networks where...Ch. 7 - Prob. 51ECh. 7 - Prob. 52ECh. 7 - Prob. 53ECh. 7 - Prob. 54ECh. 7 - Prob. 55ECh. 7 - Prob. 56ECh. 7 - A bipartite graph is a graph with the property...Ch. 7 - Prob. 58ECh. 7 - Prob. 59E
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- 30 Explain how you can use the empirical rule to find out whether a data set is mound- shaped, using only the values of the data themselves (no histogram available).arrow_forward5. Let X be a positive random variable with finite variance, and let A = (0, 1). Prove that P(X AEX) 2 (1-A)² (EX)² EX2arrow_forward6. Let, for p = (0, 1), and xe R. X be a random variable defined as follows: P(X=-x) = P(X = x)=p. P(X=0)= 1-2p. Show that there is equality in Chebyshev's inequality for X. This means that Chebyshev's inequality, in spite of being rather crude, cannot be improved without additional assumptions.arrow_forward
- 4. Prove that, for any random variable X, the minimum of EIX-al is attained for a = med (X).arrow_forward8. Recall, from Sect. 2.16.4, the likelihood ratio statistic, Ln, which was defined as a product of independent, identically distributed random variables with mean 1 (under the so-called null hypothesis), and the, sometimes more convenient, log-likelihood, log L, which was a sum of independent, identically distributed random variables, which, however, do not have mean log 1 = 0. (a) Verify that the last claim is correct, by proving the more general statement, namely that, if Y is a non-negative random variable with finite mean, then E(log Y) log(EY). (b) Prove that, in fact, there is strict inequality: E(log Y) < log(EY), unless Y is degenerate. (c) Review the proof of Jensen's inequality, Theorem 5.1. Generalize with a glimpse on (b).arrow_forward2. Derive the component transformation equations for tensors shown be- low where [C] = [BA] is the direction cosine matrix from frame A to B. B[T] = [C]^[T][C]T 3. The transport theorem for vectors shows that the time derivative can be constructed from two parts: the first is an explicit frame-dependent change of the vector whereas the second is an active rotational change of the vector. The same holds true for tensors. Starting from the previous result, derive a version of transport theorem for tensors. [C] (^[T])[C] = dt d B dt B [T] + [WB/A]B[T] – TWB/A] (10 pt) (7pt)arrow_forward
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