Elementary Statistics: A Step By Step Approach
10th Edition
ISBN: 9781259755330
Author: Allan G. Bluman
Publisher: McGraw-Hill Education
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Chapter 14.1, Problem 21E
Define sampling or selection bias.
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Chapter 14 Solutions
Elementary Statistics: A Step By Step Approach
Ch. 14.1 - The White or Wheat Bread Debate Read the following...Ch. 14.1 - Name the four basic sampling techniques.Ch. 14.1 - Why are samples used in statistics?Ch. 14.1 - What is the basic requirement for a sample?Ch. 14.1 - Why should random numbers be used when you are...Ch. 14.1 - List three incorrect methods that are often used...Ch. 14.1 - What is the principle behind random numbers?Ch. 14.1 - List the advantages and disadvantages of random...Ch. 14.1 - List the advantages and disadvantages of...Ch. 14.1 - List the advantages and disadvantages of...
Ch. 14.1 - List the advantages and disadvantages of cluster...Ch. 14.1 - Teacher Data Using the table of random numbers,...Ch. 14.1 - Prob. 12ECh. 14.1 - Teacher Data Select a cluster sample of 10 states,...Ch. 14.1 - Record High Temperatures Which method of sampling...Ch. 14.1 - Prob. 16ECh. 14.1 - Electoral Votes Select a systematic sample of 10...Ch. 14.1 - Electoral Votes Divide the 50 states into five...Ch. 14.1 - Prob. 19ECh. 14.1 - Define sampling or selection bias.Ch. 14.1 - Prob. 22ECh. 14.1 - Define nonresponsive bias.Ch. 14.1 - Prob. 24ECh. 14.1 - Define response or interview bias.Ch. 14.1 - Prob. 26ECh. 14.1 - Define volunteer bias.Ch. 14.1 - Give an example of how volunteer bias might occur.Ch. 14.2 - Prob. 1ACCh. 14.2 - Prob. 1ECh. 14.2 - Prob. 2ECh. 14.2 - Prob. 3ECh. 14.2 - Prob. 4ECh. 14.2 - Prob. 5ECh. 14.2 - Prob. 6ECh. 14.2 - Prob. 7ECh. 14.2 - Prob. 8ECh. 14.2 - Prob. 9ECh. 14.2 - Exercises 1 through 9 include questions that...Ch. 14.2 - Prob. 11ECh. 14.2 - Exercises 1 through 9 include questions that...Ch. 14.2 - Prob. 13ECh. 14.2 - Prob. 14ECh. 14.2 - Prob. 15ECh. 14.2 - Exercises 1 through 9 include questions that...Ch. 14.2 - Prob. 17ECh. 14.2 - Prob. 18ECh. 14.2 - Prob. 19ECh. 14.3 - Simulations Answer the following questions: 1....Ch. 14.3 - Prob. 1ECh. 14.3 - Prob. 2ECh. 14.3 - Who is responsible for the development of modern...Ch. 14.3 - Prob. 4ECh. 14.3 - Prob. 5ECh. 14.3 - Prob. 6ECh. 14.3 - Prob. 7ECh. 14.3 - Prob. 8ECh. 14 - Hurricanes Select a random sample of eight storms...Ch. 14 - Prob. 14.1.2RECh. 14 - Hurricanes Select a cluster of 10 storms. Compute...Ch. 14 - Prob. 14.1.4RECh. 14 - Prob. 14.1.5RECh. 14 - Prob. 14.1.6RECh. 14 - Prob. 14.1.8RECh. 14 - Prob. 14.2.9RECh. 14 - Prob. 14.2.10RECh. 14 - Prob. 14.2.11RECh. 14 - Prob. 14.2.12RECh. 14 - Prob. 1DACh. 14 - Prob. 2DACh. 14 - Select a cluster sample of 20 individuals, and...Ch. 14 - Prob. 4DACh. 14 - Prob. 5DACh. 14 - Determine whether each statement is true or false....Ch. 14 - Prob. 2CQCh. 14 - Prob. 3CQCh. 14 - Prob. 4CQCh. 14 - Prob. 5CQCh. 14 - Prob. 6CQCh. 14 - Interviewing selected people at a local...Ch. 14 - Prob. 8CQCh. 14 - Prob. 9CQCh. 14 - Prob. 10CQCh. 14 - Blood Pressure Select a random sample of 12...Ch. 14 - Prob. 12CQCh. 14 - Blood Pressure Divide the individuals into...Ch. 14 - Prob. 14CQCh. 14 - Prob. 25CQCh. 14 - Prob. 26CQCh. 14 - Prob. 27CQCh. 14 - Prob. 28CQCh. 14 - For Exercises 2530, explain why the survey...Ch. 14 - Prob. 30CQCh. 14 - Prob. 1DP
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- 11. Prove or disprove: (a) If is a characteristic function, then so is ²; (b) If is a non-negative characteristic function, then so is √√4.arrow_forward4. Suppose that P(X = 1) = P(X = -1) = 1/2, that Y = U(-1, 1) and that X and Y are independent. (a) Show, by direct computation, that X + Y = U(-2, 2). (b) Translate the result to a statement about characteristic functions. (c) Which well-known trigonometric formula did you discover?arrow_forward9. The concentration function of a random variable X is defined as Qx(h) = sup P(x ≤ X ≤x+h), h>0. x (a) Show that Qx+b (h) = Qx(h). (b) Is it true that Qx(ah) =aQx(h)? (c) Show that, if X and Y are independent random variables, then Qx+y (h) min{Qx(h). Qy (h)). To put the concept in perspective, if X1, X2, X, are independent, identically distributed random variables, and S₁ = Z=1Xk, then there exists an absolute constant, A, such that A Qs, (h) ≤ √n Some references: [79, 80, 162, 222], and [204], Sect. 1.5.arrow_forward
- 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? 91002 175/1 3arrow_forward2,3, ample and rical t? the 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?arrow_forward27 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?arrow_forward
- 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_forward3. Prove that, for any random variable X, the minimum of E(X - a)² is attained for a = EX. Provedarrow_forward
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