CHAPTER 5 EXERCISES

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Question 5.84

5.84 The cost of Internet access. In Canada, households spent an average of $80.63 CDN monthly for high-speed broadband access.24 Assume that the standard deviation is $27.32. If you ask an SRS of 500 Canadian households with high-speed broadband access how much they pay, what is the probability that the average amount will exceed $85?

Question 5.85

5.85 Dust in coal mines. A laboratory weighs filters from a coal mine to measure the amount of dust in the mine atmosphere. Repeated measurements of the weight of dust on the same filter vary Normally with standard deviation σ = 0.09 milligram (mg) because the weighing is not perfectly precise. The dust on a particular filter actually weighs 137 mg.

  1. (a) The laboratory reports the mean of three weighings of this filter. What is the distribution of this mean?

  2. (b) What is the probability that the laboratory reports a weight of 140 mg or higher for this filter?

Question 5.86

5.86 The effect of sample size on the standard deviation. Assume that the standard deviation in a very large population is 100.

  1. (a) Calculate the standard deviation for the sample mean for samples of size 1, 4, 25, 100, 250, 500, 1000, and 5000.

  2. (b) Graph your results with the sample size on the x axis and the standard deviation on the y axis.

  3. (c) Summarize the relationship between the sample size and the standard deviation that your graph shows.

Question 5.87

5.87 Marks per round in cricket. Cricket is a dart game that uses the numbers 15 to 20 and the bull’s-eye. Each time you hit one of these regions, you score either 0, 1, 2 or 3 marks. Thus, in a round of three throws, a person can score 0 to 9 marks. Lex plans to play 20 games. Her distribution of marks per round is discrete and strongly skewed. A majority of her rounds result in 0, 1, or 2 marks and only a few are more than 4 marks. Assume that her mean is 2.07 marks per round with a standard deviation of 2.11.

  1. (a) Her 20 games involve 140 rounds of three throws each. What are the mean and standard deviation of the average number of marks in 140 rounds?

  2. (b) Using the central limit theorem, what is the probability that she averages fewer than 2 marks per round?

  3. (c) Do you think that the central limit theorem can be used in this setting? Explain your answer.

Question 5.88

5.88 Common last names. The U.S. Census Bureau says that the 10 most common names in the United States are (in order) Smith, Johnson, Williams, Brown, Jones, Miller, Davis, Garcia, Rodriguez, and Wilson.25 These names account for 4.9% of all U.S. residents. Out of curiosity, you look at the authors of the textbooks for your current courses. There are 12 authors in all. Would you be surprised if none of the names of these authors were among the 10 most common? Give a probability to support your answer and explain the reasoning behind your calculation.

Question 5.89

5.89 Benford’s law. It is a striking fact that the first digits of numbers in legitimate records often follow a distribution known as Benford’s law (see Example 4.12, page 226). Here it is:

First digit 1 2 3 4 5 6 7 8 9
Proportion 0.301 0.176 0.125 0.097 0.079 0.067 0.058 0.051 0.046

Fake records usually have fewer first digits 1, 2, and 3. What is the approximate probability, if Benford’s law holds, that among 1000 randomly chosen invoices there are 575 or fewer in amounts with first digit 1, 2, or 3?

Question 5.90

5.90 Genetics of peas. According to genetic theory, the blossom color in the second generation of a certain cross of sweet peas should be red or white in a 3:1 ratio. That is, each plant has probability 3/4 of having red blossoms, and the blossom colors of separate plants are independent.

  1. (a) What is the probability that exactly 8 out of 10 of these plants have red blossoms?

  2. (b) What is the mean number of red-blossomed plants when 130 plants of this type are grown from seeds?

  3. (c) What is the probability of obtaining at least 90 red-blossomed plants when 130 plants are grown from seeds?

Question 5.91

5.91 Leaking gas tanks. Leakage from underground gasoline tanks at service stations can damage the environment. It is estimated that 25% of these tanks leak. You examine 15 tanks chosen at random, independently of each other.

  1. (a) What is the mean number of leaking tanks in such samples of 15?

  2. (b) What is the probability that 10 or more of the 15 tanks leak?

  3. (c) Now you do a larger study, examining a random sample of 2000 tanks nationally. What is the probability that at least 540 of these tanks are leaking?

Question 5.92

5.92 A roulette payoff. A $1 bet on a single number on a casino’s roulette wheel pays $35 if the ball ends up in the number slot you choose. Here is the distribution of the payoff X:

Payoff X $0 $35
Probability 0.974 0.026

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Each spin of the roulette wheel is independent of other spins.

  1. (a) What are the mean and standard deviation of X?

  2. (b) Sam comes to the casino weekly and bets on 10 spins of the roulette wheel. What does the law of large numbers say about the average payoff Sam receives from his bets each visit?

  3. (c) What does the central limit theorem say about the distribution of Sam’s average payoff after betting on 520 spins in a year?

  4. (d) Sam comes out ahead for the year if his average payoff is greater than $1 (the amount he bet on each spin). What is the probability that Sam ends the year ahead? The true probability is 0.396. Does using the central limit theorem provide a reasonable approximation?

Question 5.93

5.93 A roulette payoff revisited. Refer to the previous exercise. In part (d), the central limit theorem was used to approximate the probability that Sam ends the year ahead. The estimate was about 0.10 too large. Let’s see if we can get closer using the Normal approximation to the binomial with the continuity correction.

  1. (a) If Sam plans to bet on 520 roulette spins, he needs to win at least $520 to break even. If each win gives him $35, what is the minimum number of wins m he must have?

  2. (b) Given p = 1/38 = 0.026, what are the mean and standard deviation of X, the number of wins in 520 roulette spins?

  3. (c) Use the information in the previous two parts to compute P(Xm) with the continuity correction. Does your answer get closer to the exact probability 0.396?

Question 5.94

image 5.94 Learning a foreign language. Does delaying oral practice hinder learning a foreign language? Researchers randomly assigned 25 beginning students of Russian to begin speaking practice immediately and another 25 to delay speaking for four weeks. At the end of the semester both groups took a standard test of comprehension of spoken Russian. Suppose that in the population of all beginning students, the test scores for early speaking vary according to the N(32, 6) distribution and scores for delayed speaking have the N(29, 5) distribution.

  1. (a) What is the sampling distribution of the mean score in the early-speaking group in many repetitions of the experiment? What is the sampling distribution of the mean score in the delayed-speaking group?

  2. (b) If the experiment were repeated many times, what would be the sampling distribution of the difference between the mean scores in the two groups?

  3. (c) What is the probability that the experiment will find (misleadingly) that the mean score for delayed speaking is at least as large as that for early speaking?

Question 5.95

image 5.95 Summer employment of college students. Suppose (as is roughly true) that 88% of college men and 82% of college women were employed last summer. A sample survey interviews SRSs of 400 college men and 400 college women. The two samples are of course independent.

  1. (a) What is the approximate distribution of the proportion of women who worked last summer? What is the approximate distribution of the proportion of men who worked?

  2. (b) The survey wants to compare men and women. What is the approximate distribution of the difference in the proportions who worked, ? Explain the reasoning behind your answer.

  3. (c) What is the probability that in the sample a higher proportion of women than men worked last summer?

Question 5.96

5.96 Income of working couples. A study of working couples measures the income X of the husband and the income Y of the wife in a large number of couples in which both partners are employed. Suppose that you knew the means and and the variances and of both variables in the population.

  1. (a) Is it reasonable to take the mean of the total income to be ? Explain your answer.

  2. (b) Is it reasonable to take the variance of the total income to be ? Explain your answer.

Question 5.97

image 5.97 A random walk. A particle moves along the line in a random walk. That is, the particle starts at the origin (position 0) and moves either right or left in independent steps of length 1. If the particle moves to the right with probability 0.6, its movement at the ith step is a random variable Xi with distribution

P(Xi = 1) = 0.6

P(Xi = −1) = 0.4

The position of the particle after k steps is the sum of these random movements,

Y = X1 + X2 + · · · + Xk

Use the central limit theorem to find the approximate probability that the position of the particle after 500 steps is at least 200 to the right.

Question 5.98

5.98 A lottery payoff. A $1 bet in a state lottery’s Pick 3 game pays $500 if the three-digit number you choose exactly matches the winning number, which is drawn at random. Here is the distribution of the payoff X:

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Payoff X $0 $500
Probability 0.999 0.001

Each day’s drawing is independent of other drawings.

  1. (a) Joe buys a Pick 3 ticket twice a week. The number of times he wins follows a B(104, 0.001) distribution. Using the Poisson approximation to the binomial, what is the probability that he wins at least once?

  2. (b) The exact binomial probability is 0.0988. How accurate is the Poisson approximation here?

  3. (c) If Joe pays $5 a ticket, he needs to win at least twice a year to come out ahead. Using the Poisson approximation, what is the probability that Joe comes out ahead?

Question 5.99

5.99 Poisson distribution? Suppose you find in your spam folder an average of two spam emails every 10 minutes. Furthermore, you find that the rate of spam mail from midnight to 6 A.M. is twice the rate during other parts of the day. Explain whether or not the Poisson distribution is an appropriate model for the spam process.

Question 5.100

5.100 Tossing a die. You are tossing a balanced die that has probability 1/6 of coming up 1 on each toss. Tosses are independent. We are interested in how long we must wait to get the first 1.

  1. (a) The probability of a 1 on the first toss is 1/6. What is the probability that the first toss is not a 1 and the second toss is a 1?

  2. (b) What is the probability that the first two tosses are not 1s and the third toss is a 1? This is the probability that the first 1 occurs on the third toss.

  3. (c) Now you see the pattern. What is the probability that the first 1 occurs on the fourth toss? On the fifth toss?

Question 5.101

image 5.101 The geometric distribution. Generalize your work in Exercise 5.100. You have independent trials, each resulting in a success or a failure. The probability of a success is p on each trial. The binomial distribution describes the count of successes in a fixed number of trials. Now the number of trials is not fixed; instead, continue until you get a success. The random variable Y is the number of the trial on which the first success occurs. What are the possible values of Y? What is the probability P(Y = k) for any of these values? (Comment: The distribution of the number of trials to the first success is called a geometric distribution.)

Question 5.102

5.102 Wi-fi interruptions. Suppose that the number of wi-fi interruptions on your home network follows the Poisson distribution with an average of 0.9 wi-fi interruptions per day.

  1. (a) Show that the probability of no interruptions on a given day is 0.4066.

  2. (b) Treating each day as a trial in a binomial setting, use the binomial formula to compute the probability of no interruptions in a week.

  3. (c) Now, instead of using the binomial model, let’s use the Poisson distribution exclusively. What is the mean number of wi-fi interruptions during a week?

  4. (d) Based on the Poisson mean of part (c), use the Poisson distribution to compute the probability of no interruptions in a week. Confirm that this probability is the same as found part (b). Explain in words why the two ways of computing no interruptions in a week give the same result.

  5. (e) Explain why using the binomial distribution to compute the probability that only one day in the week will not be interruption free would not give the same probability had we used the Poisson distribution to compute that only one interruption occurs during the week.