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Note: Parts d,e,f of this problem have been changed from original version of textbook.

Salaries for teachers in a particular elementary school district are normally distributed with a mean of $44,000 and a standard deviation of $6500. We randomly survey 10 teachers from that district.

  • In words, X = size 12{X={}} {}
  • In words, X ¯ = size 12{ {overline {X}} ={}} {}
  • X ¯ ~ size 12{ {overline {X}} "~" } {}
  • Find the probability that an individual teacher earns more than $40,000.
  • Find the probability that the average salary for the sample is more than $40,000.
  • Find the probability that the average salary for the sample is more than $50,000.
  • Find the 90th percentile for an individual teacher’s salary.
  • Find the 90th percentile for the average teachers’ salary for samples of 10 teachers.
  • If we surveyed 70 teachers instead of 10, graphically, how would that change the distribution for X ¯ size 12{ {overline {X}} } {} ?
  • If each of the teachers in this elementary school district received a $3000 raise, graphically, how would that change the distribution for X ¯ size 12{ {overline {X}} } {} ?
  • X = size 12{X={}} {} the salary earned by one individual teacher
  • X ¯ = size 12{ {overline {X}} ={}} {} the average salary for the 10 teachers in the sample
  • N ( 44 , 000 , 6500 10 ) size 12{ ital "Xbar" "~" N \( "44","000", { {"6500"} over { sqrt {"10"} } } \) } {}
  • 0.7308
  • 0.9742
  • 0.0018
  • $52,330
  • $46,634
  • The distribution would be more concentrated about the mean. According to the CLT, the spread of the distribution for the sample mean gets smaller when the sample size is increased.
  • If each teacher received a $3,000 raise, then the population mean would increase by $3,000. The population mean is at the center of the distribution for the sample mean. So the distribution for the sample mean would shift $3,000 to the right along the horizontal axis.

The distribution of income in some Third World countries is considered wedge shaped (many very poor people, very few middle income people, and few to many wealthy people). Suppose we pick a country with a wedge distribution. Let the average salary be $2000 per year with a standard deviation of $8000. We randomly survey 1000 residents of that country.

  • In words, X = size 12{X={}} {}
  • In words, X ¯ = size 12{ {overline {X}} ={}} {}
  • X ¯ ~ size 12{ {overline {X}} "~" } {}
  • How is it possible for the standard deviation to be greater than the average?
  • Why is it more likely that the average of the 1000 residents will be from $2000 to $2100 than from $2100 to $2200?

The average length of a maternity stay in a U.S. hospital is said to be 2.4 days with a standard deviation of 0.9 days. We randomly survey 80 women who recently bore children in a U.S. hospital.

  • In words, X = size 12{X={}} {}
  • In words, X ¯ = size 12{ {overline {X}} ={}} {}
  • X ¯ ~ size 12{ {overline {X}} "~" } {}
  • Question removed from text
  • Question removed from text
  • Is it likely that an individual stayed more than 5 days in the hospital? Why or why not?
  • Is it likely that the average stay for the 80 women was more than 5 days? Why or why not?
  • Which is more likely:
    • an individual stayed more than 5 days; or
    • the average stay of 80 women was more than 5 days?
  • N ( 2 . 4, 0 . 9 80 ) size 12{ ital "Xbar" "~" N \( 2 "." 4, { {0 "." 9} over { sqrt {"80"} } } \) } {}
  • Individual

In 1940 the average size of a U.S. farm was 174 acres. Let’s say that the standard deviation was 55 acres. Suppose we randomly survey 38 farmers from 1940. (Source: U.S. Dept. of Agriculture)

  • In words, X = size 12{X={}} {}
  • In words, X ¯ = size 12{ {overline {X}} ={}} {}
  • X ¯ ~ size 12{ {overline {X}} "~" } {}
  • The IQR for X ¯ size 12{ {overline {X}} } {} is from _______ acres to _______ acres.

The stock closing prices of 35 U.S. semiconductor manufacturers are given below. (Source: Wall Street Journal )

  • 8.625
  • 30.25
  • 27.625
  • 46.75
  • 32.875
  • 18.25
  • 5
  • 0.125
  • 2.9375
  • 6.875
  • 28.25
  • 24.25
  • 21
  • 1.5
  • 30.25
  • 71
  • 43.5
  • 49.25
  • 2.5625
  • 31
  • 16.5
  • 9.5
  • 18.5
  • 18
  • 9
  • 10.5
  • 16.625
  • 1.25
  • 18
  • 12.875
  • 7
  • 2.875
  • 2.875
  • 60.25
  • 29.25
  • In words, X = size 12{X={}} {}
    • x ¯ = size 12{ {overline {x}} ={}} {}
    • s x = size 12{s rSub { size 8{x} } ={}} {}
    • n = size 12{n={}} {}
  • Construct a histogram of the distribution of the averages. Start at x = 0 . 0005 size 12{x= - 0 "." "0005"} {} . Make bar widths of 10.
  • In words, describe the distribution of stock prices.
  • Randomly average 5 stock prices together. (Use a random number generator.) Continue averaging 5 pieces together until you have 10 averages. List those 10 averages.
  • Use the 10 averages from (e) to calculate:
    • x ¯ = size 12{ {overline {x}} ={}} {}
    • s x ¯ = size 12{ {overline {s rSub { size 8{x} } }} ={}} {}
  • Construct a histogram of the distribution of the averages. Start at x = 0 . 0005 size 12{x= - 0 "." "0005"} {} . Make bar widths of 10.
  • Does this histogram look like the graph in (c)?
  • In 1 - 2 complete sentences, explain why the graphs either look the same or look different?
  • Based upon the theory of the Central Limit Theorem, X ¯ ~ size 12{ {overline {X}} "~" } {}
  • $20.71; $17.31; 35
  • Exponential distribution, X ~ Exp ( 1/20.71 ) size 12{X "~" N \( "60",9 \) } {}
  • $20.71; $11.14
  • N ( 20 . 71 , 17 . 31 5 ) size 12{ ital "Xbar" "~" N \( "20" "." "71", { {"17" "." "31"} over { sqrt {5} } } \) } {}

Use the Initial Public Offering data (see “Table of Contents) to do this problem.

  • In words, X = size 12{X={}} {}
    • μ X = size 12{μ rSub { size 8{x} } ={}} {}
    • σ X = size 12{σ rSub { size 8{x} } ={}} {}
    • n = size 12{n={}} {}
  • Construct a histogram of the distribution. Start at x = 0 . 50 size 12{x= - 0 "." "50"} {} . Make bar widths of $5.
  • In words, describe the distribution of stock prices.
  • Randomly average 5 stock prices together. (Use a random number generator.) Continue averaging 5 pieces together until you have 15 averages. List those 15 averages.
  • Use the 15 averages from (e) to calculate the following:
    • x ¯ = size 12{ {overline {x}} ={}} {}
    • s x ¯ = size 12{ {overline {s rSub { size 8{x} } }} ={}} {}
  • Construct a histogram of the distribution of the averages. Start at x = 0 . 50 size 12{x= - 0 "." "50"} {} . Make bar widths of $5.
  • Does this histogram look like the graph in (c)? Explain any differences.
  • In 1 - 2 complete sentences, explain why the graphs either look the same or look different?
  • Based upon the theory of the Central Limit Theorem, X ¯ ~ size 12{ {overline {X}} "~" } {}

Try these multiple choice questions.

The next two questions refer to the following information: The time to wait for a particular rural bus is distributed uniformly from 0 to 75 minutes. 100 riders are randomly sampled to learn how long they waited.

The 90th percentile sample average wait time (in minutes) for a sample of 100 riders is:

  • 315.0
  • 40.3
  • 38.5
  • 65.2

B

Would you be surprised, based upon numerical calculations, if the sample average wait time (in minutes) for 100 riders was less than 30 minutes?

  • Yes
  • No
  • There is not enough information.

A

Which of the following is NOT TRUE about the distribution for averages?

  • The mean, median and mode are equal
  • The area under the curve is one
  • The curve never touches the x-axis
  • The curve is skewed to the right

D

The next three questions refer to the following information: The cost of unleaded gasoline in the Bay Area once followed an unknown distribution with a mean of $2.59 and a standard deviation of $0.10. Thirty gas stations from the Bay Area are randomly chosen. We are interested in the average cost of gasoline for the 30 gas stations.

The situation for problems 22 and 23 has been changed from the original version of the textbook.

The distribution to use for the average cost of gasoline for the 30 gas stations is

  • X ~ N ( 2.59 , 0.10 )
  • X ~ N ( 2.59 , 0.10 30 )
  • X ~ N ( 2.59 , 0.10 30 )
  • X ~ N ( 2.59 , 30 0.10 )

B

What is the probability that the average price for 30 gas stations is over $2.69?

  • Almost zero
  • 0.1587
  • 0.0943
  • Unknown

A

Find the probability that the average price for 30 gas stations is less than $2.55.

  • 0.6554
  • 0.3446
  • 0.0142
  • 0.9858
  • 0

C

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Source:  OpenStax, Collaborative statistics homework book: custom version modified by r. bloom. OpenStax CNX. Dec 23, 2009 Download for free at http://legacy.cnx.org/content/col10619/1.2
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