Normal Probability Distributions/Introduction to Normal Distributions and the Standard Normal Curve Question 01 Find three real-life examples of a continuous variable.  Which do you think may be normally distributed?  Why?

]]> Answers will vary.

A continuous variable will be a measurable quantity such as height, weight, temperature, etc.

]]> 1.0000000 0.0000000 0 editor 5 0 ]]> Question 02 What is the the total area under the normal curve?

]]> 1.0000000 0.3333333 0 0 1 100% Area is not measured in percent.

]]> 1]]> Question 05 What is the mean of the standard normal distribution? «math xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»§#956;«/mi»«mo»=«/mo»«/math» {#1}

What is the standard deviation of the standard normal distribution? «math xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»§#963;«/mi»«mo»=«/mo»«/math» {#2}

]]> 2.0000000 0.3333333 0 ]]> Question 06 Describe how you can transform a nonstandard normal distribution to a standard normal distribution.

]]> Transform each data value x into a z-score.  This is done by subtracting the mean from x and dividing by the standard deviation.  In symbols:

«math xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»z«/mi»«mo»=«/mo»«mfrac»«mrow»«mi»x«/mi»«mo»-«/mo»«mi»§#956;«/mi»«/mrow»«mi»§#963;«/mi»«/mfrac»«/math»

]]> 1.0000000 0.0000000 0 editor 5 0 ]]> Question 07 Whay is it correct to say "a" normal distribution and "the" standard normal distribution?

]]> "The" standard normal distribution is used to describe one specific normal distribution «math xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mfenced»«mrow»«mi»§#956;«/mi»«mo»=«/mo»«mn»0«/mn»«mo»,«/mo»«mo»§#160;«/mo»«mi»§#963;«/mi»«mo»=«/mo»«mn»1«/mn»«/mrow»«/mfenced»«/math». "A" normal distribution is used to describe a normal distribution with any mean and standard deviation.

]]> 1.0000000 0.0000000 0 editor 5 0 ]]> Question 08 If a z-score is zero, which of the following must be true?

]]> 1.0000000 0.3333333 0 true true abc The mean is 0.

]]> (c) is true because a z-score equal to zero indicates that the corresponding x-value is equal to the mean.

]]> The corresponding x-value is 0.

]]> (c) is true because a z-score equal to zero indicates that the corresponding x-value is equal to the mean.

]]> The corresponding x-value is equal to the mean.

]]> ]]> Question 21-39 odd Find the indicated area under the standard normal curve to three significant figures.

  1. To the left of z = 1.36
    {#1}

  2. To the left of z = 1.96
    {#2}

  3. To the right of z = -0.65
    {#3}

  4. To the right of z = 1.28
    {#4}

  5. To the left of z = -2.575
    {#5}

  6. To the right of z = 1.615
    {#6}

  7. Between z = 0 and z = 1.54
    {#7}

  8. Between z = -1.53 and z = 0
    {#8}

  9. Between z = -1.96 and z = 1.96
    {#9}

  10. To the left of z = -1.28 and to the right of z = 1.28
    {#10}
]]> 10.0000000 0.3333333 0 ]]> Question 22-40 even Find the indicated area under the standard normal curve to three significant figures.

  1. To the left of z = 0.08
    {#1}

  2. To the left of z = 1.28
    {#2}

  3. To the right of z = -1.95
    {#3}

  4. To the right z = 3.25
    {#4}

  5. To the left of z = -3.16
    {#5}

  6. To the right of z = 2.51
    {#6}

  7. Between z = 0 and z = 2.86
    {#7}

  8. Between z = -0.51 and z = 0
    {#8}

  9. Between z = -2.33 and z = 2.33
    {#9}

  10. To the left of z = -1.96 and to the right of z = 1.96
    {#10}
]]> 10.0000000 0.3333333 0 ]]> Question 41 Manufacturer Claims

You work for a consumer watchdog publication and are testing the advertising claims of a lightbulb manufacturer.  The manufacturer claims that the life span of the bulb is normally distributed, with a mean of 2000 hours and a standard deviation of250 hours.  You test 20 lightbulbs and get the following life spans.

2210 2406 2267 1930 2005 2502 1106 2140 1949 1921
2217 2121 2004 1397 1659 1577 2840 1728 1209 1639

 

  1. Use your graphing calculator to to construct a frequency histogram of the data with 5 classes.  Is it reasonable to assume that the life span is normally distributed?
    {#1}

  2. Find the mean and standard deviation of your sample to three significant figures.
    «math xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»§#956;«/mi»«mo»=«/mo»«/math» {#2}
    «math xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»§#963;«/mi»«mo»=«/mo»«/math» {#3}

]]> 3.0000000 0.3333333 0 <session lang="en" version="2.0"><library closed="false"><mtext style="color:#ffc800" xml:lang="en">variables</mtext><group><command><input><math xmlns="http://www.w3.org/1998/Math/MathML"><mi>m</mi><mo>=</mo><mn>1941</mn><mo>.</mo><mn>35</mn></math></input></command><command><input><math xmlns="http://www.w3.org/1998/Math/MathML"><mi>s</mi><mo>=</mo><mn>432</mn><mo>.</mo><mn>385461</mn></math></input></command></group></library><group><command><input><math xmlns="http://www.w3.org/1998/Math/MathML"><mi>m</mi></math></input><output><math xmlns="http://www.w3.org/1998/Math/MathML"><mn>1941.4</mn></math></output></command></group><group><command><input><math xmlns="http://www.w3.org/1998/Math/MathML"><mi>s</mi></math></input><output><math xmlns="http://www.w3.org/1998/Math/MathML"><mn>432.39</mn></math></output></command></group><group><command><input><math xmlns="http://www.w3.org/1998/Math/MathML"/></input></command></group></session>]]> Normal Probability Distributions/Normal Approximations to Binomial Distributions Question 01 A population has a mean «math xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»§#956;«/mi»«mo»=«/mo»«mn»100«/mn»«/math» and a standard deviation of «math xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»§#963;«/mi»«mo»=«/mo»«mn»15«/mn»«/math». Find the mean and standard deviation (standard error of the mean) of the sampling distribution of sample means with a sample size «math xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mn»50«/mn»«/math».

  1. «math xmlns=¨http://www.w3.org/1998/Math/MathML¨»«msub»«mi»§#956;«/mi»«mover»«mi»x«/mi»«mo»§#175;«/mo»«/mover»«/msub»«mo»=«/mo»«mo»§#160;«/mo»«/math»{#1}

  2. «math xmlns=¨http://www.w3.org/1998/Math/MathML¨»«msub»«mi»§#963;«/mi»«mover»«mi»x«/mi»«mo»§#175;«/mo»«/mover»«/msub»«mo»=«/mo»«mo»§#160;«/mo»«/math»{#2}
]]> 2.0000000 0.3333333 0 ]]> Question 02 A population has a mean «math xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»§#956;«/mi»«mo»=«/mo»«mn»100«/mn»«/math» and a standard deviation of «math xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»§#963;«/mi»«mo»=«/mo»«mn»15«/mn»«/math». Find the mean and standard deviation (standard error of the mean) of the sampling distribution of sample means with a sample size «math xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mn»100«/mn»«/math».

  1. «math xmlns=¨http://www.w3.org/1998/Math/MathML¨»«msub»«mi»§#956;«/mi»«mover»«mi»x«/mi»«mo»§#175;«/mo»«/mover»«/msub»«mo»=«/mo»«mo»§#160;«/mo»«/math»{#1}

  2. «math xmlns=¨http://www.w3.org/1998/Math/MathML¨»«msub»«mi»§#963;«/mi»«mover»«mi»x«/mi»«mo»§#175;«/mo»«/mover»«/msub»«mo»=«/mo»«mo»§#160;«/mo»«/math»{#2}
]]> 2.0000000 0.3333333 0 ]]> Question 03 A population has a mean «math xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»§#956;«/mi»«mo»=«/mo»«mn»100«/mn»«/math» and a standard deviation of «math xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»§#963;«/mi»«mo»=«/mo»«mn»15«/mn»«/math». Find the mean and standard deviation (standard error of the mean) of the sampling distribution of sample means with a sample size «math xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mn»250«/mn»«/math».

  1. «math xmlns=¨http://www.w3.org/1998/Math/MathML¨»«msub»«mi»§#956;«/mi»«mover»«mi»x«/mi»«mo»§#175;«/mo»«/mover»«/msub»«mo»=«/mo»«mo»§#160;«/mo»«/math»{#1}

  2. «math xmlns=¨http://www.w3.org/1998/Math/MathML¨»«msub»«mi»§#963;«/mi»«mover»«mi»x«/mi»«mo»§#175;«/mo»«/mover»«/msub»«mo»=«/mo»«mo»§#160;«/mo»«/math»{#2}
]]> 2.0000000 0.3333333 0 ]]> Question 04 A population has a mean «math xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»§#956;«/mi»«mo»=«/mo»«mn»100«/mn»«/math» and a standard deviation of «math xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»§#963;«/mi»«mo»=«/mo»«mn»15«/mn»«/math». Find the mean and standard deviation (standard error of the mean) of the sampling distribution of sample means with a sample size «math xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mn»1000«/mn»«/math».

  1. «math xmlns=¨http://www.w3.org/1998/Math/MathML¨»«msub»«mi»§#956;«/mi»«mover»«mi»x«/mi»«mo»§#175;«/mo»«/mover»«/msub»«mo»=«/mo»«mo»§#160;«/mo»«/math»{#1}

  2. «math xmlns=¨http://www.w3.org/1998/Math/MathML¨»«msub»«mi»§#963;«/mi»«mover»«mi»x«/mi»«mo»§#175;«/mo»«/mover»«/msub»«mo»=«/mo»«mo»§#160;«/mo»«/math»{#2}
]]> 2.0000000 0.3333333 0 ]]> Question 05 Determine whether the statement is true or false.

As the size of the sample increases, the mean of the distribution  of sample means increase.

]]> 1.0000000 1.0000000 0 True False As the size of the sample increases, the mean of the distribution  of sample means does not change.

]]> ]]> Question 06 Determine whether the statement is true or false.

As the size of the sample increases, the standard deviation of the distribution of the sample means increases.

]]> 1.0000000 1.0000000 0 True False As the size of the sample increases, the standard deviation of the distribution of the sample means decreases.

]]> ]]> Question 07 Determine whether the statement is true or false.

A sampling distribution is only normal if the population is normal.

]]> 1.0000000 1.0000000 0 True A sampling distribution is normal if either «math xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»n«/mi»«mo»§#8805;«/mo»«mn»30«/mn»«/math» or the population is normal.

]]> False As the size of the sample increases, the standard deviation of the distribution of the sample means decreases.

]]> ]]> Question 08 Determine whether the statement is true or false.

If the size of a sample is at least 30, you can use z-scores to determine the probability that a sample mean falls in a given interval of the sampling distribution.

]]> 1.0000000 1.0000000 0 True False A sampling distribution is normal if either «math xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»n«/mi»«mo»§#8805;«/mo»«mn»30«/mn»«/math» or the population is normal.

]]> ]]> Question 17 Heights of Trees

The heights of fully grown sugar maple trees are normally distributed, with a mean of #m feet and a standard deviation of #sd feet.  Random samples of size #n are drawn from the population and the mean of each sample is determined.

  1. What is the mean of the sampling distribution? {#1}

  2. What is the standard error of the mean? {#2}
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 2.0000000 0.3333333 0 <session lang="en" version="2.0"><library closed="false"><mtext style="color:#ffc800" xml:lang="en">variables</mtext><group><command><input><math xmlns="http://www.w3.org/1998/Math/MathML"><mi>m</mi><mo>=</mo><mi>random</mi><mo>(</mo><mn>86</mn><mo>.</mo><mn>5</mn><mo>.</mo><mo>.</mo><mn>88</mn><mo>.</mo><mn>5</mn><mo>)</mo></math></input></command><command><input><math xmlns="http://www.w3.org/1998/Math/MathML"><mi>sd</mi><mo>=</mo><mi>random</mi><mo>(</mo><mn>5</mn><mo>.</mo><mn>75</mn><mo>.</mo><mo>.</mo><mn>6</mn><mo>.</mo><mn>50</mn><mo>)</mo></math></input></command><command><input><math xmlns="http://www.w3.org/1998/Math/MathML"><mi>n</mi><mo>=</mo><mi>random</mi><mo>(</mo><mn>10</mn><mo>.</mo><mo>.</mo><mn>15</mn><mo>)</mo></math></input></command><command><input><math xmlns="http://www.w3.org/1998/Math/MathML"><mi>se</mi><mo>=</mo><mfrac><mi>sd</mi><msqrt><mi>n</mi></msqrt></mfrac></math></input></command></group></library><group><command><input><math xmlns="http://www.w3.org/1998/Math/MathML"><mi>se</mi></math></input><output><math xmlns="http://www.w3.org/1998/Math/MathML"><mn>1.5368</mn></math></output></command></group><group><command><input><math xmlns="http://www.w3.org/1998/Math/MathML"/></input></command></group></session>]]> Question 19 Digital Cameras

The mean price of digital cameras at an electronics store is $#m, with a standard deviation of $#sd.  Random samples of size #n are drawn from this population and the mean of each sample is determined

  1. What is the mean of the sampling distribution?  ${#1}

  2. What is the standar error of the sampling distribution?  ${#2}
]]> 2.0000000 0.3333333 0 <session lang="en" version="2.0"><library closed="false"><mtext style="color:#ffc800" xml:lang="en">variables</mtext><group><command><input><math xmlns="http://www.w3.org/1998/Math/MathML"><mi>m</mi><mo>=</mo><mi>random</mi><mo>(</mo><mn>205</mn><mo>.</mo><mo>.</mo><mn>245</mn><mo>)</mo></math></input></command><command><input><math xmlns="http://www.w3.org/1998/Math/MathML"><mi>sd</mi><mo>=</mo><mi>random</mi><mo>(</mo><mn>6</mn><mo>.</mo><mo>.</mo><mn>10</mn><mo>)</mo></math></input></command><command><input><math xmlns="http://www.w3.org/1998/Math/MathML"><mi>n</mi><mo>=</mo><mi>random</mi><mo>(</mo><mn>35</mn><mo>.</mo><mo>.</mo><mn>45</mn><mo>)</mo></math></input></command><command><input><math xmlns="http://www.w3.org/1998/Math/MathML"><mi>se</mi><mo>=</mo><mfrac><mrow><mi>m</mi><mo>+</mo><mn>0</mn><mo>.</mo><mn>0</mn></mrow><msqrt><mi>n</mi></msqrt></mfrac></math></input></command></group></library><group><command><input><math xmlns="http://www.w3.org/1998/Math/MathML"><mi>se</mi></math></input><output><math xmlns="http://www.w3.org/1998/Math/MathML"><mn>33.58</mn></math></output></command></group><group><command><input><math xmlns="http://www.w3.org/1998/Math/MathML"/></input></command></group></session>]]> Question 21 Red Meat Consumed

The per capita consumption of red meat by people in the United states in a recent year is was normally distributed, with a mean of #m pounds and a standard deviation of #sd pounds.  Random samples of size #n are drawn from this population and the mean of each sample is determined.

  1. What is the mean of the sampling distribution?  {#1}lbs

  2. What is the standard error of the sampling distribution?  {#2}lbs

 

]]> 2.0000000 0.3333333 0 <session lang="en" version="2.0"><library closed="false"><mtext style="color:#ffc800" xml:lang="en">variables</mtext><group><command><input><math xmlns="http://www.w3.org/1998/Math/MathML"><mi>m</mi><mo>=</mo><mi>random</mi><mo>(</mo><mn>100</mn><mo>.</mo><mo>.</mo><mn>120</mn><mo>)</mo></math></input></command><command><input><math xmlns="http://www.w3.org/1998/Math/MathML"><mi>sd</mi><mo>=</mo><mi>random</mi><mo>(</mo><mn>33</mn><mo>.</mo><mn>8</mn><mo>.</mo><mo>.</mo><mn>41</mn><mo>.</mo><mn>8</mn><mo>)</mo></math></input></command><command><input><math xmlns="http://www.w3.org/1998/Math/MathML"><mi>n</mi><mo>=</mo><mi>random</mi><mo>(</mo><mn>18</mn><mo>.</mo><mo>.</mo><mn>25</mn><mo>)</mo></math></input></command><command><input><math xmlns="http://www.w3.org/1998/Math/MathML"><mi>se</mi><mo>=</mo><mfrac><mi>sd</mi><msqrt><mi>n</mi></msqrt></mfrac></math></input></command></group></library></session>]]> Question 25 Plumber Salaries

The population mean annual salary for plumbers is $46,700.  A random sample of 42 plumbers is drawn from this population.  What is the probability (to three significant figures) that the mean salary of the sample is less than $44,000?  Assume «math xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»§#963;«/mi»«mo»=«/mo»«/math»$5600.

«math xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»P«/mi»«mfenced»«mrow»«mover»«mi»x«/mi»«mo»§#175;«/mo»«/mover»«mo»§lt;«/mo»«mo»$«/mo»«mn»44«/mn»«mo»,«/mo»«mn»000«/mn»«/mrow»«/mfenced»«mo»=«/mo»«/math» {#1}

]]> 1.0000000 0.3333333 0 ]]> Question 27 Gas Prices: New England

During a certain week the mean price of gasoline in the New England region was $2.818 per gallon.  A random sample of 32 gass stations is drawn from this population.  What is the probability that the mean price for the sample was between $2.768 and $2.918 that week?  Assume «math xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»§#963;«/mi»«mo»=«/mo»«/math»$0.045.

«math xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»P«/mi»«mfenced»«mrow»«mo»$«/mo»«mn»2«/mn»«mo».«/mo»«mn»768«/mn»«mo»§lt;«/mo»«mover»«mi»x«/mi»«mo»§#175;«/mo»«/mover»«mo»§lt;«/mo»«mn»2«/mn»«mo».«/mo»«mn»918«/mn»«/mrow»«/mfenced»«mo»=«/mo»«/math» {#1}

]]> 1.0000000 0.3333333 0 ]]> Question 29 Heights of Women

The mean height of a woman in the United States (ages 20-29) is 64.1 inches.  A random sample of 60 women in this age group is selected.  What is the probability that the mean height for the sample is greater than 66 inches?  Assume «math xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»§#963;«/mi»«mo»=«/mo»«/math»2.71.

«math xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»P«/mi»«mo»(«/mo»«mover»«mi»x«/mi»«mo»§#175;«/mo»«/mover»«mo»§#62;«/mo»«mn»66«/mn»«mo»§#160;«/mo»«mtext»inches)«/mtext»«mo»=«/mo»«/math»{#1}

]]> 1.0000000 0.3333333 0 ]]> Question 33 Make a Decision

A machine used to fill gallon-sized paint cans is regulated so that the amount of paint dispensed has a mean of 128 ounces and a standard deviation of 0.20 ounce.  You randomly select 40 cans and carefully measure the contents.  The sample mean of the cans is 127.9 ounces.  Does the machine need to be reset?  Explain your reasoning.

]]> 1.0000000 0.0000000 0 editor 15 0 ]]> Question 35 Lumber Cutter

Your lumber company has bought a machine that automatically cuts lumber.  The seller of the machine claims that the machine cuts lumber to a mean length of 8 feet (96 inches) with a standard deviation of 0.5 inch.  Assume the lengths are normally distributed.  You randomly select 40 boards and find that the mean length is 96.25 inches.

  1. Assuming that the seller's claim is correct, what is the probability the mean of the sample is 96.25 inches or more? {#1}

  2. Using your answer from question 1, what do you think of the seller's claim?
    {#2}

  3. Would it be unusual to have an individual board with a length of 96.25 inches?  Why or why not?
    {#3}
]]> 3.0000000 0.3333333 0 ]]> Normal Probability Distributions/Normal Distributions: Finding Probabilities Question 01 Assume the random variable «math style=¨font-family:`Times New Roman`¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»x«/mi»«/math» is normally distributed with a mean «math style=¨font-size:14px;font-family:`Times New Roman`¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»§#956;«/mi»«mo»=«/mo»«mn»86«/mn»«mo»§#160;«/mo»«/math» and standard deviation «math style=¨font-size:14px;font-family:`Times New Roman`¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»§#963;«/mi»«mo»=«/mo»«mn»5«/mn»«/math».  Find the indicated probability to three significant figures.

«math style=¨font-size:14px;font-family:`Times New Roman`¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»P«/mi»«mfenced»«mrow»«mi»x«/mi»«mo»§lt;«/mo»«mn»80«/mn»«/mrow»«/mfenced»«mo»=«/mo»«/math» {#1}

]]> 1.0000000 0.3333333 0 ]]> Question 02 Assume the random variable «math style=¨font-family:`Times New Roman`¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»x«/mi»«/math» is normally distributed with a mean «math style=¨font-family:`Times New Roman`¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»§#956;«/mi»«mo»=«/mo»«mn»86«/mn»«/math» and standard deviation «math style=¨font-family:`Times New Roman`¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»§#963;«/mi»«mo»=«/mo»«mn»5«/mn»«/math».  Find the indicated probability to three significant figures.

«math style=¨font-family:`Times New Roman`¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»P«/mi»«mfenced»«mrow»«mi»x«/mi»«mo»§lt;«/mo»«mn»100«/mn»«/mrow»«/mfenced»«mo»=«/mo»«/math» {#1}

]]> 1.0000000 0.3333333 0 ]]> Question 03 Assume the random variable «math style=¨font-family:`Times New Roman`¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»x«/mi»«/math» is normally distributed with a mean «math style=¨font-size:14px;font-family:`Times New Roman`¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»§#956;«/mi»«mo»=«/mo»«mn»86«/mn»«mo»§#160;«/mo»«/math» and standard deviation «math style=¨font-size:14px;font-family:`Times New Roman`¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»§#963;«/mi»«mo»=«/mo»«mn»5«/mn»«/math».  Find the indicated probability to three significant figures.

«math style=¨font-size:14px;font-family:`Times New Roman`¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»P«/mi»«mfenced»«mrow»«mi»x«/mi»«mo»§#62;«/mo»«mn»92«/mn»«/mrow»«/mfenced»«mo»=«/mo»«/math» {#1}

]]> 1.0000000 0.3333333 0 ]]> Question 04 Assume the random variable «math style=¨font-family:`Times New Roman`¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»x«/mi»«/math» is normally distributed with a mean «math style=¨font-family:`Times New Roman`¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»§#956;«/mi»«mo»=«/mo»«mn»86«/mn»«/math» and standard deviation «math style=¨font-family:`Times New Roman`¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»§#963;«/mi»«mo»=«/mo»«mn»5«/mn»«/math».  Find the indicated probability to three significant figures.

«math style=¨font-family:`Times New Roman`¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»P«/mi»«mfenced»«mrow»«mi»x«/mi»«mo»§#62;«/mo»«mn»75«/mn»«/mrow»«/mfenced»«mo»=«/mo»«/math» {#1}

]]> 1.0000000 0.3333333 0 ]]> Question 05 Assume the random variable «math style=¨font-family:`Times New Roman`¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»x«/mi»«/math» is normally distributed with a mean «math style=¨font-size:14px;font-family:`Times New Roman`¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»§#956;«/mi»«mo»=«/mo»«mn»86«/mn»«mo»§#160;«/mo»«/math» and standard deviation «math style=¨font-size:14px;font-family:`Times New Roman`¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»§#963;«/mi»«mo»=«/mo»«mn»5«/mn»«mo»§#160;«/mo»«/math».  Find the indicated probability to three significant figures.

«math style=¨font-size:14px;font-family:`Times New Roman`¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»P«/mi»«mfenced»«mrow»«mn»70«/mn»«mo»§lt;«/mo»«mi»x«/mi»«mo»§lt;«/mo»«mn»80«/mn»«/mrow»«/mfenced»«mo»=«/mo»«/math» {#1}

]]> 1.0000000 0.3333333 0 ]]> Question 06 Assume the random variable «math style=¨font-family:`Times New Roman`¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»x«/mi»«/math» is normally distributed with a mean «math style=¨font-family:`Times New Roman`¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»§#956;«/mi»«mo»=«/mo»«mn»86«/mn»«/math» and standard deviation «math style=¨font-family:`Times New Roman`¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»§#963;«/mi»«mo»=«/mo»«mn»5«/mn»«/math».  Find the indicated probability to three significant figures.

«math style=¨font-family:`Times New Roman`¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»P«/mi»«mfenced»«mrow»«mn»85«/mn»«mo»§lt;«/mo»«mi»x«/mi»«mo»§lt;«/mo»«mn»95«/mn»«/mrow»«/mfenced»«mo»=«/mo»«/math» {#1}

]]> 1.0000000 0.3333333 0 ]]> Question 07 Assume a member is selected at random from the population represented by the graph.  Find the probability to three significant figures that the member selected at random is from the shaded area of the graph.  Assume the variable «math style=¨font-family:`Times New Roman`¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»x«/mi»«/math» is normally distributed.


Answer: {#1}

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1.0000000 0.3333333 0 ]]> Question 08 Assume a member is selected at random from the population represented by the graph.  Find the probability to three significant figures that the member selected at random is from the shaded area of the graph.  Assume the variable «math style=¨font-family:`Times New Roman`¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»x«/mi»«/math» is normally distributed.


Answer: {#1}

]]> 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1.0000000 0.3333333 0 ]]> Question 09 Assume a member is selected at random from the population represented by the graph.  Find the probability to three significant figures that the member selected at random is from the shaded area of the graph.  Assume the variable «math style=¨font-family:`Times New Roman`¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»x«/mi»«/math» is normally distributed.


Answer: {#1}

]]> 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avCkklP0r9Vf/E50/wD4hq1V8qpJKfqpJfKqSSn6qVHrf/I+Z/xL/wAi+Y0klPvv1U/5Zu/8IY//AFVq61fKqSSn6qSXyqkkp+qkl8qpJKfqpJfKqSSn6qSXyqkkp+qkl8qpJKf/2f/tK3RQaG90b3Nob3AgMy4wADhCSU0EJQAAAAAAEAAAAAAAAAAAAAAAAAAAAAA4QklNA+oAAAAAGBA8P3htbCB2ZXJzaW9uPSIxLjAiIGVuY29kaW5nPSJVVEYtOCI/Pgo8IURPQ1RZUEUgcGxpc3QgUFVCTElDICItLy9BcHBsZS8vRFREIFBMSVNUIDEuMC8vRU4iICJodHRwOi8vd3d3LmFwcGxlLmNvbS9EVERzL1Byb3BlcnR5TGlzdC0xLjAuZHRkIj4KPHBsaXN0IHZlcnNpb249IjEuMCI+CjxkaWN0PgoJPGtleT5jb20uYXBwbGUucHJpbnQuUGFnZUZvcm1hdC5QTUhvcml6b250YWxSZXM8L2tleT4KCTxkaWN0PgoJCTxrZXk+Y29tLmFwcGxlLnByaW50LnRpY2tldC5jcmVhdG9yPC9rZXk+CgkJPHN0cmluZz5jb20uYXBwbGUuam9idGlja2V0PC9zdHJpbmc+CgkJPGtleT5jb20uYXBwbGUucHJpbnQudGlja2V0Lml0ZW1BcnJheTwva2V5PgoJCTxhcnJheT4KCQkJPGRpY3Q+CgkJCQk8a2V5PmNvbS5hcHBsZS5wcmludC5QYWdlRm9ybWF0LlBNSG9yaXpvbnRhbFJlczwva2V5PgoJCQkJPHJlYWw+NzI8L3JlYWw+CgkJCQk8a2V5PmNvbS5hcHBsZS5wcmludC50aWNrZXQuc3RhdGVGbGFnPC9rZXk+CgkJCQk8aW50ZWdlcj4wPC9pbnRlZ2VyPgoJCQk8L2RpY3Q+CgkJPC9hcnJheT4KCTwvZGljdD4KCTxrZXk+Y29tLmFwcGxlLnByaW50LlBhZ2VGb3JtYXQuUE1PcmllbnRhdGlvbjwva2V5PgoJPGRpY3Q+CgkJPGtleT5jb20uYXBwbGUucHJpbnQudGlja2V0LmNyZWF0b3I8L2tleT4KCQk8c3RyaW5nPmNvbS5hcHBsZS5qb2J0aWNrZXQ8L3N0cmluZz4KCQk8a2V5PmNvbS5hcHBsZS5wcmludC50aWNrZXQuaXRlbUFycmF5PC9rZXk+CgkJPGFycmF5PgoJCQk8ZGljdD4KCQkJCTxrZXk+Y29tLmFwcGxlLnByaW50LlBhZ2VGb3JtYXQuUE1PcmllbnRhdGlvbjwva2V5PgoJCQkJPGludGVnZXI+MTwvaW50ZWdlcj4KCQkJCTxrZXk+Y29tLmFwcGxlLnByaW50LnRpY2tldC5zdGF0ZUZsYWc8L2tleT4KCQkJCTxpbnRlZ2VyPjA8L2ludGVnZXI+CgkJCTwvZGljdD4KCQk8L2FycmF5PgoJPC9kaWN0PgoJPGtleT5jb20uYXBwbGUucHJpbnQuUGFnZUZvcm1hdC5QTVNjYWxpbmc8L2tleT4KCTxkaWN0PgoJCTxrZXk+Y29tLmFwcGxlLnByaW50LnRpY2tldC5jcmVhdG9yPC9rZXk+CgkJPHN0cmluZz5jb20uYXBwbGUuam9idGlja2V0PC9zdHJpbmc+CgkJPGtleT5jb20uYXBwbGUucHJpbnQudGlja2V0Lml0ZW1BcnJheTwva2V5PgoJCTxhcnJheT4KCQkJPGRpY3Q+CgkJCQk8a2V5PmNvbS5hcHBsZS5wcmludC5QYWdlRm9ybWF0LlBNU2NhbGluZzwva2V5PgoJCQkJPHJlYWw+MTwvcmVhbD4KCQkJCTxrZXk+Y29tLmFwcGxlLnByaW50LnRpY2tldC5zdGF0ZUZsYWc8L2tleT4KCQkJCTxpbnRlZ2VyPjA8L2ludGVnZXI+CgkJCTwvZGljdD4KCQk8L2FycmF5PgoJPC9kaWN0PgoJPGtleT5jb20uYXBwbGUucHJpbnQuUGFnZUZvcm1hdC5QTVZlcnRpY2FsUmVzPC9rZXk+Cgk8ZGljdD4KCQk8a2V5PmNvbS5hcHBsZS5wcmludC50aWNrZXQuY3JlYXRvcjwva2V5PgoJCTxzdHJpbmc+Y29tLmFwcGxlLmpvYnRpY2tldDwvc3RyaW5nPgoJCTxrZXk+Y29tLmFwcGxlLnByaW50LnRpY2tldC5pdGVtQXJyYXk8L2tleT4KCQk8YXJyYXk+CgkJCTxkaWN0PgoJCQkJPGtleT5jb20uYXBwbGUucHJpbnQuUGFnZUZvcm1hdC5QTVZlcnRpY2FsUmVzPC9rZXk+CgkJCQk8cmVhbD43MjwvcmVhbD4KCQkJCTxrZXk+Y29tLmFwcGxlLnByaW50LnRpY2tldC5zdGF0ZUZsYWc8L2tleT4KCQkJCTxpbnRlZ2VyPjA8L2ludGVnZXI+CgkJCTwvZGljdD4KCQk8L2FycmF5PgoJPC9kaWN0PgoJPGtleT5jb20uYXBwbGUucHJpbnQuUGFnZUZvcm1hdC5QTVZlcnRpY2FsU2NhbGluZzwva2V5PgoJPGRpY3Q+CgkJPGtleT5jb20uYXBwbGUucHJpbnQudGlja2V0LmNyZWF0b3I8L2tleT4KCQk8c3RyaW5nPmNvbS5hcHBsZS5qb2J0aWNrZXQ8L3N0cmluZz4KCQk8a2V5PmNvbS5hcHBsZS5wcmludC50aWNrZXQuaXRlbUFycmF5PC9rZXk+CgkJPGFycmF5PgoJCQk8ZGljdD4KCQkJCTxrZXk+Y29tLmFwcGxlLnByaW50LlBhZ2VGb3JtYXQuUE1WZXJ0aWNhbFNjYWxpbmc8L2tleT4KCQkJCTxyZWFsPjE8L3JlYWw+CgkJCQk8a2V5PmNvbS5hcHBsZS5wcmludC50aWNrZXQuc3RhdGVGbGFnPC9rZXk+CgkJCQk8aW50ZWdlcj4wPC9pbnRlZ2VyPgoJCQk8L2RpY3Q+CgkJPC9hcnJheT4KCTwvZGljdD4KCTxrZXk+Y29tLmFwcGxlLnByaW50LnN1YlRpY2tldC5wYXBlcl9pbmZvX3RpY2tldDwva2V5PgoJPGRpY3Q+CgkJPGtleT5QTVBQRFBhcGVyQ29kZU5hbWU8L2tleT4KCQk8ZGljdD4KCQkJPGtleT5jb20uYXBwbGUucHJpbnQudGlja2V0LmNyZWF0b3I8L2tleT4KCQkJPHN0cmluZz5jb20uYXBwbGUuam9idGlja2V0PC9zdHJpbmc+CgkJCTxrZXk+Y29tLmFwcGxlLnByaW50LnRpY2tldC5pdGVtQXJyYXk8L2tleT4KCQkJPGFycmF5PgoJCQkJPGRpY3Q+CgkJCQkJPGtleT5QTVBQRFBhcGVyQ29kZU5hbWU8L2tleT4KCQkJCQk8c3RyaW5nPkxldHRlcjwvc3RyaW5nPgoJCQkJCTxrZXk+Y29tLmFwcGxlLnByaW50LnRpY2tldC5zdGF0ZUZsYWc8L2tleT4KCQkJCQk8aW50ZWdlcj4wPC9pbnRlZ2VyPgoJCQkJPC9kaWN0PgoJCQk8L2FycmF5PgoJCTwvZGljdD4KCQk8a2V5PlBNVGlvZ2FQYXBlck5hbWU8L2tleT4KCQk8ZGljdD4KCQkJPGtleT5jb20uYXBwbGUucHJpbnQudGlja2V0LmNyZWF0b3I8L2tleT4KCQkJPHN0cmluZz5jb20uYXBwbGUuam9idGlja2V0PC9zdHJpbmc+CgkJCTxrZXk+Y29tLmFwcGxlLnByaW50LnRpY2tldC5pdGVtQXJyYXk8L2tleT4KCQkJPGFycmF5PgoJCQkJPGRpY3Q+CgkJCQkJPGtleT5QTVRpb2dhUGFwZXJOYW1lPC9rZXk+CgkJCQkJPHN0cmluZz5uYS1sZXR0ZXI8L3N0cmluZz4KCQkJCQk8a2V5PmNvbS5hcHBsZS5wcmludC50aWNrZXQuc3RhdGVGbGFnPC9rZXk+CgkJCQkJPGludGVnZXI+MDwvaW50ZWdlcj4KCQkJCTwvZGljdD4KCQkJPC9hcnJheT4KCQk8L2RpY3Q+CgkJPGtleT5jb20uYXBwbGUucHJpbnQuUGFnZUZvcm1hdC5QTUFkanVzdGVkUGFnZVJlY3Q8L2tleT4KCQk8ZGljdD4KCQkJPGtleT5jb20uYXBwbGUucHJpbnQudGlja2V0LmNyZWF0b3I8L2tleT4KCQkJPHN0cmluZz5jb20uYXBwbGUuam9idGlja2V0PC9zdHJpbmc+CgkJCTxrZXk+Y29tLmFwcGxlLnByaW50LnRpY2tldC5pdGVtQXJyYXk8L2tleT4KCQkJPGFycmF5PgoJCQkJPGRpY3Q+CgkJCQkJPGtleT5jb20uYXBwbGUucHJpbnQuUGFnZUZvcm1hdC5QTUFkanVzdGVkUGFnZVJlY3Q8L2tleT4KCQkJCQk8YXJyYXk+CgkJCQkJCTxyZWFsPjAuMDwvcmVhbD4KCQkJCQkJPHJlYWw+MC4wPC9yZWFsPgoJCQkJCQk8cmVhbD43MzQ8L3JlYWw+CgkJCQkJCTxyZWFsPjU3NjwvcmVhbD4KCQkJCQk8L2FycmF5PgoJCQkJCTxrZXk+Y29tLmFwcGxlLnByaW50LnRpY2tldC5zdGF0ZUZsYWc8L2tleT4KCQkJCQk8aW50ZWdlcj4wPC9pbnRlZ2VyPgoJCQkJPC9kaWN0PgoJCQk8L2FycmF5PgoJCTwvZGljdD4KCQk8a2V5PmNvbS5hcHBsZS5wcmludC5QYWdlRm9ybWF0LlBNQWRqdXN0ZWRQYXBlclJlY3Q8L2tleT4KCQk8ZGljdD4KCQkJPGtleT5jb20uYXBwbGUucHJpbnQudGlja2V0LmNyZWF0b3I8L2tleT4KCQkJPHN0cmluZz5jb20uYXBwbGUuam9idGlja2V0PC9zdHJp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1.0000000 0.3333333 0 ]]> Question 10 Assume a member is selected at random from the population represented by the graph.  Find the probability to three significant figures that the member selected at random is from the shaded area of the graph.  Assume the variable «math style=¨font-family:`Times New Roman`¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»x«/mi»«/math» is normally distributed.


Answer: {#1}

]]> 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1.0000000 0.3333333 0 ]]> Question 11 Assume a member is selected at random from the population represented by the graph.  Find the probability to three significant figures that the member selected at random is from the shaded area of the graph.  Assume the variable «math style=¨font-family:`Times New Roman`¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»x«/mi»«/math» is normally distributed.


Answer: {#1}

]]> 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1.0000000 0.3333333 0 ]]> Question 12 Assume a member is selected at random from the population represented by the graph.  Find the probability to three significant figures that the member selected at random is from the shaded area of the graph.  Assume the variable «math style=¨font-family:`Times New Roman`¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mi»x«/mi»«/math» is normally distributed.


Answer: {#1}

]]> 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1.0000000 0.3333333 0 ]]> Question 13 Heights of Men

A survey was conducted to measure the height of U.S. men.  In the survey, respondents were grouped by age.  In the 20-29 age group, the heights were normally distributed, with a mean of 69.6 inches and a standard deviation of 3.0 inches.  A study participant is randomly selected.

  1. Find the probability to three significant figures that his height is less than 66 inches.  {#1}

  2. Find the probability to three significant figures that his height is between 66 and 72 inches.  {#2}

  3. Find the probability to three significant figures that his height is more than 72 inches. {#3}
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 3.0000000 0.3333333 0 ]]> Question 14 Fish Lengths


The lengths of Atlantic croaker fish are normally distributed, with a mean of 10 inches and a standard deviation of 2 inches.  An Atlantic croaker fish is randomly selected.

  1. Find the probability to three significant figures that the length of the fish is less than 7 inches.  {#1}
  2. Find the probability to three significant figures that the length of the fish is between 7 and 15 inches.  {#2}

  3. Find the probability to three significant figure that the length of the fish is more than 15 inches.  {#3}
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 3.0000000 0.3333333 0 ]]> Question 15 ACT Scores

In a recent year, the ACT scores for high school students with a 3.50 to 4.00 grade point average were normally distributed, with a mean of 24.2 and a standard deviation of 4.3.  A student with a 3.50 to 4.00 grade point average who took the ACT during this time is randomly selected.

  1. Find the probability to three significant figures that the student's ACT score is less than 17.  {#1}

  2. Find the probability to three significant figures that the student's ACT score is between 20 and 29.  {#2}

  3. Find the probability to three significant figures that the student's ACT score is more than 32.  {#3}
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The weights of adult male beagles are normally distributed, with a mean of 25 pounds and a standard deviation of three pounds.  A beagle is randomly selected.

  1. Find the probability to three significant figures that the beagle's weight is less than 23 pounds.  {#1}
  2. Find the probability to three significant figures that the beagle's weight is between 23 and 25 pounds.  {#2}

  3. Find the probability to three significant figures that the beagle's weight is more than 27 pounds.  {#3}
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 3.0000000 0.3333333 0 ]]> Question 17 Computer Usage

A survey was conducted to measure the number of hours per week adults in the United states spend on home computers.  In the survey, the number of hours were normally distributed, with a mean of 7 hours and a standard deviation of 1 hour.  A survey participant is randomly selected.

  1. Find the probability to three significant figures that the hours spent on the home computer by the participant are less than 5 hours per week.  {#1}

  2. Find the probability to three significant figures that the hours spent on the home computer by the participant is between 5.5 and 9.5 hours per week.  {#2}

  3. Find the probability to three significant figures that the hours spent on the home computer by the participant are more than 10 hours per week.  {#3}
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 3.0000000 0.3333333 0 ]]> Question 18 Utility Bills

The monthly utility bills in a city are normally distributed with a mean of $100 and a standard deviation of $12.  A utility bill is randomly selected.

  1. Find the probability to three significant figures that the utility bill is less than $70.  {#1}
  2. Find the probability to three significant figures that the utility bill is between $90 and $120.  {#2}

  3. Find the probability to three significant figures that the utility bill is more than $140.  {#3}
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 3.0000000 0.3333333 0 ]]> Question 19 Computer Lab Schedule

The time per week a student uses a lab computer is normally distributed, with a mean of 6.2 hours and a standard deviation of 0.9 hour.  A student is randomly selected.

  1. Find the probability to three significant figures that the student uses a lab computer less than 4 hours per week.  {#1}
  2. Find the probability to three significant figures that the student uses a lab computer between 5 and 7 hours per week.  {#2}

  3. Find the probability to three significant figures that the student uses a lab computer more than 8 hours a week.  {#3}
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 3.0000000 0.3333333 0 ]]> Question 20 Health Club Schedule

The time per workout an athlete uses a stairclimber is normally distributed, with a mean of 20 minutes and a standard deviation of 5 minutes.  An athlete is randomly selected.

  1. Find the probability to three significant figures that the athlete uses a stairclimber for less than 17 minutes.  {#1}
  2. Find the probability to three significant figures that the athlete uses a stairclimber between 20 and 28 minutes.  {#2}

  3. Find the probability to three significant figures that the athlete uses a stairclimber more than 30 minutes.  {#3}
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 3.0000000 0.3333333 0 ]]> Question 21 SAT Critical Reading Scores

The normal distribution of SAT critical reading scores for a certain year has a mean of 503 and a standard deviation of 113.

  1. What percent (to three significant figures) of the SAT verbal scores are less than 600?
    {#1}%

  2. If 1000 SAT verbal scores are randomly selected, about how many would you expect to be greater than 550? (round your answer to the test result)
    {#2}
]]> 2.0000000 0.3333333 0 ]]> Question 22 SAT Math Scores

The normal distribution of SAT math scores for a certain year has a mean of 518 and a standard deviation of 115.

  1. What percent (to three significant figures) of the SAT math scores are less than 500?
    {#1}%

  2. If 1500 SAT math scores are randomly selected, about how many would you expect to be greater than 600? (round your answer to the test result)
    {#2}
]]> 2.0000000 0.3333333 0 ]]> Question 23 Cholesterol

The normal distribution of U.S. woman's total cholesterol levels in the age group of 20-34 has a mean of 186 milligrams per deciliter and a standard deviation of of 35.8 milligrams per deciliter.

  1. What percent of the women (to three significant figures)  have a total cholesterol level less than 200 milligrams per deciliter of blood?
    {#1}%

  2. If 250 U.S. women in the 20-34 age group are randomly selected, about how many would you expect to have a total cholesteral level greater than 240 milligrams per deciliter of blood?  (round your answer to the nearest woman)
    {#2} women
]]> 2.0000000 0.3333333 0 ]]> Question 24 Cholesterol

The normal distribution of U.S. woman's total cholesterol levels in the age group of 55-64 has a mean of 219 milligrams per deciliter and a standard deviation of of 41.6 milligrams per deciliter.

  1. What percent of the women (to three significant figures)  have a total cholesterol level less than 239 milligrams per deciliter of blood?
    {#1}%

  2. If 200 U.S. women in the 55-64 age group are randomly selected, about how many would you expect to have a total cholesteral level greater than 200 milligrams per deciliter of blood?  (round your answer to the nearest woman)
    {#2} women
]]> 2.0000000 0.3333333 0 ]]> Question 25 Fish Lengths

The normal distribution for Atlantic croaker fish lengths has a mean of 10 inches and a standard deviation of 2 inches.

  1. What percent (to three significant figures) of fish are longer than 11 inches?
    {#1}%

  2. If 200 Atlantic croakers are randomly selected, about how many would you expect to be shorter than 8 inches?  (round to the nearest fish)
    {#2} fish
]]> 2.0000000 0.3333333 0 ]]> Question 26 Beagles

The normal distribution of the weight of adult male beagles has a mean of 25 pounds and a standard deviation of 3 pounds.

  1. What percent (to three significant figures) of the beagles have a weight that is greater than 30 pounds?
    {#1}%

  2. If 50 beagles are randomly selected, about how many would you expect to weight less than 22 pounds? (round your answer to the nearest beagle)
    {#2} beagles
]]> 2.0000000 0.3333333 0 ]]> Question 27 Computer Useage

The normal distribution of home computer use in U.S adults has a mean of 7 hours and a standard deviation of 1 hour.

  1. What percent (to three significant figures) of adults spend more than 4 hours per week on a home computer?
    {#1}%

  2. If 35 adults in the United States are randomly selected, about how many would you expect to say they spend less than 5 hours per week on a home computer?  (round your answer to the nearest adult)
    {#2} adult(s)
]]> 2.0000000 0.3333333 0 ]]> Question 28 Utility Bills

The normal distribution of monthly utility bills in a city has a mean of $100 and a standard deviation of $12.

  1. What percent (to three significant figures) of the utulity bills are more than $125?
    {#1}%

  2. If 300 utility bills are randomly selected, about how many would you expect to be less than $90?  (round your answer to the nearest bill)
    {#2} bill(s)
]]> 2.0000000 0.3333333 0 ]]> Question 29 Battery Life Spans

The life span of a battery is normally distributed, with a mean of 2000 hours and a standard deviation of 30 hours.  

  1. What percent (to three significant figures) of batteries have a life span that is more than 2065 hours?
    {#1}%

  2. Would it be unusual for a battery to have a life span that is more than 2065 hours?
    {#2}
]]> 2.0000000 0.3333333 0 ]]> Question 30  Peanuts

Assume the mean annual consumption of peanuts is normally distributed, with a mean of 5.9 pounds per person and a standard deviation of 1.8 pounds per person.  

  1. What percent (to three significant figures) of people annually consume less than 3.1 pounds of peanuts per person?
    {#1}%

  2. Would it be unusual for a person to consume less than 3.1 pounds of peanuts in a year?
    {#2}
]]> 2.0000000 0.3333333 0 ]]> Normal Probability Distributions/Normal Distributions: Finding Values Provide an appropriate response.Two high school students took equivalent language tests, one in German and one in French. The student taking the German test, for which the mean was 66 and the standard deviation was 8, scored an 82, while the student... Provide an appropriate response.

Two high school students took equivalent language tests, one in German and one in French. The student taking the German test, for which the mean was 66 and the standard deviation was 8, scored an 82, while the student taking the French test, for which the mean was 27 and the standard deviation was 5, scored a 35. Compare the scores.]]> 1.0000000 1.0000000 0 true true abc The two scores are statistically the same. x You cannot determine which score is better from the given information. x A score of 35 with a mean of 27 and a standard deviation of 5 is better. x A score of 82 with a mean of 66 and a standard deviation of 8 is better. x Question 01 Use your graphing calculator to find the z-score to three significant figures that corresponds to the cumulative area of «math style=¨font-size:14px¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mn»0«/mn»«mo».«/mo»«mn»7580«/mn»«/math». 

Answer: {#1}

]]> 1.0000000 0.3333333 0 ]]> Question 02 Use your graphing calculator to find the z-score to three significant figures that corresponds to the cumulative area of «math style=¨font-size:14px¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mn»0«/mn»«mo».«/mo»«mn»2090«/mn»«/math». 

Answer: {#1}

]]> 1.0000000 0.3333333 0 ]]> Question 03 Use your graphing calculator to find the z-score to three significant figures that corresponds to the cumulative area of «math style=¨font-size:14px¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mn»0«/mn»«mo».«/mo»«mn»6331«/mn»«/math». 

Answer: {#1}

]]> 1.0000000 0.3333333 0 ]]> Question 04 Use your graphing calculator to find the z-score to three significant figures that corresponds to the cumulative area of «math style=¨font-size:14px¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mn»0«/mn»«mo».«/mo»«mn»0918«/mn»«/math». 

Answer: {#1}

]]> 1.0000000 0.3333333 0 ]]> Question 05 Use your graphing calculator to find the z-score to three significant figures that corresponds to the cumulative area of «math style=¨font-size:14px¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mn»0«/mn»«mo».«/mo»«mn»4364«/mn»«/math». 

Answer: {#1}

]]> 1.0000000 0.3333333 0 ]]> Question 06 Use your graphing calculator to find the z-score to three significant figures that corresponds to the cumulative area of «math style=¨font-size:14px¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mn»0«/mn»«mo».«/mo»«mn»0080«/mn»«/math». 

Answer: {#1}

]]> 1.0000000 0.3333333 0 ]]> Question 07 Use your graphing calculator to find the z-score to three significant figures that corresponds to the cumulative area of «math style=¨font-size:14px¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mn»0«/mn»«mo».«/mo»«mn»9916«/mn»«/math». 

Answer: {#1}

]]> 1.0000000 0.3333333 0 ]]> Question 08 Use your graphing calculator to find the z-score to three significant figures that corresponds to the cumulative area of «math style=¨font-size:14px¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mn»0«/mn»«mo».«/mo»«mn»7995«/mn»«/math». 

Answer: {#1}

]]> 1.0000000 0.3333333 0 ]]> Question 09 Use your graphing calculator to find the z-score to three significant figures that corresponds to the cumulative area of «math style=¨font-size:14px¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mn»0«/mn»«mo».«/mo»«mn»05«/mn»«/math». 

Answer: {#1}

]]> 1.0000000 0.3333333 0 ]]> Question 10 Use your graphing calculator to find the z-score to three significant figures that corresponds to the cumulative area of «math style=¨font-size:14px¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mn»0«/mn»«mo».«/mo»«mn»85«/mn»«/math». 

Answer: {#1}

]]> 1.0000000 0.3333333 0 ]]> Question 11 Use your graphing calculator to find the z-score to three significant figures that corresponds to the cumulative area of «math style=¨font-size:14px¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mn»0«/mn»«mo».«/mo»«mn»94«/mn»«/math». 

Answer: {#1}

]]> 1.0000000 0.3333333 0 ]]> Question 12 Use your graphing calculator to find the z-score to three significant figures that corresponds to the cumulative area of «math style=¨font-size:14px¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«mn»0«/mn»«mo».«/mo»«mn»01«/mn»«/math». 

Answer: {#1}

]]> 1.0000000 0.3333333 0 ]]> Question 13 Use your graphing calculator to find the z-score to three significant figures that corresponds to «math style=¨font-size:14px¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«msub»«mi»P«/mi»«mn»1«/mn»«/msub»«/math». 

Answer: {#1}

]]> 1.0000000 0.3333333 0 ]]> Question 14 Use your graphing calculator to find the z-score to three significant figures that corresponds to «math style=¨font-size:14px¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«msub»«mi»P«/mi»«mn»15«/mn»«/msub»«/math». 

Answer: {#1}

]]> 1.0000000 0.3333333 0 ]]> Question 15 Use your graphing calculator to find the z-score to three significant figures that corresponds to «math style=¨font-size:14px¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«msub»«mi»P«/mi»«mn»20«/mn»«/msub»«/math». 

Answer: {#1}

]]> 1.0000000 0.3333333 0 ]]> Question 16 Use your graphing calculator to find the z-score to three significant figures that corresponds to «math style=¨font-size:14px¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«msub»«mi»P«/mi»«mn»55«/mn»«/msub»«/math». 

Answer: {#1}

]]> 1.0000000 0.3333333 0 ]]> Question 17 Use your graphing calculator to find the z-score to three significant figures that corresponds to «math style=¨font-size:14px¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«msub»«mi»P«/mi»«mn»88«/mn»«/msub»«/math». 

Answer: {#1}

]]> 1.0000000 0.3333333 0 ]]> Question 18 Use your graphing calculator to find the z-score to three significant figures that corresponds to «math style=¨font-size:14px¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«msub»«mi»P«/mi»«mn»67«/mn»«/msub»«/math». 

Answer: {#1}

]]> 1.0000000 0.3333333 0 ]]> Question 19 Use your graphing calculator to find the z-score to three significant figures that corresponds to «math style=¨font-size:14px¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«msub»«mi»P«/mi»«mn»25«/mn»«/msub»«/math». 

Answer: {#1}

]]> 1.0000000 0.3333333 0 ]]> Question 20 Use your graphing calculator to find the z-score to three significant figures that corresponds to «math style=¨font-size:14px¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«msub»«mi»P«/mi»«mn»50«/mn»«/msub»«/math». 

Answer: {#1}

]]> 1.0000000 0.3333333 0 ]]> Question 21 Use your graphing calculator to find the z-score to three significant figures that corresponds to «math style=¨font-size:14px¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«msub»«mi»P«/mi»«mn»75«/mn»«/msub»«/math». 

Answer: {#1}

]]> 1.0000000 0.3333333 0 ]]> Question 22 Use your graphing calculator to find the z-score to three significant figures that corresponds to «math style=¨font-size:14px¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«msub»«mi»P«/mi»«mn»90«/mn»«/msub»«/math». 

Answer: {#1}

]]> 1.0000000 0.3333333 0 ]]> Question 23 Use your graphing calculator to find the z-score to three significant figures that corresponds to «math style=¨font-size:14px¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«msub»«mi»P«/mi»«mn»35«/mn»«/msub»«/math». 

Answer: {#1}

]]> 1.0000000 0.3333333 0 ]]> Question 24 Use your graphing calculator to find the z-score to three significant figures that corresponds to «math style=¨font-size:14px¨ xmlns=¨http://www.w3.org/1998/Math/MathML¨»«msub»«mi»P«/mi»«mn»65«/mn»«/msub»«/math». 

Answer: {#1}

]]> 1.0000000 0.3333333 0 ]]> Question 25 Find the indicated z-score shown in the graph to three significant figures.

                                                 z = {#1}

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1.0000000 0.3333333 0 ]]> Question 26 Find the indicated z-score shown in the graph to three significant figures.

                                                             z = {#1}

]]> 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1.0000000 0.3333333 0 ]]> Question 27 Find the indicated z-score shown in the graph to three significant figures.

                                              z = {#1}

]]> 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1.0000000 0.3333333 0 ]]> Question 28 Find the indicated z-score shown in the graph to three significant figures.

                                                                                        z = {#1}

]]> 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A/eO25zlO2H/0/VUl8qpJKfqpJfKqSSn6qSXyqkkp+qlS636f7HzfV3+l6Fnqels37NrvU9P7R+h3bP9IvmJJJT9T0x6TIiNojbEcfm7Vl5n2j/nFg7J9HY7f/Ofu3fQ2foNu70vtHr/APdT0182JJKfqpJfKqSSn6qSXyqkkp+qkl8qpJKfqpJfKqSSn6qSXyqkkp//2f/tKVRQaG90b3Nob3AgMy4wADhCSU0EJQAAAAAAEAAAAAAAAAAAAAAAAAAAAAA4QklNA+oAAAAAGBA8P3htbCB2ZXJzaW9uPSIxLjAiIGVuY29kaW5nPSJVVEYtOCI/Pgo8IURPQ1RZUEUgcGxpc3QgUFVCTElDICItLy9BcHBsZS8vRFREIFBMSVNUIDEuMC8vRU4iICJodHRwOi8vd3d3LmFwcGxlLmNvbS9EVERzL1Byb3BlcnR5TGlzdC0xLjAuZHRkIj4KPHBsaXN0IHZlcnNpb249IjEuMCI+CjxkaWN0PgoJPGtleT5jb20uYXBwbGUucHJpbnQuUGFnZUZvcm1hdC5QTUhvcml6b250YWxSZXM8L2tleT4KCTxkaWN0PgoJCTxrZXk+Y29tLmFwcGxlLnByaW50LnRpY2tldC5jcmVhdG9yPC9rZXk+CgkJPHN0cmluZz5jb20uYXBwbGUuam9idGlja2V0PC9zdHJpbmc+CgkJPGtleT5jb20uYXBwbGUucHJpbnQudGlja2V0Lml0ZW1BcnJheTwva2V5PgoJCTxhcnJheT4KCQkJPGRpY3Q+CgkJCQk8a2V5PmNvbS5hcHBsZS5wcmludC5QYWdlRm9ybWF0LlBNSG9yaXpvbnRhbFJlczwva2V5PgoJCQkJPHJlYWw+NzI8L3JlYWw+CgkJCQk8a2V5PmNvbS5hcHBsZS5wcmludC50aWNrZXQuc3RhdGVGbGFnPC9rZXk+CgkJCQk8aW50ZWdlcj4wPC9pbnRlZ2VyPgoJCQk8L2RpY3Q+CgkJPC9hcnJheT4KCTwvZGljdD4KCTxrZXk+Y29tLmFwcGxlLnByaW50LlBhZ2VGb3JtYXQuUE1PcmllbnRhdGlvbjwva2V5PgoJPGRpY3Q+CgkJPGtleT5jb20uYXBwbGUucHJpbnQudGlja2V0LmNyZWF0b3I8L2tleT4KCQk8c3RyaW5nPmNvbS5hcHBsZS5qb2J0aWNrZXQ8L3N0cmluZz4KCQk8a2V5PmNvbS5hcHBsZS5wcmludC50aWNrZXQuaXRlbUFycmF5PC9rZXk+CgkJPGFycmF5PgoJCQk8ZGljdD4KCQkJCTxrZXk+Y29tLmFwcGxlLnByaW50LlBhZ2VGb3JtYXQuUE1PcmllbnRhdGlvbjwva2V5PgoJCQkJPGludGVnZXI+MTwvaW50ZWdlcj4KCQkJCTxrZXk+Y29tLmFwcGxlLnByaW50LnRpY2tldC5zdGF0ZUZsYWc8L2tleT4KCQkJCTxpbnRlZ2VyPjA8L2ludGVnZXI+CgkJCTwvZGljdD4KCQk8L2FycmF5PgoJPC9kaWN0PgoJPGtleT5jb20uYXBwbGUucHJpbnQuUGFnZUZvcm1hdC5QTVNjYWxpbmc8L2tleT4KCTxkaWN0PgoJCTxrZXk+Y29tLmFwcGxlLnByaW50LnRpY2tldC5jcmVhdG9yPC9rZXk+CgkJPHN0cmluZz5jb20uYXBwbGUuam9idGlja2V0PC9zdHJpbmc+CgkJPGtleT5jb20uYXBwbGUucHJpbnQudGlja2V0Lml0ZW1BcnJheTwva2V5PgoJCTxhcnJheT4KCQkJPGRpY3Q+CgkJCQk8a2V5PmNvbS5hcHBsZS5wcmludC5QYWdlRm9ybWF0LlBNU2NhbGluZzwva2V5PgoJCQkJPHJlYWw+MTwvcmVhbD4KCQkJCTxrZXk+Y29tLmFwcGxlLnByaW50LnRpY2tldC5zdGF0ZUZsYWc8L2tleT4KCQkJCTxpbnRlZ2VyPjA8L2ludGVnZXI+CgkJCTwvZGljdD4KCQk8L2FycmF5PgoJPC9kaWN0PgoJPGtleT5jb20uYXBwbGUucHJpbnQuUGFnZUZvcm1hdC5QTVZlcnRpY2FsUmVzPC9rZXk+Cgk8ZGljdD4KCQk8a2V5PmNvbS5hcHBsZS5wcmludC50aWNrZXQuY3JlYXRvcjwva2V5PgoJCTxzdHJpbmc+Y29tLmFwcGxlLmpvYnRpY2tldDwvc3RyaW5nPgoJCTxrZXk+Y29tLmFwcGxlLnByaW50LnRpY2tldC5pdGVtQXJyYXk8L2tleT4KCQk8YXJyYXk+CgkJCTxkaWN0PgoJCQkJPGtleT5jb20uYXBwbGUucHJpbnQuUGFnZUZvcm1hdC5QTVZlcnRpY2FsUmVzPC9rZXk+CgkJCQk8cmVhbD43MjwvcmVhbD4KCQkJCTxrZXk+Y29tLmFwcGxlLnByaW50LnRpY2tldC5zdGF0ZUZsYWc8L2tleT4KCQkJCTxpbnRlZ2VyPjA8L2ludGVnZXI+CgkJCTwvZGljdD4KCQk8L2FycmF5PgoJPC9kaWN0PgoJPGtleT5jb20uYXBwbGUucHJpbnQuUGFnZUZvcm1hdC5QTVZlcnRpY2FsU2NhbGluZzwva2V5PgoJPGRpY3Q+CgkJPGtleT5jb20uYXBwbGUucHJpbnQudGlja2V0LmNyZWF0b3I8L2tleT4KCQk8c3RyaW5nPmNvbS5hcHBsZS5qb2J0aWNrZXQ8L3N0cmluZz4KCQk8a2V5PmNvbS5hcHBsZS5wcmludC50aWNrZXQuaXRlbUFycmF5PC9rZXk+CgkJPGFycmF5PgoJCQk8ZGljdD4KCQkJCTxrZXk+Y29tLmFwcGxlLnByaW50LlBhZ2VGb3JtYXQuUE1WZXJ0aWNhbFNjYWxpbmc8L2tleT4KCQkJCTxyZWFsPjE8L3JlYWw+CgkJCQk8a2V5PmNvbS5hcHBsZS5wcmludC50aWNrZXQuc3RhdGVGbGFnPC9rZXk+CgkJCQk8aW50ZWdlcj4wPC9pbnRlZ2VyPgoJCQk8L2RpY3Q+CgkJPC9hcnJheT4KCTwvZGljdD4KCTxrZXk+Y29tLmFwcGxlLnByaW50LnN1YlRpY2tldC5wYXBlcl9pbmZvX3RpY2tldDwva2V5PgoJPGRpY3Q+CgkJPGtleT5QTVBQRFBhcGVyQ29kZU5hbWU8L2tleT4KCQk8ZGljdD4KCQkJPGtleT5jb20uYXBwbGUucHJpbnQudGlja2V0LmNyZWF0b3I8L2tleT4KCQkJPHN0cmluZz5jb20uYXBwbGUuam9idGlja2V0PC9zdHJpbmc+CgkJCTxrZXk+Y29tLmFwcGxlLnByaW50LnRpY2tldC5pdGVtQXJyYXk8L2tleT4KCQkJPGFycmF5PgoJCQkJPGRpY3Q+CgkJCQkJPGtleT5QTVBQRFBhcGVyQ29kZU5hbWU8L2tleT4KCQkJCQk8c3RyaW5nPkxldHRlcjwvc3RyaW5nPgoJCQkJCTxrZXk+Y29tLmFwcGxlLnByaW50LnRpY2tldC5zdGF0ZUZsYWc8L2tleT4KCQkJCQk8aW50ZWdlcj4wPC9pbnRlZ2VyPgoJCQkJPC9kaWN0PgoJCQk8L2FycmF5PgoJCTwvZGljdD4KCQk8a2V5PlBNVGlvZ2FQYXBlck5hbWU8L2tleT4KCQk8ZGljdD4KCQkJPGtleT5jb20uYXBwbGUucHJpbnQudGlja2V0LmNyZWF0b3I8L2tleT4KCQkJPHN0cmluZz5jb20uYXBwbGUuam9idGlja2V0PC9zdHJpbmc+CgkJCTxrZXk+Y29tLmFwcGxlLnByaW50LnRpY2tldC5pdGVtQXJyYXk8L2tleT4KCQkJPGFycmF5PgoJCQkJPGRpY3Q+CgkJCQkJPGtleT5QTVRpb2dhUGFwZXJOYW1lPC9rZXk+CgkJCQkJPHN0cmluZz5uYS1sZXR0ZXI8L3N0cmluZz4KCQkJCQk8a2V5PmNvbS5hcHBsZS5wcmludC50aWNrZXQuc3RhdGVGbGFnPC9rZXk+CgkJCQkJPGludGVnZXI+MDwvaW50ZWdlcj4KCQkJCTwvZGljdD4KCQkJPC9hcnJheT4KCQk8L2RpY3Q+CgkJPGtleT5jb20uYXBwbGUucHJpbnQuUGFnZUZvcm1hdC5QTUFkanVzdGVkUGFnZVJlY3Q8L2tleT4KCQk8ZGljdD4KCQkJPGtleT5jb20uYXBwbGUucHJpbnQudGlja2V0LmNyZWF0b3I8L2tleT4KCQkJPHN0cmluZz5jb20uYXBwbGUuam9idGlja2V0PC9zdHJpbmc+CgkJCTxrZXk+Y29tLmFwcGxlLnByaW50LnRpY2tldC5pdGVtQXJyYXk8L2tleT4KCQkJPGFycmF5PgoJCQkJPGRpY3Q+CgkJCQkJPGtleT5jb20uYXBwbGUucHJpbnQuUGFnZUZvcm1hdC5QTUFkanVzdGVkUGFnZVJlY3Q8L2tleT4KCQkJCQk8YXJyYXk+CgkJCQkJCTxyZWFsPjAuMDwvcmVhbD4KCQkJCQkJPHJlYWw+MC4wPC9yZWFsPgoJCQkJCQk8cmVhbD43MzQ8L3JlYWw+CgkJCQkJCTxyZWFsPjU3NjwvcmVhbD4KCQkJCQk8L2FycmF5PgoJCQkJCTxrZXk+Y29tLmFwcGxlLnByaW50LnRpY2tldC5zdGF0ZUZsYWc8L2tleT4KCQkJCQk8aW50ZWdlcj4wPC9pbnRlZ2VyPgoJCQkJPC9kaWN0PgoJCQk8L2FycmF5PgoJCTwvZGljdD4KCQk8a2V5PmNvbS5hcHBsZS5wcmludC5QYWdlRm9ybWF0LlBNQWRqdXN0ZWRQYXBlclJlY3Q8L2tleT4KCQk8ZGljdD4KCQkJPGtleT5jb20uYXBwbGUucHJpbnQudGlja2V0LmNy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1.0000000 0.3333333 0 ]]> Question 29 Find the indicated z-score shown in the graph to three significant figures.

                            z = {#1}                                 z = {#2}

]]> 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2.0000000 0.3333333 0 ]]> Question 30 Find the indicated z-score shown in the graph to three significant figures.

                            z = {#1}                               z = {#2}

]]> 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2.0000000 0.3333333 0 ]]> Question 31 Find the z-score that has 11.9% of the distribution's area to its left.

Answer: {#1}

]]> 1.0000000 0.3333333 0 ]]> Question 32 Find the z-score that has 78.5% of the distribution's area to its left.

Answer: {#1}

]]> 1.0000000 0.3333333 0 ]]> Question 33 Find the z-score that has 11.9% of the distribution's area to its right.

Answer: {#1}

]]> 1.0000000 0.3333333 0 ]]> Question 34 Find the z-score that has 78.5% of the distribution's area to its right.

Answer: {#1}

]]> 1.0000000 0.3333333 0 ]]> Question 35 Find the z-score for which 80% of the distribution's area lies between -z and z.

{#1}                                          {#2}

]]> 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 2.0000000 0.3333333 0 ]]> Question 36 Find the z-score for which 99% of the distribution's area lies between -z and z.

{#1}                                          {#2}

]]> 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 2.0000000 0.3333333 0 ]]> Question 37 Find the z-score for which 5% of the distribution's area lies between -z and z.

{#1}                                          {#2}

]]> 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nW0ceR8itvVIY5p3DIp6UakK2vcoAus0dUxRNBSn4s8o+WcnaBOwd5BI/GTRMXHzVtNiFVfZ8uzO/XKsyjaGcVRyHPe8kupCddWm8ju/T2AiD5HcAFy6m5E9h0RERERERERERFAPJbj7ifJrUWSaoy0hoxuYtVY7kHkvPJabrCJtS18UQywubxuZMQd4sYEQO7MTqH+G/IHLtjUmZ6U3LZqixcg+Pz0Nm2SUzA0F5imA2orzSuLs7tVxRDKfQWDqYnH7EgiN3kRERERERERERf/U38RERERERERQzvreWD8ddZZBtPYFVNDZrKHl0dDTRnLU3CvlEvdaGnEBdmknNu1iPoA/OMhBnJq28YdHZrd86vfLzkC0zbh2RReXhGvqrrLDgONVDDJTWmFpoIZI6xoy7aohCPqTyMQ95ykV+ERERERERERERERZ98ttF7LgzXEeWnGmjpKjfGr6Ga237EqnzXhzDHDcnO2yA08cXmQ+ZIcfQWkPqLMffFCzWh0ZvHAuQmu7RsbX9y96t9b0pr1aJfYrrPc44wOqtlwh+WKop3NmJvkJnEwcozAimNERERERERERF//V38REREREREXg9i7LwPUeJXXOdj5Vb8QxezxnJVXS4zjCJmwFIMEAk/dNNIwO0cUbEZv7IC7+Czz07qvYXL7Z2Mcr+RNqrcQ1zic0Fx43aCmqDdqYw8qWHIbkQDA5nMYeZHGYdSbt7vohEZNSEREREREREREREREWU25sfv8Awb3ZceUet7JdL1x72jURtyb13Z4KQYrPVh2xU+Q0UIRifR3lM5hZm7pO95Jeko+XpdhOa4tsbE7BnOE3umyLE8oo46+xXqkd3ingkbwfoTMQELs4mBMxATOJMxM7N6tERERERERERf/W38RERERERFF239v4FovArzsfZN7CyY3ZRYerN5lRV1MnXyaSkhZ+6aeV26ADfhJ3YRImpNg+rsq5l7BxrkJyHwufEdQYNNLNx80FeoAatqCOR+uQ5NEYO7SziEbxUjE8YsI9e4ep1GlSIiIiIiIiIiIiIiIuHV0lJcKSooK+niraGtiOCso5wGSKaKQXE45AJnEhIXdnZ26Oyy4umG5l+zsyLL9ganxCbYHEbN7nSXbZWvaWpk/O+CVZzBS1FzslORkNVSyBKLywsDyM0UbPJHFGUi0nwjN8V2PidgzrB75TZJiWT0YV1ivdI7vFPCf8vQmEgIXZxMDZiAmcSZiZ2b1iIiIiIiIiIv/X38RERERERQFv3kdrbjpj9rumcVtTWX7Kp5Lfr7ArRTyVt7yK5CwsNHb6SESIiI5Iwcy6AJGAkTEYMVd9O6A2xtDYVp5G8v6uhrMisRxV+j9E21zKy4N5wBK9VVF0jasugu/Y5mJjEYuYGX0LU+gqIiIiIiIiIiIiIiIiIsxMu0LsriPsm/744qWd8l1JkjtX7r4vUfm+ZKbSxNPc8Upw6xtVNE5H7u/a3QXjj7xOOKK72lN4605CYLR7F1TkYZHjVRPJRVJvFJT1NHWwiBTUlXTyiJxSxsYu7O3QhITByAhJ5dRERERERERf/9DfxERERERUt27y4hs2c0Gj9AYo29963eE5ay2W2qj/ADBisDShD75k9xgeV6QBc3JoWF5D6MLvG8sLyc3j9xWDXmRVW6tvZLLtfknlNNKGS5zUmZ221BUSEZW/HqSUR90po43GFn6MZiL+EYm8TXFRERERERERERERERERERUM3bxJyN9hz8jOLGW02qOQRx+RkNDXMT4rllKYmMsV6o4YyJ5/aEwnZi9oGcg7+2aP12juXmN7CyF9TbUsUujOQ9vdorjqbIJ43Kuf3eGo94s1aP0NbEYyu4gBeazCbuHYPe9xURERERERF//R38RERERRNt7d2rdD4rUZhtPMbfilqjCR6GnqZRetuEsbdfd6CkZ/NqZX6t7EYu7N4v0FndqQ1tbye5rVNBDjYZHxR4u1cztdr7Vk1s2Lk9F2wyC9DTtHN+bYSkjMBkKX6SI+/tlB/LV3dN6R1foHDoME1PiVJimPxTHU1Ixd0tVV1Eju5T1lVK5TVEnToLFIZOIMID0ARFpaREREREREREREREREREREUIbs48ao5B2e12nZ2M/nSbHar3/Fcio55aC7Wmr8PpqGupiCaJ+oiTj3OBOIOQu4D0pm+yOU3DKpo7XuW2XflNx2idoqPc+N0UlTmmPUlLbu4zyG3j7NRC0kTkVU8pEw98ksxSHFA199ZbV17uLFKHNNa5bbMwsFbHER1NsqoakqWaWEJ/dasIjN4JwCQe+KToYO/QmZSIiIiIiIi//S38RERFB+7ORmmuPOPT5HtnOrfjMYxNLQWVz8+617kXlgNHQRd083U/ByEewfEjIQEiaoFPvDmhyPvNfbdC6lj476pOcaH/WraVERX/tHo1VU23HDJhIwcnaFpmkhPt9uQHJwimrUPCzWGtsu/wBU8su1/wB37qGqlqINt57WncbjSREDxxU1DD1GnpooQIuztj72ci6GwdgBcVERERERERERERERERERERERFSnZ/B/W2UZJctmakvN144brrwqmm2ZgZNSNWvWdSnG6WxnCmqwlk6SydGjkORhN5e5lGc3Ivk3xn82l5VaqLaGtbPamkfkRqymeodjpifzai/WMyiKk7oekkskTDABs7RsYk/k3L1Zu/Ue7rQV81PsKy51RRxU81aNsqROppBqo2liGspC7Z6Y3F/EJgAmdnF2YhdmldEREREX/09/F112u9qsNrud8vtzpbLZLLSzV94vFfMFNS0lLTA8s9RPPK4hHHGAuRmTswszu79FAN15h8ULPQT3Gr5I61mp6fs8yOhye2V0795sDdlPSVEsp+JN17Rfo3Un6MzuoYy39pXxHx73Wgx7P63auW3SakpsfwTBrTW3a53OorKkKWOnpSeKGleXuPu8s5xMmboAkRAJdJJyk5W7FGKi0lwnySwvcal6eHM9uXGnxmjtsL1QwBWVdo6vXTgwMZyRQm0gszPG03VuvpY9FcsNoUk0O9OTg4JZqoTKXCtK2wbRI0hnDIDHkVzaprSijbzYXjjijcxcTOT5QUmap4d8e9P3KXIscwKG+5xUzhVV2xcqmlv8AfpqmMhIZ/frgUxRGzxi/0LRt1Zn6dfFWgRF+Mv1ERERfjL9REREXwzeL9W/pX2iIiIvhm8W8PBHbxbo39K+0RERF8v8AL+BfSKqW3eGWgdxXSnym5YrLhewaKWonotlYTUnj99CWrIiqDkqqPsacpXM+55xN/aPo7ORdYkl05zu1mZ0upeTOObaxinKn/M+PbfsrjcIIYpfpIZ75ZwaeqeUTJyllj7vZEQYfEn6K2cvuUuFU1BS724HZ3HWuFIFbetY1dJl8MxyeZ58oUFEcpU7D2M4gVTI/j7Zj7Pd7bHf2k/Di/SDQV+2HwnIaen8294vlVoulqq7bUA4hPRVUk1L7t7xBITgYRTH4iXa5CzkvYf8A384bf/oTFP8Azpv/AEl7ek5c8Va2kpqyHknrAIquEJ4gnyy0QSsMgsTMcMtUEkZdH8RMWJn8HZnUr4Zn2C7GtMl+15mlhzyxRVJ0kt6x25Ut0pBqIxEzheekklBjETF3Hr1Znb+dl7FEX//Unrk9+nTOv3f3z7f+mr79fDqX4v8Ah/uPqPKUB/wq0/hVqcNNf9yfu9/8H+jv/j/bv+V/vro9n/bz/dx/E6j76/aPsFt+X6/+39T7sor/AIVatRrf7l2b9Rv/ABH3P+C/aZfsv/j+s7l7sPmzfqh/M/2fk+c3zvwf1r49ICekBR3VfpQt36ifwIvy/wB6Py0v5D/2n8/43cpE9ICekBPSAnpAT0gJ6QE9IC42R/Bab9Tz4havi/2L7dT/ADPrv+m+u8tcn0gJ6QE9ICekBPSAnpAT0gLosn+7WQ/qXfDKv418N/In9t+o/vPxOq730gJ6QE9ICekBPSAnpAT0gLh3H4fXfqc/Z5ftf2f5j/lvxP7X4F12MfdrHv1LvhlJ8F+G/kQ+xfUf3f4nRd76QE9ICekBPSAnpAT0gJ6QF5XCPupZP1JPyD/dr4V88vsn4n9fVeq9ICekBPSAnpAT0gKD9y/9t/u9/wDGfpE/4H2H/m/7ig/+FWn8KtP4Vafwq1IGp/0qa0/drfeuzfcT71fbofgP/wAh/wBL9d2Lb5f/2Q== 2.0000000 0.3333333 0 ]]> Question 38 Find the z-score for which 12% of the distribution's area lies between -z and z.

{#1}                                          {#2}

]]> 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 2.0000000 0.3333333 0 ]]> Question 39 Heights of Women

In a survey of women in the United States (ages 20-29), the mean height was 64.1 inches with a standard deviation of 2.71 inches.

  1. What height represents the 95th percentile? {#1}


  2. What height represents the first quartile? {#2}
]]> 2.0000000 0.3333333 0 ]]> Question 40 Heights of Men

In a survey of men in the United States (ages 20-29), the mean height was 69.6 inches with a standard deviation of 3.0 inches.

  1. What height represents the 90th percentile?
    {#1} inches

  2. What height represents the first quartile?
    {#2} inches
]]> 2.0000000 0.3333333 0 ]]> Question 41 Apples

The annual per capita utilization of apples (in pounds) in the Uniteed States can be approximated by a normal distribution, as shown in the graph.

 

  1. What annual per capita utilization of apples represents the 10th percentile?
    {#1} pounds

  2. What annual per captita utilization of apples represents the third quartile?
    {#2} pounds
]]> 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2.0000000 0.3333333 0 ]]> Question 42 Oranges

The annual per capita utilization of oranges (in pounds) in the United States can be approximated by a normal distribution, as shown in the graph.

 

  1. What annual per captita utilization of oranges represents the 5th percentile?
    {#1} pounds

  2. What annual per capita utilization of oranges represents the third quartile?
    {#2} pounds
]]> 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2.0000000 0.3333333 0 ]]> Question 43 Heart Transplant Waiting Times

The time spent (in days) waiting for a heart transplant in Ohio and Michigan for patients with type A+ blood can be approximated by a normal distribution, as shown in the graph.

 

  1. What is the shortest time spent waiting for a heart that would still place a patient in the top 30% of waiting times?
    {#1} days

  2. What is the longest time spent for a heart that would still place a patient in the bottom 10% of waiting times?
    {#2} days
]]> 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2.0000000 0.3333333 0 ]]> Question 44 Ice Cream

The annual per capita consumption of ice cream (in pounds) in the United States can be approximated by a normal distribution, as shown in the graph.

 

  1. What is the smallest annual per capita consumption of ice cream that can be in the top 25% of consumptions?
    {#1} pounds

  2. What is the largest annual per capita consumption of ice cream that can be in the bottom 15% of consumptions?
    {#2} pounds
]]> 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 2.0000000 0.3333333 0 ]]> Question 45 Cereal Boxes

The weights of the contents of a cereal box are normally distributed with a mean weight of 20 ounces and a standard deviation of 0.07 ounce.  Boxes in the lower 5% do not meet the minimum weight requirements and must be repackaged.  What is the minimum weight requirement for the cereal box?

Answer:{#1} ounces

]]> 1.0000000 0.3333333 0 ]]> Question 46 Bags of Baby Carrots

The weights of bags of baby carrots are normally distributed with a mean of 32 ounces and a standard deviation of 0.36 ounce.  Bags in the upper 4.5% are too heavy and must be repackaged.  What is the most a bag of baby carrots can weigh and not need to be repackaged?

Answer:{#1} ounces

 

]]> 1.0000000 0.3333333 0 ]]>