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  1. Preface
  2. 1 Sampling and Data
    1. Introduction
    2. 1.1 Definitions of Statistics, Probability, and Key Terms
    3. 1.2 Data, Sampling, and Variation in Data and Sampling
    4. 1.3 Frequency, Frequency Tables, and Levels of Measurement
    5. 1.4 Experimental Design and Ethics
    6. 1.5 Data Collection Experiment
    7. 1.6 Sampling Experiment
    8. Key Terms
    9. Chapter Review
    10. Practice
    11. Homework
    12. Bringing It Together: Homework
    13. References
    14. Solutions
  3. 2 Descriptive Statistics
    1. Introduction
    2. 2.1 Stem-and-Leaf Graphs (Stemplots), Line Graphs, and Bar Graphs
    3. 2.2 Histograms, Frequency Polygons, and Time Series Graphs
    4. 2.3 Measures of the Location of the Data
    5. 2.4 Box Plots
    6. 2.5 Measures of the Center of the Data
    7. 2.6 Skewness and the Mean, Median, and Mode
    8. 2.7 Measures of the Spread of the Data
    9. 2.8 Descriptive Statistics
    10. Key Terms
    11. Chapter Review
    12. Formula Review
    13. Practice
    14. Homework
    15. Bringing It Together: Homework
    16. References
    17. Solutions
  4. 3 Probability Topics
    1. Introduction
    2. 3.1 Terminology
    3. 3.2 Independent and Mutually Exclusive Events
    4. 3.3 Two Basic Rules of Probability
    5. 3.4 Contingency Tables
    6. 3.5 Tree and Venn Diagrams
    7. 3.6 Probability Topics
    8. Key Terms
    9. Chapter Review
    10. Formula Review
    11. Practice
    12. Bringing It Together: Practice
    13. Homework
    14. Bringing It Together: Homework
    15. References
    16. Solutions
  5. 4 Discrete Random Variables
    1. Introduction
    2. 4.1 Probability Distribution Function (PDF) for a Discrete Random Variable
    3. 4.2 Mean or Expected Value and Standard Deviation
    4. 4.3 Binomial Distribution
    5. 4.4 Geometric Distribution
    6. 4.5 Hypergeometric Distribution
    7. 4.6 Poisson Distribution
    8. 4.7 Discrete Distribution (Playing Card Experiment)
    9. 4.8 Discrete Distribution (Lucky Dice Experiment)
    10. Key Terms
    11. Chapter Review
    12. Formula Review
    13. Practice
    14. Homework
    15. References
    16. Solutions
  6. 5 Continuous Random Variables
    1. Introduction
    2. 5.1 Continuous Probability Functions
    3. 5.2 The Uniform Distribution
    4. 5.3 The Exponential Distribution
    5. 5.4 Continuous Distribution
    6. Key Terms
    7. Chapter Review
    8. Formula Review
    9. Practice
    10. Homework
    11. References
    12. Solutions
  7. 6 The Normal Distribution
    1. Introduction
    2. 6.1 The Standard Normal Distribution
    3. 6.2 Using the Normal Distribution
    4. 6.3 Normal Distribution (Lap Times)
    5. 6.4 Normal Distribution (Pinkie Length)
    6. Key Terms
    7. Chapter Review
    8. Formula Review
    9. Practice
    10. Homework
    11. References
    12. Solutions
  8. 7 The Central Limit Theorem
    1. Introduction
    2. 7.1 The Central Limit Theorem for Sample Means (Averages)
    3. 7.2 The Central Limit Theorem for Sums
    4. 7.3 Using the Central Limit Theorem
    5. 7.4 Central Limit Theorem (Pocket Change)
    6. 7.5 Central Limit Theorem (Cookie Recipes)
    7. Key Terms
    8. Chapter Review
    9. Formula Review
    10. Practice
    11. Homework
    12. References
    13. Solutions
  9. 8 Confidence Intervals
    1. Introduction
    2. 8.1 A Single Population Mean using the Normal Distribution
    3. 8.2 A Single Population Mean using the Student t Distribution
    4. 8.3 A Population Proportion
    5. 8.4 Confidence Interval (Home Costs)
    6. 8.5 Confidence Interval (Place of Birth)
    7. 8.6 Confidence Interval (Women's Heights)
    8. Key Terms
    9. Chapter Review
    10. Formula Review
    11. Practice
    12. Homework
    13. References
    14. Solutions
  10. 9 Hypothesis Testing with One Sample
    1. Introduction
    2. 9.1 Null and Alternative Hypotheses
    3. 9.2 Outcomes and the Type I and Type II Errors
    4. 9.3 Distribution Needed for Hypothesis Testing
    5. 9.4 Rare Events, the Sample, Decision and Conclusion
    6. 9.5 Additional Information and Full Hypothesis Test Examples
    7. 9.6 Hypothesis Testing of a Single Mean and Single Proportion
    8. Key Terms
    9. Chapter Review
    10. Formula Review
    11. Practice
    12. Homework
    13. References
    14. Solutions
  11. 10 Hypothesis Testing with Two Samples
    1. Introduction
    2. 10.1 Two Population Means with Unknown Standard Deviations
    3. 10.2 Two Population Means with Known Standard Deviations
    4. 10.3 Comparing Two Independent Population Proportions
    5. 10.4 Matched or Paired Samples
    6. 10.5 Hypothesis Testing for Two Means and Two Proportions
    7. Key Terms
    8. Chapter Review
    9. Formula Review
    10. Practice
    11. Homework
    12. Bringing It Together: Homework
    13. References
    14. Solutions
  12. 11 The Chi-Square Distribution
    1. Introduction
    2. 11.1 Facts About the Chi-Square Distribution
    3. 11.2 Goodness-of-Fit Test
    4. 11.3 Test of Independence
    5. 11.4 Test for Homogeneity
    6. 11.5 Comparison of the Chi-Square Tests
    7. 11.6 Test of a Single Variance
    8. 11.7 Lab 1: Chi-Square Goodness-of-Fit
    9. 11.8 Lab 2: Chi-Square Test of Independence
    10. Key Terms
    11. Chapter Review
    12. Formula Review
    13. Practice
    14. Homework
    15. Bringing It Together: Homework
    16. References
    17. Solutions
  13. 12 Linear Regression and Correlation
    1. Introduction
    2. 12.1 Linear Equations
    3. 12.2 Scatter Plots
    4. 12.3 The Regression Equation
    5. 12.4 Testing the Significance of the Correlation Coefficient
    6. 12.5 Prediction
    7. 12.6 Outliers
    8. 12.7 Regression (Distance from School)
    9. 12.8 Regression (Textbook Cost)
    10. 12.9 Regression (Fuel Efficiency)
    11. Key Terms
    12. Chapter Review
    13. Formula Review
    14. Practice
    15. Homework
    16. Bringing It Together: Homework
    17. References
    18. Solutions
  14. 13 F Distribution and One-Way ANOVA
    1. Introduction
    2. 13.1 One-Way ANOVA
    3. 13.2 The F Distribution and the F-Ratio
    4. 13.3 Facts About the F Distribution
    5. 13.4 Test of Two Variances
    6. 13.5 Lab: One-Way ANOVA
    7. Key Terms
    8. Chapter Review
    9. Formula Review
    10. Practice
    11. Homework
    12. References
    13. Solutions
  15. A | Review Exercises (Ch 3-13)
  16. B | Practice Tests (1-4) and Final Exams
  17. C | Data Sets
  18. D | Group and Partner Projects
  19. E | Solution Sheets
  20. F | Mathematical Phrases, Symbols, and Formulas
  21. G | Notes for the TI-83, 83+, 84, 84+ Calculators
  22. H | Tables
  23. Index
1.

The random variable is the mean Internet speed in Megabits per second.

3.

The random variable is the mean number of children an American family has.

5.

The random variable is the proportion of people picked at random in Times Square visiting the city.

7.
  1. H0: p = 0.42
  2. Ha: p < 0.42
9.
  1. H0: μ = 15
  2. Ha: μ ≠ 15
11.

Type I: The mean price of mid-sized cars is $32,000, but we conclude that it is not $32,000.

Type II: The mean price of mid-sized cars is not $32,000, but we conclude that it is $32,000.

13.

α = the probability that you think the bag cannot withstand -15 degrees F, when in fact it can

β = the probability that you think the bag can withstand -15 degrees F, when in fact it cannot

15.

Type I: The procedure will go well, but the doctors think it will not.

Type II: The procedure will not go well, but the doctors think it will.

17.

0.019

19.

0.998

21.

A normal distribution or a Student’s t-distribution

23.

Use a Student’s t-distribution

25.

a normal distribution for a single population mean

27.

It must be approximately normally distributed.

29.

They must both be greater than five.

31.

binomial distribution

33.

The outcome of winning is very unlikely.

35.

H0: μ > = 73
Ha: μ < 73
The p-value is almost zero, which means there is sufficient data to conclude that the mean height of high school students who play basketball on the school team is less than 73 inches at the 5% level. The data do support the claim.

37.

The shaded region shows a low p-value.

39.

Do not reject H0.

41.

means

43.

the mean time spent in jail for 26 first time convicted burglars

45.
  1. 3
  2. 1.5
  3. 1.8
  4. 26
47.

X ¯ ~N( 2.5, 1.5 26 ) X ¯ ~N( 2.5, 1.5 26 )

49.

This is a left-tailed test.

51.

This is a two-tailed test.

53.
Figure 9.25
55.

a right-tailed test

57.

a left-tailed test

59.

This is a left-tailed test.

61.

This is a two-tailed test.

62.
  1. H0: μ = 34; Ha: μ ≠ 34
  2. H0: p ≤ 0.60; Ha: p > 0.60
  3. H0: μ ≥ 100,000; Ha: μ < 100,000
  4. H0: p = 0.29; Ha: p ≠ 0.29
  5. H0: p = 0.05; Ha: p < 0.05
  6. H0: μ ≤ 10; Ha: μ > 10
  7. H0: p = 0.50; Ha: p ≠ 0.50
  8. H0: μ = 6; Ha: μ ≠ 6
  9. H0: p ≥ 0.11; Ha: p < 0.11
  10. H0: μ ≤ 20,000; Ha: μ > 20,000
64.

c

66.
  1. Type I error: We conclude that the mean is not 34 years, when it really is 34 years. Type II error: We conclude that the mean is 34 years, when in fact it really is not 34 years.
  2. Type I error: We conclude that more than 60% of Americans vote in presidential elections, when the actual percentage is at most 60%.Type II error: We conclude that at most 60% of Americans vote in presidential elections when, in fact, more than 60% do.
  3. Type I error: We conclude that the mean starting salary is less than $100,000, when it really is at least $100,000. Type II error: We conclude that the mean starting salary is at least $100,000 when, in fact, it is less than $100,000.
  4. Type I error: We conclude that the proportion of high school seniors who get drunk each month is not 29%, when it really is 29%. Type II error: We conclude that the proportion of high school seniors who get drunk each month is 29% when, in fact, it is not 29%.
  5. Type I error: We conclude that fewer than 5% of adults ride the bus to work in Los Angeles, when the percentage that do is really 5% or more. Type II error: We conclude that 5% or more adults ride the bus to work in Los Angeles when, in fact, fewer that 5% do.
  6. Type I error: We conclude that the mean number of cars a person owns in his or her lifetime is more than 10, when in reality it is not more than 10. Type II error: We conclude that the mean number of cars a person owns in his or her lifetime is not more than 10 when, in fact, it is more than 10.
  7. Type I error: We conclude that the proportion of Americans who prefer to live away from cities is not about half, though the actual proportion is about half. Type II error: We conclude that the proportion of Americans who prefer to live away from cities is half when, in fact, it is not half.
  8. Type I error: We conclude that the duration of paid vacations each year for Europeans is not six weeks, when in fact it is six weeks. Type II error: We conclude that the duration of paid vacations each year for Europeans is six weeks when, in fact, it is not.
  9. Type I error: We conclude that the proportion is less than 11%, when it is really at least 11%. Type II error: We conclude that the proportion of women who develop breast cancer is at least 11%, when in fact it is less than 11%.
  10. Type I error: We conclude that the average tuition cost at private universities is more than $20,000, though in reality it is at most $20,000. Type II error: We conclude that the average tuition cost at private universities is at most $20,000 when, in fact, it is more than $20,000.
68.

b

70.

d

72.

d

74.
  1. H0: μ ≥ 50,000
  2. Ha: μ < 50,000
  3. Let X ¯ X ¯ = the average lifespan of a brand of tires.
  4. normal distribution
  5. z = -2.315
  6. p-value = 0.0103
  7. Check student’s solution.
    1. alpha: 0.05
    2. Decision: Reject the null hypothesis.
    3. Reason for decision: The p-value is less than 0.05.
    4. Conclusion: There is sufficient evidence to conclude that the mean lifespan of the tires is less than 50,000 miles.
  8. (43,537, 49,463)
76.
  1. H0: μ = $1.00
  2. Ha: μ ≠ $1.00
  3. Let X ¯ X ¯ = the average cost of a daily newspaper.
  4. normal distribution
  5. z = –0.866
  6. p-value = 0.3865
  7. Check student’s solution.
    1. Alpha: 0.01
    2. Decision: Do not reject the null hypothesis.
    3. Reason for decision: The p-value is greater than 0.01.
    4. Conclusion: There is sufficient evidence to support the claim that the mean cost of daily papers is $1. The mean cost could be $1.
  8. ($0.84, $1.06)
78.
  1. H0: μ = 10
  2. Ha: μ ≠ 10
  3. Let X ¯ X ¯ the mean number of sick days an employee takes per year.
  4. Student’s t-distribution
  5. t = –1.12
  6. p-value = 0.300
  7. Check student’s solution.
    1. Alpha: 0.05
    2. Decision: Do not reject the null hypothesis.
    3. Reason for decision: The p-value is greater than 0.05.
    4. Conclusion: At the 5% significance level, there is insufficient evidence to conclude that the mean number of sick days is not ten.
  8. (4.9443, 11.806)
80.
  1. H0: p ≥ 0.6
  2. Ha: p < 0.6
  3. Let P′ = the proportion of students who feel more enriched as a result of taking Elementary Statistics.
  4. normal for a single proportion
  5. 1.12
  6. p-value = 0.1308
  7. Check student’s solution.
    1. Alpha: 0.05
    2. Decision: Do not reject the null hypothesis.
    3. Reason for decision: The p-value is greater than 0.05.
    4. Conclusion: There is insufficient evidence to conclude that less than 60 percent of her students feel more enriched.
  8. Confidence Interval: (0.409, 0.654)
    The “plus-4s” confidence interval is (0.411, 0.648)
82.
  1. H0: μ = 4
  2. Ha: μ ≠ 4
  3. Let X ¯ X ¯ the average I.Q. of a set of brown trout.
  4. two-tailed Student's t-test
  5. t = 1.95
  6. p-value = 0.076
  7. Check student’s solution.
    1. Alpha: 0.05
    2. Decision: Reject the null hypothesis.
    3. Reason for decision: The p-value is greater than 0.05
    4. Conclusion: There is insufficient evidence to conclude that the average IQ of brown trout is not four.
  8. (3.8865,5.9468)
84.
  1. H0: p ≥ 0.13
  2. Ha: p < 0.13
  3. Let P′ = the proportion of Americans who have seen or sensed angels
  4. normal for a single proportion
  5. –2.688
  6. p-value = 0.0036
  7. Check student’s solution.
    1. alpha: 0.05
    2. Decision: Reject the null hypothesis.
    3. Reason for decision: The p-value is less than 0.05.
    4. Conclusion: There is sufficient evidence to conclude that the percentage of Americans who have seen or sensed an angel is less than 13%.
  8. (0, 0.0623).
    The“plus-4s” confidence interval is (0.0022, 0.0978)
86.
  1. H0: μ ≥ 129
  2. Ha: μ < 129
  3. Let X ¯ X ¯ = the average time in seconds that Terri finishes Lap 4.
  4. Student's t-distribution
  5. t = 1.209
  6. 0.8792
  7. Check student’s solution.
    1. Alpha: 0.05
    2. Decision: Do not reject the null hypothesis.
    3. Reason for decision: The p-value is greater than 0.05.
    4. Conclusion: There is insufficient evidence to conclude that Terri’s mean lap time is less than 129 seconds.
  8. (128.63, 130.37)
88.
  1. H0: p = 0.60
  2. Ha: p < 0.60
  3. Let P′ = the proportion of family members who shed tears at a reunion.
  4. normal for a single proportion
  5. –1.71
  6. 0.0438
  7. Check student’s solution.
    1. alpha: 0.05
    2. Decision: Reject the null hypothesis.
    3. Reason for decision: p-value < alpha
    4. Conclusion: At the 5% significance level, there is sufficient evidence to conclude that the proportion of family members who shed tears at a reunion is less than 0.60. However, the test is weak because the p-value and alpha are quite close, so other tests should be done.
  8. We are 95% confident that between 38.29% and 61.71% of family members will shed tears at a family reunion. (0.3829, 0.6171). The“plus-4s” confidence interval (see chapter 8) is (0.3861, 0.6139)

Note that here the “large-sample” 1 – PropZTest provides the approximate p-value of 0.0438. Whenever a p-value based on a normal approximation is close to the level of significance, the exact p-value based on binomial probabilities should be calculated whenever possible. This is beyond the scope of this course.

90.
  1. H0: μ ≥ 22
  2. Ha: μ < 22
  3. Let X ¯ X ¯ = the mean number of bubbles per blow.
  4. Student's t-distribution
  5. –2.667
  6. p-value = 0.00486
  7. Check student’s solution.
    1. Alpha: 0.05
    2. Decision: Reject the null hypothesis.
    3. Reason for decision: The p-value is less than 0.05.
    4. Conclusion: There is sufficient evidence to conclude that the mean number of bubbles per blow is less than 22.
  8. (18.501, 21.499)
92.
  1. H0: μ ≤ 1
  2. Ha: μ > 1
  3. Let X ¯ X ¯ = the mean cost in dollars of macaroni and cheese in a certain town.
  4. Student's t-distribution
  5. t = 0.340
  6. p-value = 0.36756
  7. Check student’s solution.
    1. Alpha: 0.05
    2. Decision: Do not reject the null hypothesis.
    3. Reason for decision: The p-value is greater than 0.05
    4. Conclusion: The mean cost could be $1, or less. At the 5% significance level, there is insufficient evidence to conclude that the mean price of a box of macaroni and cheese is more than $1.
  8. (0.8291, 1.241)
94.
  1. H0: p = 0.01
  2. Ha: p > 0.01
  3. Let P′ = the proportion of errors generated
  4. Normal for a single proportion
  5. 2.13
  6. 0.0165
  7. Check student’s solution.
    1. Alpha: 0.05
    2. Decision: Reject the null hypothesis
    3. Reason for decision: The p-value is less than 0.05.
    4. Conclusion: At the 5% significance level, there is sufficient evidence to conclude that the proportion of errors generated is more than 0.01.
  8. Confidence interval: (0, 0.094).
    The“plus-4s” confidence interval is (0.004, 0.144).
96.
  1. H0: p = 0.50
  2. Ha: p < 0.50
  3. Let P′ = the proportion of friends that has a pierced ear.
  4. normal for a single proportion
  5. –1.70
  6. p-value = 0.0448
  7. Check student’s solution.
    1. Alpha: 0.05
    2. Decision: Reject the null hypothesis
    3. Reason for decision: The p-value is less than 0.05. (However, they are very close.)
    4. Conclusion: There is sufficient evidence to support the claim that less than 50% of his friends have pierced ears.
  8. Confidence Interval: (0.245, 0.515): The “plus-4s” confidence interval is (0.259, 0.519).
98.
  1. H0: p = 0.40
  2. Ha: p < 0.40
  3. Let P′ = the proportion of schoolmates who fear public speaking.
  4. normal for a single proportion
  5. –1.01
  6. p-value = 0.1563
  7. Check student’s solution.
    1. Alpha: 0.05
    2. Decision: Do not reject the null hypothesis.
    3. Reason for decision: The p-value is greater than 0.05.
    4. Conclusion: There is insufficient evidence to support the claim that less than 40% of students at the school fear public speaking.
  8. Confidence Interval: (0.3241, 0.4240): The “plus-4s” confidence interval is (0.3257, 0.4250).
100.
  1. H0: p = 0.14
  2. Ha: p < 0.14
  3. Let P′ = the proportion of NYC residents that smoke.
  4. normal for a single proportion
  5. –0.2756
  6. p-value = 0.3914
  7. Check student’s solution.
    1. alpha: 0.05
    2. Decision: Do not reject the null hypothesis.
    3. Reason for decision: The p-value is greater than 0.05.
    4. At the 5% significance level, there is insufficient evidence to conclude that the proportion of NYC residents who smoke is less than 0.14.
  8. Confidence Interval: (0.0502, 0.2070): The “plus-4s” confidence interval (see chapter 8) is (0.0676, 0.2297).
102.
  1. H0: μ = 69,110
  2. Ha: μ > 69,110
  3. Let X ¯ X ¯ = the mean salary in dollars for California registered nurses.
  4. Student's t-distribution
  5. t = 1.719
  6. p-value: 0.0466
  7. Check student’s solution.
    1. Alpha: 0.05
    2. Decision: Reject the null hypothesis.
    3. Reason for decision: The p-value is less than 0.05.
    4. Conclusion: At the 5% significance level, there is sufficient evidence to conclude that the mean salary of California registered nurses exceeds $69,110.
  8. ($68,757, $73,485)
104.

c

106.

c

108.
  1. H0: p = 0.488 Ha: p ≠ 0.488
  2. p-value = 0.0114
  3. alpha = 0.05
  4. Reject the null hypothesis.
  5. At the 5% level of significance, there is enough evidence to conclude that 48.8% of families own stocks.
  6. The survey does not appear to be accurate.
110.
  1. H0: p = 0.517 Ha: p ≠ 0.517
  2. p-value = 0.9203.
  3. alpha = 0.05.
  4. Do not reject the null hypothesis.
  5. At the 5% significance level, there is not enough evidence to conclude that the proportion of homes in Kentucky that are heated by natural gas is 0.517.
  6. However, we cannot generalize this result to the entire nation. First, the sample’s population is only the state of Kentucky. Second, it is reasonable to assume that homes in the extreme north and south will have extreme high usage and low usage, respectively. We would need to expand our sample base to include these possibilities if we wanted to generalize this claim to the entire nation.
112.
  1. H0: µ ≥ 11.52 Ha: µ < 11.52
  2. p-value = 0.000002 which is almost 0.
  3. alpha = 0.05.
  4. Reject the null hypothesis.
  5. At the 5% significance level, there is enough evidence to conclude that the mean amount of summer rain in the northeaster US is less than 11.52 inches, on average.
  6. We would make the same conclusion if alpha was 1% because the p-value is almost 0.
114.
  1. H0: µ ≤ 5.8 Ha: µ > 5.8
  2. p-value = 0.9987
  3. alpha = 0.05
  4. Do not reject the null hypothesis.
  5. At the 5% level of significance, there is not enough evidence to conclude that a woman visits her doctor, on average, more than 5.8 times a year.
116.
  1. H0: µ ≥ 150 Ha: µ < 150
  2. p-value = 0.0622
  3. alpha = 0.01
  4. Do not reject the null hypothesis.
  5. At the 1% significance level, there is not enough evidence to conclude that freshmen students study less than 2.5 hours per day, on average.
  6. The student academic group’s claim appears to be correct.
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