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Introductory Business Statistics

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Table of contents
  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 Levels of Measurement
    5. 1.4 Experimental Design and Ethics
    6. Key Terms
    7. Chapter Review
    8. Homework
    9. References
    10. Solutions
  3. 2 Descriptive Statistics
    1. Introduction
    2. 2.1 Display Data
    3. 2.2 Measures of the Location of the Data
    4. 2.3 Measures of the Center of the Data
    5. 2.4 Sigma Notation and Calculating the Arithmetic Mean
    6. 2.5 Geometric Mean
    7. 2.6 Skewness and the Mean, Median, and Mode
    8. 2.7 Measures of the Spread of the Data
    9. Key Terms
    10. Chapter Review
    11. Formula Review
    12. Practice
    13. Homework
    14. Bringing It Together: Homework
    15. References
    16. 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 and Probability Trees
    6. 3.5 Venn Diagrams
    7. Key Terms
    8. Chapter Review
    9. Formula Review
    10. Practice
    11. Bringing It Together: Practice
    12. Homework
    13. Bringing It Together: Homework
    14. References
    15. Solutions
  5. 4 Discrete Random Variables
    1. Introduction
    2. 4.1 Hypergeometric Distribution
    3. 4.2 Binomial Distribution
    4. 4.3 Geometric Distribution
    5. 4.4 Poisson Distribution
    6. Key Terms
    7. Chapter Review
    8. Formula Review
    9. Practice
    10. Homework
    11. References
    12. Solutions
  6. 5 Continuous Random Variables
    1. Introduction
    2. 5.1 Properties of Continuous Probability Density Functions
    3. 5.2 The Uniform Distribution
    4. 5.3 The Exponential Distribution
    5. Key Terms
    6. Chapter Review
    7. Formula Review
    8. Practice
    9. Homework
    10. References
    11. 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 Estimating the Binomial with the Normal Distribution
    5. Key Terms
    6. Chapter Review
    7. Formula Review
    8. Practice
    9. Homework
    10. References
    11. Solutions
  8. 7 The Central Limit Theorem
    1. Introduction
    2. 7.1 The Central Limit Theorem for Sample Means
    3. 7.2 Using the Central Limit Theorem
    4. 7.3 The Central Limit Theorem for Proportions
    5. 7.4 Finite Population Correction Factor
    6. Key Terms
    7. Chapter Review
    8. Formula Review
    9. Practice
    10. Homework
    11. References
    12. Solutions
  9. 8 Confidence Intervals
    1. Introduction
    2. 8.1 A Confidence Interval for a Population Standard Deviation, Known or Large Sample Size
    3. 8.2 A Confidence Interval for a Population Standard Deviation Unknown, Small Sample Case
    4. 8.3 A Confidence Interval for A Population Proportion
    5. 8.4 Calculating the Sample Size n: Continuous and Binary Random Variables
    6. Key Terms
    7. Chapter Review
    8. Formula Review
    9. Practice
    10. Homework
    11. References
    12. 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 Full Hypothesis Test Examples
    6. Key Terms
    7. Chapter Review
    8. Formula Review
    9. Practice
    10. Homework
    11. References
    12. Solutions
  11. 10 Hypothesis Testing with Two Samples
    1. Introduction
    2. 10.1 Comparing Two Independent Population Means
    3. 10.2 Cohen's Standards for Small, Medium, and Large Effect Sizes
    4. 10.3 Test for Differences in Means: Assuming Equal Population Variances
    5. 10.4 Comparing Two Independent Population Proportions
    6. 10.5 Two Population Means with Known Standard Deviations
    7. 10.6 Matched or Paired Samples
    8. Key Terms
    9. Chapter Review
    10. Formula Review
    11. Practice
    12. Homework
    13. Bringing It Together: Homework
    14. References
    15. Solutions
  12. 11 The Chi-Square Distribution
    1. Introduction
    2. 11.1 Facts About the Chi-Square Distribution
    3. 11.2 Test of a Single Variance
    4. 11.3 Goodness-of-Fit Test
    5. 11.4 Test of Independence
    6. 11.5 Test for Homogeneity
    7. 11.6 Comparison of the Chi-Square Tests
    8. Key Terms
    9. Chapter Review
    10. Formula Review
    11. Practice
    12. Homework
    13. Bringing It Together: Homework
    14. References
    15. Solutions
  13. 12 F Distribution and One-Way ANOVA
    1. Introduction
    2. 12.1 Test of Two Variances
    3. 12.2 One-Way ANOVA
    4. 12.3 The F Distribution and the F-Ratio
    5. 12.4 Facts About the F Distribution
    6. Key Terms
    7. Chapter Review
    8. Formula Review
    9. Practice
    10. Homework
    11. References
    12. Solutions
  14. 13 Linear Regression and Correlation
    1. Introduction
    2. 13.1 The Correlation Coefficient r
    3. 13.2 Testing the Significance of the Correlation Coefficient
    4. 13.3 Linear Equations
    5. 13.4 The Regression Equation
    6. 13.5 Interpretation of Regression Coefficients: Elasticity and Logarithmic Transformation
    7. 13.6 Predicting with a Regression Equation
    8. 13.7 How to Use Microsoft Excel® for Regression Analysis
    9. Key Terms
    10. Chapter Review
    11. Practice
    12. Solutions
  15. A | Statistical Tables
  16. B | Mathematical Phrases, Symbols, and Formulas
  17. Index

5.1 Properties of Continuous Probability Density Functions

1.

Which type of distribution does the graph illustrate?

The horizontal axis ranges from 0 to 10. The distribution is modeled by a rectangle extending from x = 3 to x =8.
Figure 5.23
2.

Which type of distribution does the graph illustrate?

This graph slopes downward. It begins at a point on the y-axis and approaches the x-axis at the right edge of the graph.
Figure 5.24
3.

Which type of distribution does the graph illustrate?

This graph shows a bell-shaped graph. The symmetric graph reaches maximum height at x = 0 and slopes downward gradually to the x-axis on each side of the peak.
Figure 5.25
4.

What does the shaded area represent? P(___< x < ___)

This graph shows a uniform distribution. The horizontal axis ranges from 0 to 10. The distribution is modeled by a rectangle extending from x = 1 to x = 8. A region from x = 2 to x = 5 is shaded inside the rectangle.
Figure 5.26
5.

What does the shaded area represent? P(___< x < ___)

This graph shows an exponential distribution. The graph slopes downward. It begins at a point on the y-axis and approaches the x-axis at the right edge of the graph. The region under the graph from x = 6 to x = 7 is shaded.
Figure 5.27
6.

For a continuous probability distribution, 0 ≤ x ≤ 15. What is P(x > 15)?

7.

What is the area under f(x) if the function is a continuous probability density function?

8.

For a continuous probability distribution, 0 ≤ x ≤ 10. What is P(x = 7)?

9.

A continuous probability function is restricted to the portion between x = 0 and 7. What is P(x = 10)?

10.

f(x) for a continuous probability function is 1 5 1 5 , and the function is restricted to 0 ≤ x ≤ 5. What is P(x < 0)?

11.

f(x), a continuous probability function, is equal to 1 12 1 12 , and the function is restricted to 0 ≤ x ≤ 12. What is P (0 < x < 12)?

12.

Find the probability that x falls in the shaded area.

This shows the graph of the function f(x) = 1 9, the pdf for a uniform distribution. A horizontal line ranges from the point (0, 1/9) to the point (9, 1/9). A vertical line extends from the x-axis to the graph at x = 9 creating a rectangle with the coordinate axes on two sides. A region is shaded inside the rectangle from x = 6 to x = 8.
Figure 5.28
13.

Find the probability that x falls in the shaded area.

Figure 5.29
14.

Find the probability that x falls in the shaded area.

Figure 5.30
15.

f(x), a continuous probability function, is equal to 1 3 1 3 and the function is restricted to 1 ≤ x ≤ 4. Describe P( x> 3 2 ). P( x> 3 2 ).

5.2 The Uniform Distribution

Use the following information to answer the next ten questions. The data that follow are the square footage (in 1,000 feet squared) of 28 homes.

1.5 2.4 3.6 2.6 1.6 2.4 2.0
3.5 2.5 1.8 2.4 2.5 3.5 4.0
2.6 1.6 2.2 1.8 3.8 2.5 1.5
2.8 1.8 4.5 1.9 1.9 3.1 1.6
Table 5.2

The sample mean = 2.50 and the sample standard deviation = 0.8302.

The distribution can be written as X ~ U(1.5, 4.5).

16.

What type of distribution is this?

17.

In this distribution, outcomes are equally likely. What does this mean?

18.

What is the height of f(x) for the continuous probability distribution?

19.

What are the constraints for the values of x?

20.

Graph P(2 < x < 3).

21.

What is P(2 < x < 3)?

22.

What is P(x < 3.5 || x < 4)?

23.

What is P(x = 1.5)?

24.

Find the probability that a randomly selected home has more than 3,000 square feet given that you already know the house has more than 2,000 square feet.


Use the following information to answer the next eight exercises. A distribution is given as X ~ U(0, 12).

25.

What is a? What does it represent?

26.

What is b? What does it represent?

27.

What is the probability density function?

28.

What is the theoretical mean?

29.

What is the theoretical standard deviation?

30.

Draw the graph of the distribution for P(x > 9).

31.

Find P(x > 9).


Use the following information to answer the next eleven exercises. The age of cars in the staff parking lot of a suburban college is uniformly distributed from six months (0.5 years) to 9.5 years.

32.

What is being measured here?

33.

In words, define the random variable X.

34.

Are the data discrete or continuous?

35.

The interval of values for x is ______.

36.

The distribution for X is ______.

37.

Write the probability density function.

38.

Graph the probability distribution.

  1. Sketch the graph of the probability distribution.
    This is a blank graph template. The vertical and horizontal axes are unlabeled.
    Figure 5.31
  2. Identify the following values:
    1. Lowest value for x – x – : _______
    2. Highest value for x – x – : _______
    3. Height of the rectangle: _______
    4. Label for x-axis (words): _______
    5. Label for y-axis (words): _______
39.

Find the average age of the cars in the lot.

40.

Find the probability that a randomly chosen car in the lot was less than four years old.

  1. Sketch the graph, and shade the area of interest.
    Blank graph with vertical and horizontal axes.
    Figure 5.32
  2. Find the probability. P(x < 4) = _______
41.

Considering only the cars less than 7.5 years old, find the probability that a randomly chosen car in the lot was less than four years old.

  1. Sketch the graph, shade the area of interest.
    This is a blank graph template. The vertical and horizontal axes are unlabeled.
    Figure 5.33
  2. Find the probability. P(x < 4 || x < 7.5) = _______
42.

What has changed in the previous two problems that made the solutions different?

43.

Find the third quartile of ages of cars in the lot. This means you will have to find the value such that 3 4 3 4 , or 75%, of the cars are at most (less than or equal to) that age.

  1. Sketch the graph, and shade the area of interest.
    Blank graph with vertical and horizontal axes.
    Figure 5.34
  2. Find the value k such that P(x < k) = 0.75.
  3. The third quartile is _______

5.3 The Exponential Distribution

Use the following information to answer the next ten exercises. A customer service representative must spend different amounts of time with each customer to resolve various concerns. The amount of time spent with each customer can be modeled by the following distribution: X ~ Exp(0.2)

44.

What type of distribution is this?

45.

Are outcomes equally likely in this distribution? Why or why not?

46.

What is m? What does it represent?

47.

What is the mean?

48.

What is the standard deviation?

49.

State the probability density function.

50.

Graph the distribution.

51.

Find P(2 < x < 10).

52.

Find P(x > 6).

53.

Find the 70th percentile.


Use the following information to answer the next seven exercises. A distribution is given as X ~ Exp(0.75).

54.

What is m?

55.

What is the probability density function?

56.

What is the cumulative distribution function?

57.

Draw the distribution.

58.

Find P(x < 4).

59.

Find the 30th percentile.

60.

Find the median.

61.

Which is larger, the mean or the median?

Use the following information to answer the next 16 exercises. Carbon-14 is a radioactive element with a half-life of about 5,730 years. Carbon-14 is said to decay exponentially. The decay rate is 0.000121. We start with one gram of carbon-14. We are interested in the time (years) it takes to decay carbon-14.

62.

What is being measured here?

63.

Are the data discrete or continuous?

64.

In words, define the random variable X.

65.

What is the decay rate (m)?

66.

The distribution for X is ______.

67.

Find the amount (percent of one gram) of carbon-14 lasting less than 5,730 years. This means, find P(x < 5,730).

  1. Sketch the graph, and shade the area of interest.
    This is a blank graph template. The vertical and horizontal axes are unlabeled.
    Figure 5.35
  2. Find the probability. P(x < 5,730) = __________
68.

Find the percentage of carbon-14 lasting longer than 10,000 years.

  1. Sketch the graph, and shade the area of interest.
    Blank graph with horizontal and vertical axes.
    Figure 5.36
  2. Find the probability. P(x > 10,000) = ________
69.

Thirty percent (30%) of carbon-14 will decay within how many years?

  1. Sketch the graph, and shade the area of interest.
    This is a blank graph template. The vertical and horizontal axes are unlabeled.
    Figure 5.37
  2. Find the value k such that P(x < k) = 0.30.
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