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Statistics for Business and Economics 13th Edition McClave Solutions Manual

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This is completed downloadable of Statistics for business and economics 13th edition mcclaveSolutions Manual

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ISBN-10 ‏ : ‎ 0134506596
ISBN-13 ‏ : ‎ 978-0134506593
Author: James McClave (Author), P. George Benson (Author), Terry Sincich (Author)

Now in its Thirteenth Edition, Statistics for Business and Economics introduces statistics in the context of contemporary business. Emphasizing statistical literacy in thinking, the text applies its concepts with real data and uses technology to develop a deeper conceptual understanding. Examples, activities, and case studies foster active learning while emphasizing intuitive concepts of probability and teaching readers to make informed business decisions. The Thirteenth Edition continues to highlight the importance of ethical behavior in collecting, interpreting, and reporting on data, while also providing a wealth of new and updated exercises and case studies.

Table of Content:

1 Statistics, Data, and Statistical Thinking
Contents
Where We’re Going
1.1 The Science of Statistics
1.2 Types of Statistical Applications in Business
1.3 Fundamental Elements of Statistics
Key Elements of a Statistical Problem—Ages of TV Viewers
Problem
Solution
Look Back
Key Elements of a Statistical Problem—Pepsi vs. Coca-Cola
Problem
Solution
Look Back
Reliability of an Inference—Pepsi vs. Coca-Cola
Problem
Solution
Look Back
1.4 Processes (Optional)
Key Elements of a Process—Waiting Time at a Fast-Food Window
Problem
Solution
Look Back
1.5 Types of Data
Types of Data—Study of a River Contaminated by a Chemical Plant
Problem
Solution
Look Back
1.6 Collecting Data: Sampling and Related Issues
Generating a Simple Random Sample—Selecting Households for a Feasibility Study
Problem
Solution
Look Back
Randomization in a Designed Experiment— A Clinical Trial
Problem
Solution
Method of Data Collection—Survey of Online Shoppers
Problem
Solution
Look Back
Representative Data—Price Promotion Study
Problem
Solution
Look Back
1.7 Business Analytics: Critical Thinking with Statistics
Biased Sample—Motorcycle Helmet Law
Problem
Solution
Look Back
Manipulative or Ambiguous Survey Questions—Satellite Radio Survey
Problem
Solution
Look Back
Chapter Notes
Key Terms
Key Ideas
Types of Statistical Applications
Descriptive
Inferential
Types of Data
Data-Collection Methods
Types of Random Samples
Problems with Nonrandom Samples
Exercises 1.1–1.40
Learning the Mechanics
Applet Exercise 1.1
Applet Exercise 1.2
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
Critical Thinking Challenge
Activity 1.1 Keep the Change: Collecting Data
Activity 1.2 Identifying Misleading Statistics
References
2 Methods for Describing Sets of Data
Contents
Where We’ve Been
Where We’re Going
2.1 Describing Qualitative Data
Graphing and Summarizing Qualitative Data—Blood Loss Study
Problem
Solution
Look Back
Exercises 2.1–2.17
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
2.2 Graphical Methods for Describing Quantitative Data
Dot Plots
Stem-and-Leaf Display
Histograms
Graphs for a Quantitative Variable—Lost Price Quotes
Problem
Solution
Look Back
Exercises 2.18–2.34
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
2.3 Numerical Measures of Central Tendency
Calculating the Sample Mean
Problem
Solution
Look Back
Finding the Mean on a Printout—R&D Expenditures
Problem
Solution
Look Back
Computing the Median
Problem
Solution
Look Back
Finding the Median on a Printout—R&D Expenditures
Problem
Solution
Look Back
Finding the Mode
Problem
Solution
Look Back
Comparing the Mean, Median, and Mode—CEO Salaries
Problem
Solution
Look Back
Exercises 2.35–2.55
Learning the Mechanics
Applet Exercise 2.1
Applet Exercise 2.2
Applet Exercise 2.3
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
2.4 Numerical Measures of Variability
Computing Measures of Variation
Problem
Solution
Look Ahead
Finding Measures of Variation on a Printout—R&D Expenditures
Problem
Solution
Exercises 2.56–2.70
Learning the Mechanics
Applet Exercise 2.4
Applet Exercise 2.5
Applet Exercise 2.6
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
2.5 Using the Mean and Standard Deviation to Describe Data
Interpreting the Standard Deviation—R&D Expenditures
Problem
Solution
Look Back
Check on the Calculation of s—R&D Expenditures
Problem
Solution
Look Ahead
Making a Statistical Inference—Car Battery Guarantee
Problem
Solution
Look Back
Exercises 2.71–2.89
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
2.6 Numerical Measures of Relative Standing
Finding and Interpreting Percentiles—R&D Expenditures
Problem
Solution
Look Back
Finding a z-Score—GMAT Results
Problem
Solution
Look Back
Exercises 2.90–2.105
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
2.7 Methods for Detecting Outliers: Box Plots and z-Scores
Interpreting a Box Plot—Lost Price Quotes
Problem
Solution
Look Back
Comparing Box Plots—Lost Price Quotes
Problem
Solution
Look Back
Inference Using z-Scores—Salary Discrimination
Problem
Solution
Look Back
Exercises 2.106–2.121
Learning the Mechanics
Applet Exercise 2.7
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
2.8 Graphing Bivariate Relationships (Optional)
Graphing Bivariate Data—Hospital Length of Stay
Problem
Solution
Look Back
Exercises 2.122–2.134
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
2.9 The Time Series Plot (Optional)
Time Series Plot vs. a Histogram—Deming’s Example
Problem
Solution
Look Back
2.10 Distorting the Truth with Descriptive Techniques
Graphical Distortions
Misleading Numerical Descriptive Statistics
Misleading Descriptive Statistics—Your Average Salary
Problem
Solution
Look Back
More Misleading Descriptive Statistics—Delinquent Children
Problem
Solution
Look Back
Exercises 2.135–2.138
Applying the Concepts—Intermediate
Chapter Notes
Key Terms
Key Symbols
Key Ideas
Describing Qualitative Data
Graphing Quantitative Data
One Variable
Two Variables
Guide to Selecting the Data Description Method
Supplementary Exercises 2.139–2.171
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
Critical Thinking Challenges
Activity 2.1 Real Estate Sales
Activity 2.2 Keep the Change: Measures of Central Tendency and Variability
References
3 Probability
Contents
Where We’ve Been
Where We’re Going
3.1 Events, Sample Spaces, and Probability
Listing the Sample Points for a Coin–Tossing Experiment
Problem
Solution
Look Back
Sample Point Probabilities—Hotel Water Conservation
Problem
Solution
Look Back
Probability of a Collection of Sample Points—Die-Tossing Experiment
Problem
Solution
Look Back
The Probability of a Compound Event—Defective Smartphones
Problem
Solution
Look Back
Applying the Five Steps to Find a Probability—Diversity Training
Problem
Solution
Look Back
Another Compound Event Probability—Investing in a Successful Venture
Problem
Solution
Using the Combinations Rule—Selecting 2 Investments from 4
Problem
Solution
Look Back
Using the Combinations Rule—Selecting 5 Investments from 20
Problem
Solution
Look Back
Exercises 3.1–3.29
Learning the Mechanics
Applet Exercise 3.1
Applet Exercise 3.2
Applying the Concepts–Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
3.2 Unions and Intersections
Probabilities of Unions and Intersections—Die-Toss Experiment
Problem
Solution
Look Back
Finding Probabilities in a Two-Way Table—Income vs. Age
Problem
Solution
Look Back
3.3 Complementary Events
Probability of a Complementary Event—Coin-Toss Experiment
Problem
Solution
Look Back
Look Forward
3.4 The Additive Rule and Mutually Exclusive Events
Applying the Additive Rule—Hospital Admission Study
Problem
Solution
Look Back
The Union of Two Mutually Exclusive Events—Coin-Tossing Experiment
Problem
Solution
Look Back
Exercises 3.30–3.51
Learning the Mechanics
Applet Exercise 3.3
Applet Exercise 3.4
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
3.5 Conditional Probability
The Conditional Probability Formula—Executives Who Cheat at Golf
Problem
Solution
Look Back
Applying the Conditional Probability Formula to a Two-Way Table—Customer Desire to Buy
Problem
Solution
Look Back
Applying the Conditional Probability Formula to a Two-Way Table—Customer Complaints
Problem
Solution
Look Back
3.6 The Multiplicative Rule and Independent Events
Applying the Multiplicative Rule—Wheat Futures
Problem
Solution
Look Back
Applying the Multiplicative Rule—Study of Welfare Workers
Problem
Solution
Look Back
Checking for Independence—Die-Tossing Experiment
Problem
Solution
Look Back
Checking for Independence—Consumer Product Complaint Study
Problem
Solution
Probability of Independent Events Occurring Simultaneously—Diversity Training Study
Problem
Solution
Look Back
Exercises 3.52–3.80
Learning the Mechanics
Applet Exercise 3.5
Applying the Concepts—Basic
Applying the Concepts–Intermediate
Applying the Concepts—Advanced
3.7 Bayes’s Rule
Applying Bayes’s Logic—Intruder Detection System
Problem
Solution
Look Back
Bayes’s Rule Application—Wheelchair Control
Problem
Solution
Exercises 3.81–3.93
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
Chapter Notes
Key Terms
Key Symbols
Key Ideas
Combinations Rule
Bayes’s Rule
Guide to Selecting Probability Rules
Supplementary Exercises 3.94 – 3.130
Learning the Mechanics
Applet Exercise 3.6
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
Critical Thinking Challenges
Activity 3.1 Exit Polls: Conditional Probability
Activity 3.2 Keep the Change: Independent Events
References
4 Random Variables and Probability Distributions
Contents
Where We’ve Been
Where We’re Going
4.1 Two Types of Random Variables
Values of a Discrete Random Variable—Wine Ratings
Problem
Solution
Look Back
Values of a Discrete Random Variable—EPA Application
Problem
Solution
Look Back
Values of a Continuous Random Variable—Another EPA Application
Problem
Solution
Look Ahead
Exercises 4.1–4.10
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Part I: Discrete Random Variables
4.2 Probability Distributions for Discrete Random Variables
Finding a Probability Distribution— Coin-Tossing Experiment
Problem
Solution
Look Ahead
Probability Distribution from a Graph—Playing Craps
Problem
Solution
Look Back
Probability Distribution Using a Formula—Texas Droughts
Problem
Solution
Look Back
Finding an Expected Value—An Insurance Application
Problem
Solution
Look Back
Finding μ and σ — Internet Business Venture
Problem
Solution
Exercises 4.11–4.39
Learning the Mechanics
Applet Exercise 4.1
Applet Exercise 4.2
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
4.3 The Binomial Distribution
Assessing Whether x Is Binomial—Business Problems
Problem
Solution
Look Back
Deriving the Binomial Probability Distribution in a Purchase Application
Problem
Solution
Look Ahead
Applying the Binomial Distribution—Manufacture of Automobiles
Problem
Solution
Look Back
Finding μ and σ —Automobile Manufacturing Application
Problem
Solution
Look Back
Using Tables and Software to Find Binomial Probabilities
Using the Binomial Table and Computer Software—Worker Unionization Problem
Problem
Solution
Exercises 4.40–4.60
Learning the Mechanics
Applet Exercise 4.3
Applet Exercise 4.4
Applet Exercise 4.5
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts–Advanced
4.4 Other Discrete Distributions: Poisson and Hypergeometric
Poisson Random Variable
Finding Poisson Probabilities—Worker Absenteeism
Problem
Solution
Look Back
Hypergeometric Random Variable
Applying the Hypergeometric Distribution—Selecting Teaching Assistants
Problem
Solution
Look Back
Exercises 4.61– 4.83
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
Part II: Continuous Random Variables
4.5 Probability Distributions for Continuous Random Variables
4.6 The Normal Distribution
Using the Standard Normal Table to Find
Problem
Solution
Look Back
Using the Standard Normal Table to Find Tail Probabilities
Problem
Solution
Look Back
Finding the Probability of a Normal Random Variable–Cell Phone Application
Problem
Solution
Using Normal Probabilities to Make an Inference–Advertised Gas Mileage
Problem
Solution
Look Back
Finding a z-value Associated with a Normal Probability
Problem
Solution
Look Back
Finding a Value of a Normal Random Variable–Paint Manufacturing Application
Problem
Solution
Look Back
Applying the Normal Approximation to a Binomial Probability—Lot Acceptance Sampling
Problem
Solution
Look Back
Exercises 4.84 – 4.116
Learning the Mechanics
Applet Exercise 4.6
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
4.7 Descriptive Methods for Assessing Normality
Checking for Normal Data—EPA Estimated Gas Mileages
Problem
Solution
Look Back
Exercises 4.117–4.131
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
4.8 Other Continuous Distributions: Uniform and Exponential
Uniform Random Variable
Applying the Uniform Distribution—Steel Manufacturing
Problem
Solution
Look Back
Exponential Random Variable
Finding an Exponential Probability—Hospital Emergency Arrivals
Problem
Solution
Look Back
The Mean and Variance of an Exponential Random Variable—Length of Life of a Microwave Oven
Problem
Solution
Look Back
Exercises 4.132–4.155
Learning the Mechanics
Applet Exercise 4.7
Applet Exercise 4.8
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
Chapter Notes
Key Terms
Key Symbols
Key Ideas
Properties of Probability Distributions
Discrete Distributions
Continuous Distributions
Normal Approximation to Binomial
Methods for Assessing Normality
Key Formulas
Guide to Selecting a Probability Distribution
Supplementary Exercises 4.156 – 4.206
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
Critical Thinking Challenges
Activity 4.1 Warehouse Club Memberships: Exploring a Binomial Random Variable
Activity 4.2 Identifying the Type of Probability Distribution
References
5 Sampling Distributions
Contents
Where We’ve Been
Where We’re Going
5.1 The Concept of a Sampling Distribution
Problem
Solution
Problem
Solution
Look Back
Problem
Solution
Look Back
Exercises 5.1–5.7
Learning the Mechanics
5.2 Properties of Sampling Distributions: Unbiasedness and Minimum Variance
Problem
Solution
Look Back
Problem
Solution
Problem
Solution
Look Back
Exercises 5.8–5.14
Learning the Mechanics
5.3 The Sampling Distribution of the Sample Mean and the Central Limit Theorem
Problem
Solution
Look Back
Problem
Solution
Look Back
Problem
Solution
Look Back
Exercises 5.15–5.35
Learning the Mechanics
Applet Exercise 5.1
Applet Exercise 5.2
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
5.4 The Sampling Distribution of the Sample Proportion
Problem
Solution
Look Back
Problem
Solution
Exercises 5.36–5.51
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Chapter Notes
Key Terms
Key Formulas
Key Ideas
Key Symbols
Generating the Sampling Distribution of
Supplementary Exercises 5.52–5.77
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
Critical Thinking Challenges
Activity 5.1 Simulating a Sampling Distribution—Cell Phone Usage
References
Making Business Decisions [Chapters 3–5]
6 Inferences Based on a Single Sample Estimation with Confidence Intervals
Contents
Where We’ve Been
Where We’re Going
6.1 Identifying and Estimating the Target Parameter
6.2 Confidence Interval for a Population Mean: Normal (z) Statistic
Estimating the Mean, σ Known—Delinquent Debtors
Problem
Solution
Look Back
Estimating the Mean, σ Unknown–Delinquent Debtors
Problem
Solution
Look Back
Large-Sample Confidence Interval for μ —Unoccupied Seats per Flight
Problem
Solution
Look Back
Exercises 6.1–6.22
Learning the Mechanics
Applet Exercise 6.1
Applet Exercise 6.2
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
6.3 Confidence Interval for a Population Mean: Student’s t-Statistic
Problem 1
Solution to Problem 1
Problem 2
Solution to Problem 2
A Confidence Interval for μ Using the t-statistic–Blood Pressure Drug
Problem
Solution
Look Back
A Small-Sample Confidence Interval for μ —Destructive Sampling
Problem
Solution
Look Back
Exercises 6.23–6.39
Learning the Mechanics
Applet Exercise 6.3
Applet Exercise 6.4
Applying the Concepts—Basic
Applying the Concepts—Intermediate
6.4 Large-Sample Confidence Interval for a Population Proportion
Estimating a Population Proportion–Preference for Breakfast Cereal
Problem
Solution
Look Back
Large-Sample Confidence Interval for p—Proportion Optimistic about the Economy
Problem
Solution
Look Back
Adjusted Confidence Interval Procedure for p—Injury Rate at a Jewelry Store
Problem
Solution
Look Back
Exercises 6.40–6.59
Learning the Mechanics
Applet Exercise 6.5
Applet Exercise 6.6
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
6.5 Determining the Sample Size
Estimating a Population Mean
Sample Size for Estimating μ —Mean Inflation Pressure of Footballs
Problem
Solution
Look Back
Estimating a Population Proportion
Sample Size for Estimating p—Fraction of Defective Cell Phones
Problem
Solution
Look Back
Exercises 6.60–6.79
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
6.6 Finite Population Correction for Simple Random Sampling (Optional)
Applying the Finite Population Correction Factor—Manufacture of Sheet Aluminum Foil
Problem
Solution
Look Back
Exercises 6.80–6.92
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
6.7 Confidence Interval for a Population Variance (Optional)
Estimating σ2 —Weight Variance of Contaminated Fish
Problem
Solution
Look Ahead
Estimating σ —Weight Standard Deviation of Contaminated Fish
Problem
Solution
Look Back
Exercises 6.93–6.105
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Chapter Notes
Key Terms
Key Symbols
Key Ideas/Formulas
Population Parameters, Estimators, and Standard Errors
Key Words for Identifying the Target Parameter
Commonly Used z-Values for a Large-Sample Confidence Interval
Determining the Sample Size n
*Finite Population Correction Factor (use when )
Illustrating the Notion of “95% Confidence”
Guide to Forming a Confidence Interval
Supplementary Exercises 6.106 –6.137
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
Critical Thinking Challenges
Activity 6.1 Conducting a Pilot Study
References
7 Inferences Based on a Single Sample Tests of Hypotheses
Contents
Where We’ve Been
Where We’re Going
7.1 The Elements of a Test of Hypothesis
7.2 Formulating Hypotheses and Setting Up the Rejection Region
Formulating H0 and Ha for a Test of a Population Mean—Quality Control
Problem
Solution
Look Back
Formulating H0 and Ha for a Test of a Population Proportion—Cigarette Advertisements
Problem
Solution
Look Back
Setting Up a Hypothesis Test for μ —Mean Amount of Cereal in a Box
Problem
Solution
Look Back
7.3 Observed Significance Levels: p-Values
Comparing Rejection Regions to p-Values
Problem
Solution
Look Ahead
Exercises 7.19–7.27
Learning the Mechanics
7.4 Test of Hypothesis About a Population Mean: Normal (z) Statistic
Carrying Out a Hypothesis Test for μ–Mean Amount of Cereal in a Box
Problem
Solution
Look Back
Using p-Values—Test of Mean Filling Weight
Problem
Solution
Look Back
Using p-Values—Test of Mean Hospital Length of Stay
Problem
Solution
Look Back
7.5 Test of Hypothesis About a Population Mean: Student’s t-Statistic
Small-Sample Test for μ —Does a New Engine Meet Air Pollution Standards?
Problem
Solution
Look Back
The p-Value for a Small-Sample Test of μ
Problem
Solution
Exercises 7.47–7.63
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
7.6 Large-Sample Test of Hypothesis About a Population Proportion
Hypothesis Test for p—Proportion of Defective Batteries
Problem
Solution
The p-Value for a Test About a Population Proportion p
Problem
Solution
Look Back
Small Samples
7.7 Test of Hypothesis About a Population Variance
Test for σ2 —Fill Weight Variance
Problem
Solution
Look Back
Exercises 7.82–7.95
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
7.8 Calculating Type II Error Probabilities: More About β (Optional)
The Power of a Test—Quality-Control Study
Problem
Solution
Look Back
Exercises 7.96–7.107
Learning the Mechanics
Applying the Concepts—Intermediate
Chapter Notes
Key Terms
Key Symbols
Key Ideas
Key Words for Identifying the Target Parameter
Elements of a Hypothesis Test
Probabilities in Hypothesis Testing
Forms of Alternative Hypothesis
Using p-Values to Make Conclusions
Guide to Selecting a One-Sample Hypothesis Test
Supplementary Exercises 7.108–7.147
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
Critical Thinking Challenge
Activity 7.1 Challenging a Company’s Claim: Tests of Hypotheses
Activity 7.2 Keep the Change: Tests of Hypotheses
References
8 Inferences Based on Two Samples Confidence Intervals and Tests of Hypotheses
Contents
Where We’ve Been
Where We’re Going
8.1 Identifying the Target Parameter
8.2 Comparing Two Population Means: Independent Sampling
Large Samples
Large-Sample Confidence Interval for —Comparing Mean Car Prices
Problem
Solution
Look Back
Large-Sample Test for —Comparing Mean Car Prices
Problem
Solution
Look Back
The p-Value of a Test for
Problem
Solution
Look Back
Small Samples
Small-Sample Confidence Interval for ()—Managerial Success
Problem
Solution
Look Back
Exercises 8.1–8.25
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
8.3 Comparing Two Population Means: Paired Difference Experiments
Confidence Interval for μd —Comparing Mean Salaries of Males and Females
Problem
Solution
Look Back
Exercises 8.26–8.42
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
8.4 Comparing Two Population Proportions: Independent Sampling
Large-Sample Test About —Comparing Car Repair Rates
Problem
Solution
Look Back
Finding the Observed Significance Level of a Test for
Problem
Solution
Exercises 8.43–8.60
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
8.5 Determining the Required Sample Size
Finding the Sample Sizes for Estimating —Comparing Mean Crop Yields
Problem
Solution
Look Back
Finding the Sample Sizes for Estimating —Comparing Defect Rates of Two Machines
Problem
Solution
Look Back
Exercises 8.61–8.72
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
8.6 Comparing Two Population Variances: Independent Sampling
An F-Test Application—Comparing Paper Mill Production Variation
Problem
Solution
Look Back
The Observed Significance Level of an F-Test
Problem
Solution
Look Back
Checking the Assumption of Equal Variances
Problem
Solution
Look Back
Exercises 8.73–8.88
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Chapter Notes
Key Terms
Key Symbols
Key Ideas
Key Words for Identifying the Target Parameter
Determining the Sample Size
Conditions Required for Inferences About
Large samples:
Small samples:
Conditions Required for Inferences About
Large or small samples:
Conditions Required for Inferences About μd
Large samples:
Small samples:
Conditions Required for Inferences About
Large samples:
Using a Confidence Interval for or to Determine Whether a Difference Exists
Guide to Selecting a Two-Sample Hypothesis Test and Confidence Interval
Supplementary Exercises 8.89–8.123
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
Critical Thinking Challenges
Activity 8.1 Box Office Receipts: Comparing Population Means
Activity 8.2 Keep the Change: Inferences Based on Two Samples
References
Making Business Decisions [Chapters 6–8] [Part II] The Kentucky Milk Case
9 Design of Experiments and Analysis of Variance
Contents
Where We’ve Been
Where We’re Going
9.1 Elements of a Designed Experiment
Key Elements of a Designed Experiment—Testing Golf Ball Brands
Problem
Solution
Look Back
A Two-Factor Experiment—Testing Golf Ball Brands
Problem
Solution
Look Back
Exercises 9.1–9.14
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
9.2 The Completely Randomized Design: Single Factor
Assigning Treatments in a Completely Randomized Design—Bottled Water Brands Study
Problem
Solution
Look Back
Conducting an ANOVA F-Test—Comparing Golf Ball Brands
Problem
Solution
Look Ahead
Checking the ANOVA Assumptions
Problem
Solution
Exercises 9.15–9.34
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
9.3 Multiple Comparisons of Means
Ranking Treatment Means—Golf Ball Experiment
Problem
Solution
Look Back
Exercises 9.35–9.49
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
9.4 The Randomized Block Design
Experimental Design Principles
Problem
Solution
Randomized Block Design—Comparing Golf Ball Brands
Problem
Solution
Look Back
Ranking Treatment Means in a Randomized Block Design—Golf Ball Study
Problem
Solution
Exercises 9.50–9.63
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
9.5 Factorial Experiments: Two Factors
Conducting a Factorial ANOVA—Golf Ball Study
Problem
Solution
Look Back
More Practice on Conducting a Factorial Analysis—Golf Ball Study
Problem
Solution
Test for Equality of Treatment Means
Test for Interaction
Test for Brand Main Effect
Test for Club Main Effect
Ranking of Means
Look Back
Exercises 9.64–9.81
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
Chapter Notes
Key Terms
Key Symbols/Notation
Key Ideas
Key Elements of a Designed Experiment
Balanced design
Tests for main effects in a factorial design
Robust method
Conditions Required for Valid F-Test in a Completely Randomized Design
Conditions Required for Valid F-Test in a Randomized Block Design
Conditions Required for Valid F-Tests in a Complete Factorial Design
Multiple Comparisons of Means Methods
Tukey method:
Bonferroni method:
Scheffé method:
Experimentwise error rate (EER)
Guide to Selecting the Experimental Design
Guide to Conducting Anova F-Tests
Supplementary Exercises 9.82–9.109
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
Critical Thinking Challenge
Activity 9.1 Designed vs. Observational Experiments
References
10 Categorical Data Analysis
Contents
Where We’ve Been
Where We’re Going
10.1 Categorical Data and the Multinomial Experiment
Identifying a Multinomial Experiment
Problem
Solution
10.2 Testing Category Probabilities: One-Way Table
A One-Way χ2 Test—Evaluating a Firm’s Merit-Increase Plan
Problem
Solution
Look Back
Exercises 10.1–10.18
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
10.3 Testing Category Probabilities: Two-Way (Contingency) Table
Conducting a Two-Way Analysis—Broker Rating and Customer Income
Problem
Solution
Contingency Tables with Fixed Marginals
Exact Tests for Independence in a Contingency Table
Exact Test for a 2×2 Contingency Table—AIDS Vaccine Application
Problem
Solution
Look Back
Exercises 10.19–10.39
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
10.4 A Word of Caution About Chi-Square Tests
Chapter Notes
Key Terms
Key Symbols/Notation
Key Ideas
Multinomial data
Properties of a Multinomial Experiment
One-way table
Two-way (contingency) table
Chi-square ( χ2 ) statistic
Chi-square tests for independence
Conditions Required for Valid χ2 Tests
Categorical Data Analysis Guide
Supplementary Exercises 10.40–10.58
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
Critical Thinking Challenge
Activity 10.1 Binomial vs. Multinomial Experiments
Activity 10.2 Contingency Tables
References
Making Business Decisions [Chapters 9–10]
Discrimination in the Workplace
Part I: Downsizing at a Computer Firm
Part II: Age Discrimination—You Be the Judge
11 Simple Linear Regression
Contents
Where We’ve Been
Where We’re Going
11.1 Probabilistic Models
Modeling Job Outsourcing Level of a U.S. Company
Problem
Solution
Look Ahead
Exercises 11.1–11.13
Learning the Mechanics
Applying the Concepts—Basic
11.2 Fitting the Model: The Least Squares Approach
Applying the Method of Least Squares—Advertising-Sales Data
Problem
Solution
Look Back
Exercises 11.14–11.30
Learning the Mechanics
Applet Exercise 11.1
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
11.3 Model Assumptions
Estimating σ —Advertising-Sales Regression
Problem
Solution
Look Back
Exercises 11.31–11.44
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
11.4 Assessing the Utility of the Model: Making Inferences About the Slope β1
Testing the Regression Slope, β1 —Sales Revenue Model
Problem
Solution
Look Back
Exercises 11.45–11.63
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
11.5 The Coefficients of Correlation and Determination
Coefficient of Correlation
Using the Correlation Coefficient—Relating Crime Rate and Casino Employment
Problem
Solution
Look Back
Coefficient of Determination
Obtaining the Value of r2—Sales Revenue Model
Problem
Solution
Exercises 11.64–11.81
Learning the Mechanics
Applet Exercise 11.2
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
11.6 Using the Model for Estimation and Prediction
Estimating the Mean of y—Sales Revenue Model
Problem
Solution
Look Back
Predicting an Individual value of y—Sales Revenue Model
Problem
Solution
Look Back
Exercises 11.82–11.96
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
11.7 A Complete Example
Exercises 11.97–11.100
Applying the Concepts—Intermediate
Chapter Notes
Key Terms
Key Symbols/Notation
Key Ideas
Simple Linear Regression variables
Method of Least Squares Properties
Practical Interpretation of y-Intercept
Practical Interpretation of Slope
First-Order (Straight-Line) Model
Coefficient of Correlation, r
Coefficient of Determination, r2
Practical Interpretation of Model Standard Deviation, s
Guide to Simple Linear Regression
Supplementary Exercises 11.101–11.120
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
Critical Thinking Challenges
Activity 11.1 Applying Simple Linear Regression to Your Favorite Data
References
12 Multiple Regression and Model Building
Contents
Where We’ve Been
Where We’re Going
12.1 Multiple Regression Models
Part I: First-Order Models with Quantitative Independent Variables
12.2 Estimating and Making Inferences About the β Parameters
Problem
Solution
Look Back
Problem
Solution
Look Back
Problem
Solution
Look Back
12.3 Evaluating Overall Model Utility
Problem
Solution
Look Back
Exercises 12.1–12.24
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
12.4 Using the Model for Estimation and Prediction
Problem
Solution
Look Back
Exercises 12.25–12.34
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Part II: Model Building in Multiple Regression
12.5 Interaction Models
Problem
Solution
Look Back
Exercises 12.35–12.48
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
12.6 Quadratic and Other Higher-Order Models
Problem
Solution
Look Back
Problem
Solution
Look Back
Exercises 12.49–12.65
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
12.7 Qualitative (Dummy) Variable Models
Problem
Solution
Look Back
Exercises 12.66–12.81
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
12.8 Models with Both Quantitative and Qualitative Variables
Problem
Solution
Look Back
Problem
Solution
Look Back
Exercises 12.82–12.96
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
12.9 Comparing Nested Models
Problem
Solution
Look Back
Exercises 12.97–12.110
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
12.10 Stepwise Regression
Problem
Solution
Exercises 12.111–12.119
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Part III: Multiple Regression Diagnostics
12.11 Residual Analysis: Checking the Regression Assumptions
Checking Assumption #1: Mean
Problem
Solution
Look Back
Checking Assumption #2: Constant Error Variance
Problem
Solution
Look Back
Checking Assumption #3: Errors Normally Distributed
Problem
Solution
Look Back
Problem
Solution
Look Back
Checking Assumption #4: Errors Independent
Summary
Statistics in Action Revisited A Residual Analysis
12.12 Some Pitfalls: Estimability, Multicollinearity, and Extrapolation
Problem 1: Parameter Estimability
Problem 2: Multicollinearity
Problem
Solution
Look Back
Problem 3: Prediction Outside the Experimental Region
Exercises 12.120 –12.133
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Chapter Notes
Key Terms
Key Formulas
Key Symbols
Key Ideas
Multiple regression variables
First-order model in k quantitative x’s
Interaction model in 2 quantitative x’s
Quadratic model in 1 quantitative x
Complete second-order model in 2 quantitative x’s
Dummy variable model for 1 qualitative x
Complete second-order model in 1 quantitative x and 1 qualitative x (two levels, A and B)
Adjusted coefficient of determination,
Interaction between and
Parsimonious model
Recommendation for Assessing Model Adequacy
Recommendation for Testing Individual β ’s
Extrapolation
Nested models
Multicollinearity
Problems with Using Stepwise Regression Model as the “Final” Model
Analysis of Residuals
Guide to Multiple Regression
Supplementary Exercises 12.134 –12.174
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
Critical Thinking Challenge
Activity 12.1 Insurance Premiums: Collecting Data for Several Variables
Activity 12.2 Collecting Data and Fitting a Multiple Regression Model
References
Making Business Decisions The Condo Sales Case
13 Methods for Quality Improvement Statistical Process Control
Contents
Where We’ve Been
Where We’re Going
13.1 Quality, Processes, and Systems
Quality
Processes
Systems
13.2 Statistical Control
Models of Process Variation Patterns
Problem
Solution
Look Back
A Process “in Statistical Control”—Filling Paint Cans
Problem
Solution
Look Back
13.3 The Logic of Control Charts
Control Chart for Individual Measurements—Filling Paint Cans
Problem
Solution
Look Back
13.4 A Control Chart for Monitoring the Mean of a Process: The -Chart
Selecting Rational Subgroups
Problem
Solution
Probabilities for Pattern-Analysis Rules
Problem
Solution
Creating and Interpreting an —Paint-Filling Process
Problem
Solution
Look Back
Monitoring Future Output with an -Chart—Paint-Filling Process
Problem
Solution
Look Back
Exercises 13.1–13.22
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
13.5 A Control Chart for Monitoring the Variation of a Process: The R-Chart
Creating and Interpreting an R-Chart—Paint-Filling Process
Problem
Solution
Exercises 13.23–13.38
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
13.6 A Control Chart for Monitoring the Proportion of Defectives Generated by a Process: The p-Chart
Creating and Interpreting a p-Chart—Order Assembly Process
Problem
Solution
Exercises 13.39–13.52
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
13.7 Diagnosing the Causes of Variation
13.8 Capability Analysis
Finding and Interpreting —Paint-Filling Process
Problem
Solution
Look Back
Exercises 13.53–13.68
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Chapter Notes
Key Terms
Key Formulas
Key Symbols
Key Ideas
Total quality management (TQM)
Statistical process control (SPC)
In-control process
Out-of-control process
Dimensions of Quality
Major Sources of Process Variation
Causes of Variation
Types of Control Charts
Specification limits
Capability analysis
Capability index
Pattern-analysis rules
Rational subgroups
Sample size for p-chart:
Cause-and-effect diagram
Guide to Control Charts
Supplementary Exercises 13.69–13.93
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Critical Thinking—Challenge
Activity 13.1 Quality Control: Consistency
References
14 Time Series Descriptive Analyses, Models, and Forecasting
Contents
Where We’ve Been
Where We’re Going
14.1 Descriptive Analysis: Index Numbers
Simple Index Numbers
Composite Index Numbers
Constructing a Simple Composite Index—High-Tech Stocks
Problem
Solution
Look Back
Constructing a Laspeyres Index— High-Tech Stocks
Problem
Solution
Look Back
Constructing a Paasche Index—High-Tech Stocks
Problem
Solution
Exercises 14.1–14.14
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
14.2 Descriptive Analysis: Exponential Smoothing
Exponential Smoothing—Annual Sales Revenue
Problem
Solution
Look Back
Exercises 14.15–14.22
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
14.3 Time Series Components
14.4 Forecasting: Exponential Smoothing
Forecasting with Exponential Smoothing—Sales Revenue
Problem
Solution
Look Back
14.5 Forecasting Trends: Holt’s Method
Applying Holt’s Smoothing Method—Annual Sales Data
Problem
Solution
Look Back
Forecasting with Holt’s Method—Annual Sales
Problem
Solution
Look Back
Exercises 14.23–14.31
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
14.6 Measuring Forecast Accuracy: MAD and RMSE
Comparing Measures of Forecast Accuracy—Annual Sales
Problem
Solution
Look Back
Exercises 14.32–14.37
Applying the Concepts—Basic
Applying the Concepts—Intermediate
14.7 Forecasting Trends: Simple Linear Regression
Problem 1
Problem 2
14.8 Seasonal Regression Models
Exercises 14.38–14.47
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
14.9 Autocorrelation and the Durbin-Watson Test
Conducting the Durbin—Watson Test—Sales Revenue Model
Problem
Solution
Look Back
Exercises 14.48–14.57
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Chapter Notes
Key Terms
Key Formulas
Key Symbols
Key Ideas
Time Series Data
Index Number
Time Series Components
Time Series Forecasting
Autocorrelation
Guide to Time Series Analysis
Supplementary Exercises 14.58–14.72
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Activity 14.1 Time Series
References
15 Nonparametric Statistics
Contents
Where We’ve Been
Where We’re Going
15.1 Introduction: Distribution-Free Tests
15.2 Single Population Inferences
Sign Test Application—Failure Times of MP3 Players
Problem
Solution
Look Back
Exercises 15.1–15.14
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
15.3 Comparing Two Populations: Independent Samples
Applying the Rank Sum Test—Comparing Economists’ Predictions
Problem
Solution
Look Back
A Flawed Analysis of the Data
A Nonparametric Analysis of the Data
Exercises 15.15–15.28
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
15.4 Comparing Two Populations: Paired Difference Experiment
Applying the Signed Rank Test—Comparing Electrical Safety Ratings
Problem
Solution
Look Back
Exercises 15.29–15.42
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
15.5 Comparing Three or More Populations: Completely Randomized Design
Applying the Kruskal-Wallis Test—Comparing Available Hospital Beds
Problem
Solution
Look Back
Exercises 15.43–15.53
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
15.6 Comparing Three or More Populations: Randomized Block Design
Applying the Friedman Test—Comparing Drug Reaction Times
Problem
Solution
Look Back
Exercises 15.54–15.65
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
15.7 Rank Correlation
Spearman’s Rank Correlation—Food Preservation Study
Problem
Solution
Look Back
Exercises 15.66–15.79
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Chapter Notes
Key Terms
Key Formulas
Key Symbols
Key Ideas
Distribution-Free Tests
Nonparametrics
Guide to Selecting a Nonparametric Method
Supplementary Exercises 15.80–15.103
Learning the Mechanics
Applying the Concepts—Basic
Applying the Concepts—Intermediate
Applying the Concepts—Advanced
Critical Thinking Challenge
Activity 15.1 Keep the Change: Nonparametric Statistics
References
Making Business Decisions Chapters 10 and 15 Detecting “Sales Chasing”
The Problem and the Data
Applying Statistical Methodology
Answers to Selected Exercises
Appendix A: Summation Notation
Finding a Sum
Problem
Solution
Finding a Sum of Squares
Problem
Solution
Finding a Sum of Differences
Problem
Solution
Appendix B: Basic Counting Rules
Applying the Multiplicative Rule
Problem
Solution
Look Back
Applying the Multiplicative Rule
Problem
Solution
Applying the Partitions Rule
Problem
Solution
Applying the Combinations Rule
Problem
Solution
Applying the Permutations Rule
Problem
Solution
Appendix C: Calculation Formulas for Analysis of Variance
C.1 Formulas for the Calculations in the Completely Randomized Design
C.2 Formulas for the Calculations in the Randomized Block Design
C.3 Formulas for the Calculations for a Two-Factor Factorial Experiment
C.4 Tukey’s Multiple Comparisons Procedure (Equal Sample Sizes)
C.5 Bonferroni Multiple Comparisons Procedure (Pairwise Comparisons)
C.6 Scheffé’s Multiple Comparisons Procedure (Pairwise Comparisons)
Appendix D: Tables
Answers to Selected Exercises
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8
Chapter 9
Chapter 10
Chapter 11
Chapter 12
Chapter 13
Chapter 14
Chapter 15
Index
Photo Credits
Selected Formulas

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