This is completed downloadable of Statistics for business and economics 13th edition mcclaveSolutions Manual
Product Details:
- ISBN-10 : 0134506596
- ISBN-13 : 978-0134506593
- 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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