For courses in Management Science or Decision Modeling
A solid foundation in quantitative methods and management science
This popular text gives students a genuine foundation in business analytics, quantitative methods, and management science—and how to apply the concepts and techniques in the real world—through a strong emphasis on model building, computer applications, and examples. The authors’ approach presents mathematical models, with all of the necessary assumptions, in clear, plain English, and then applies the ensuing solution procedures to example problems along with step-by-step, how-to instructions. In instances in which the mathematical computations are intricate, the details are presented in a manner that ensures flexibility, allowing instructors to omit these sections without interrupting the flow of the material. The use of computer software enables the instructor to focus on the managerial problem and spend less time on the details of the algorithms. Computer output is provided for many examples throughout the text.
Teaching and Learning Experience
This text provides a solid foundation in quantitative methods and management science. Here’s how:
Students see clearly how concepts and techniques are used in real organizations.
Outstanding in-text features provide reinforcement and ensure understanding.
The text’s use of software allows instructors to focus on the managerial problem, while spending less time on the mathematical details of the algorithms.
Features + Benefits
Students see clearly how concepts and techniques are used in real organizations.
An emphasis on model building and computer applications shows how the techniques presented are used in business.
NEW! The transportation, assignment, and network models are now in one chapter focused on modeling with linear programming.
NEW! Specialized algorithms for the transportation, assignment, and network methods are combined into Online Module 8.
NEW! An introduction to business analytics is provided.
QA in Action boxes illustrate how real organizations use quantitative analysis to solve problems.
NEW! 8 new boxes have been added.
Modeling in the Real World boxes demonstrate the application of the quantitative analysis approach to every technique discussed in the book.
NEW! 4 new boxes have been added.
NEW! New examples have been added throughout the text. Many other problems and cases have been updated.
Case Studies provide additional challenging managerial applications.
Outstanding in-text features provide reinforcement and ensure understanding
Procedure boxes summarize the more complex quantitative techniques, presenting them as a series of easily understandable steps.
Solved Problems at the end of each chapter serve as models for students in solving their own homework problems.
Discussion Questions test students’ understanding of the concepts covered in each chapter.
Applications-oriented problems in every chapter test students’ abilities to solve exam-type problems.
Internet Homework Problems, available on the Companion website, provide additional support.
Glossaries at the end of each chapter define important terms.
Key Equations are listed at the end of each chapter.
End-of-chapter bibliographies provide a current selection of more advanced books and articles.
NEW! Examples and problems have been updated, and many new ones have been added.
The text’s use of software allows instructors to focus on the managerial problem, while spending less time on the mathematical details of the algorithms.
NEW! Excel 2013 is incorporated throughout the chapters.
NEW! Screenshots are integrated in the appropriate sections so students can easily see how to use Excel for the calculations.
NEW! The Excel QM add-in is used with Excel 2013, allowing students with limited Excel experience to easily perform the necessary calculations.
Online modules provide additional coverage to topics in quantitative analysis.
The Companion Website at www.pearsonhighered.com/render provides the online modules, additional problems, cases, and other material for almost every chapter.
Data files with Excel spreadsheets and POM-QM for Windows files containing all the examples in the text are available for students to download from the Companion Website. Instructors can download these plus additional files containing computer solutions to the relevant end-of-chapter problems from the Instructor Resource Center website.
Preface
Index of Applications
Chapter 1
Data and Decisions (E-Commerce)
1.1 Data and Decisions
1.2 Variable Types
1.3 Data Sources: Where, How, and When
Ethics in Action
Technology Help: Data on the Computer
Brief Case: Credit Card Bank
Chapter 2
Displaying and Describing Categorical Data (Keen, Inc.)
2.1 Summarizing a Categorical Variable
2.2 Displaying a Categorical Variable
2.3 Exploring Two Categorical Variables: Contingency Tables
2.4 Segmented Bar Charts and Mosaic Plots
2.5 Simpson’s Paradox
Ethics in Action
Technology Help: Displaying Categorical Data on the Computer
Brief Case: Credit Card Bank
Chapter 3
Displaying and Describing Quantitative Data (AIG)
3.1 Displaying Quantitative Variables
3.2 Shape
3.3 Center
3.4 Spread of the Distribution
3.5 Shape, Center, and Spread–A Summary
3.6 Standardizing Variables
3.7 Five-Number Summary and Boxplots
3.8 Comparing Groups,
3.9 Identifying Outliers,
3.10 Time Series Plots
3.11 Transforming Skewed Data
Ethics in Action
Technology Help: Displaying and Summarizing Quantitative Variables
Brief Cases: Detecting the Housing Bubble and Socio-Economic Data on States
Chapter 4
Correlation and Linear Regression (Amazon.com)
4.1 Looking at Scatterplots
4.2 Assigning Roles to Variables in Scatterplots
4.3 Understanding Correlation
4.4 Lurking Variables and Causation
4.5 The Linear Model
4.6 Correlation and the Line
4.7 Regression to the Mean
4.8 Checking the Model
4.9 Variation in the Model and R2
4.10 Reality Check: Is the Regression Reasonable?
4.11 Nonlinear Relationships
Ethics in Action
Technology Help: Correlation and Regression
Brief Cases: Fuel Efficiency, Cost of Living, and Mutual Funds
Case Study I: Paralyzed Veterans of America
Chapter 5
Randomness and Probability (Credit Reports and the Fair Isaacs Corporation)
5.1 Random Phenomena and Probability
5.2 The Nonexistent Law of Averages
5.3 Different Types of Probability
5.4 Probability Rules
5.5 Joint Probability and Contingency Tables
5.6 Conditional Probability
5.7 Constructing Contingency Tables
5.8 Probability Trees
5.9 Reversing the Conditioning: Bayes’ Rule
Ethics in Action
Technology Help: Generating Random Numbers
Brief Case: title to come
Chapter 6
Random Variables and Probability Models (Metropolitan Life Insurance Company)
6.1 Expected Value of a Random Variable
6.2 Standard Deviation of a Random Variable
6.3 Properties of Expected Values and Variances
6.4 Bernoulli Trials
6.5 Discrete Probability Models
Ethics in Action
Technology Help: Random Variables and Probability Models
Brief Case: Investment Options
Chapter 7
The Normal and other Continuous Distributions (The NYSE)
7.1 The Standard Deviation as a Ruler
7.2 The Normal Distribution
7.3 Normal Probability Plots
7.4 The Distribution of Sums of Normals
7.5 The Normal Approximation for the Binomial
7.6 The Other Continuous Random Variables
Ethics in Action
Technology Help: Probability Calculations and Plots
Brief Case: case title to come
Chapter 8
Surveys and Sampling (Roper Polls)
8.1 Three Ideas of Sampling
8.2 Populations and Parameters
8.3 Common Sampling Designs
8.4 The Valid Survey
8.5 How to Sample Badly
Ethics in Action
Technology Help: Random Sampling
Brief Cases: Market Survey Research and The GfK Roper Reports Worldwide Survey
Chapter 9
Sampling Distributions and Confidence Intervals for Proportions (Marketing Credit Cards: The MBNA Story)
9.1 The Distribution of Sample Proportions
9.2 A Confidence Interval
9.3 Margin of Error: Certainty vs. Precision
9.4 Choosing and Sample Size
Ethics in Action
Technology Help: Confidence Intervals for Proportions
Brief Case: Real Estate Simulation
Case Study II:
Chapter 10
Testing Hypotheses about Proportions (Dow Jones Industrial Average)
10.1 Hypotheses
10.2 A Trial as a Hypothesis Test
10.3 P-Values
10.4 The Reasoning of Hypothesis Testing
10.5 Alternative Hypotheses
10.6 P-Values and Decisions: What to Tell About a Hypothesis Test
Ethics in Action
Technology Help: Hypothesis Tests
Brief Cases: Metal Production and Loyalty Program
Chapter 11
Confidence Intervals and Hypothesis Tests for Means (Guinness & Co.)
11.1 The Central Limit Theorem
11.2 The Sampling Distribution of the Mean
11.3 How Sampling Distribution Models Work
11.4 Gossett and the t-Distribution
11.5 A Confidence Interval for Means
11.6 Assumptions and Conditions
11.7 Testing Hypothesis about Means–the One-Sample t-Test
Ethics in Action
Technology Help: Inference for Means
Brief Cases: Real Estate and Donor Profiles
Chapter 12
More About Tests and Intervals (Traveler’s Insurance)
12.1 How to Think About P-Values
12.2 Alpha Levels and Significance
12.3 Critical Values
12.4 Confidence Intervals and Hypothesis Tests
12.5 Two Types of Errors
12.6 Power
Ethics in Action
Technology Help: Hypothesis Tests
Brief Case: brief case title to come
Chapter 13
Comparing Two Means (Visa Global Organization)
13.1 Comparing Two Means
13.2 The Two-Sample t-Test
13.3 Assumptions and Conditions
13.4 A Confidence Interval for the Difference Between Two Means
13.5 The Pooled t-Test
13.6 Paired Data
13.7 Paired Methods
Ethics in Action
Technology Help: Two-Sample Methods
Technology Help: Paired t
Brief Cases: Real Estate and Consumer Spending Patterns (Data Analysis)
Chapter 14
Inference for Counts: Chi-Square Tests (SAC Capital)
14.1 Goodness-of-Fit Tests
14.2 Interpreting Chi-Square Values
14.3 Examining the Residuals
14.4 The Chi-Square Test of Homogeneity
14.5 Comparing Two Proportions
14.6 Chi-Square Test of Independence
Ethics in Action
Technology Help: Chi-Square
Brief Cases: Health Insurance and Loyalty Program
Case Study III: Investment Strategy Segmentation
Chapter 15
Inference for Regression (Nambé Mills)
15.1 A Hypothesis Test and Confidence Interval for the Slope
15.2 Assumptions and Conditions
15.3 Standard Errors for Predicted Values
15.4 Using Confidence and Prediction Intervals
Ethics in Action
Technology Help: Regression Analysis
Brief Cases: Frozen Pizza and Global Warming?
Chapter 16
Understanding Residuals (Kellogg’s)
16.1 Examining Residuals for Groups
16.2 Extrapolation and Prediction
16.3 Unusual and Extraordinary Observations
16.4 Working with Summary Values
16.5 Autocorrelation
16.6 Transforming (Re-expressing) Data
16.7 The Ladder of Powers
Ethics in Action
Technology Help: Examining Residuals
Brief Cases: Gross Domestic Product and Energy Sources
Chapter 17
Multiple Regression (Zillow.com)
17.1 The Multiple Regression Model
17.2 Interpreting Multiple Regression Coefficients
17.3 Assumptions and Conditions for the Multiple Regression Model
17.4 Testing the Multiple Regression Model
17.5 Adjusted R2 and the F-statistic
17.6 The Logistic Regression Model
Ethics in Action
Technology Help: Regression Analysis
Brief Case: Golf Success
Chapter 18
Building Multiple Regression Models (Bolliger and Mabillard)
18.1 Indicator (or Dummy) Variables
18.2 Adjusting for Different Slopes–Interaction Terms
18.3 Multiple Regression Diagnostics
18.4 Building Regr
A solid foundation in quantitative methods and management science
This popular text gives students a genuine foundation in business analytics, quantitative methods, and management science—and how to apply the concepts and techniques in the real world—through a strong emphasis on model building, computer applications, and examples. The authors’ approach presents mathematical models, with all of the necessary assumptions, in clear, plain English, and then applies the ensuing solution procedures to example problems along with step-by-step, how-to instructions. In instances in which the mathematical computations are intricate, the details are presented in a manner that ensures flexibility, allowing instructors to omit these sections without interrupting the flow of the material. The use of computer software enables the instructor to focus on the managerial problem and spend less time on the details of the algorithms. Computer output is provided for many examples throughout the text.
Teaching and Learning Experience
This text provides a solid foundation in quantitative methods and management science. Here’s how:
Students see clearly how concepts and techniques are used in real organizations.
Outstanding in-text features provide reinforcement and ensure understanding.
The text’s use of software allows instructors to focus on the managerial problem, while spending less time on the mathematical details of the algorithms.
Features + Benefits
Students see clearly how concepts and techniques are used in real organizations.
An emphasis on model building and computer applications shows how the techniques presented are used in business.
NEW! The transportation, assignment, and network models are now in one chapter focused on modeling with linear programming.
NEW! Specialized algorithms for the transportation, assignment, and network methods are combined into Online Module 8.
NEW! An introduction to business analytics is provided.
QA in Action boxes illustrate how real organizations use quantitative analysis to solve problems.
NEW! 8 new boxes have been added.
Modeling in the Real World boxes demonstrate the application of the quantitative analysis approach to every technique discussed in the book.
NEW! 4 new boxes have been added.
NEW! New examples have been added throughout the text. Many other problems and cases have been updated.
Case Studies provide additional challenging managerial applications.
Outstanding in-text features provide reinforcement and ensure understanding
Procedure boxes summarize the more complex quantitative techniques, presenting them as a series of easily understandable steps.
Solved Problems at the end of each chapter serve as models for students in solving their own homework problems.
Discussion Questions test students’ understanding of the concepts covered in each chapter.
Applications-oriented problems in every chapter test students’ abilities to solve exam-type problems.
Internet Homework Problems, available on the Companion website, provide additional support.
Glossaries at the end of each chapter define important terms.
Key Equations are listed at the end of each chapter.
End-of-chapter bibliographies provide a current selection of more advanced books and articles.
NEW! Examples and problems have been updated, and many new ones have been added.
The text’s use of software allows instructors to focus on the managerial problem, while spending less time on the mathematical details of the algorithms.
NEW! Excel 2013 is incorporated throughout the chapters.
NEW! Screenshots are integrated in the appropriate sections so students can easily see how to use Excel for the calculations.
NEW! The Excel QM add-in is used with Excel 2013, allowing students with limited Excel experience to easily perform the necessary calculations.
Online modules provide additional coverage to topics in quantitative analysis.
The Companion Website at www.pearsonhighered.com/render provides the online modules, additional problems, cases, and other material for almost every chapter.
Data files with Excel spreadsheets and POM-QM for Windows files containing all the examples in the text are available for students to download from the Companion Website. Instructors can download these plus additional files containing computer solutions to the relevant end-of-chapter problems from the Instructor Resource Center website.
Preface
Index of Applications
Chapter 1
Data and Decisions (E-Commerce)
1.1 Data and Decisions
1.2 Variable Types
1.3 Data Sources: Where, How, and When
Ethics in Action
Technology Help: Data on the Computer
Brief Case: Credit Card Bank
Chapter 2
Displaying and Describing Categorical Data (Keen, Inc.)
2.1 Summarizing a Categorical Variable
2.2 Displaying a Categorical Variable
2.3 Exploring Two Categorical Variables: Contingency Tables
2.4 Segmented Bar Charts and Mosaic Plots
2.5 Simpson’s Paradox
Ethics in Action
Technology Help: Displaying Categorical Data on the Computer
Brief Case: Credit Card Bank
Chapter 3
Displaying and Describing Quantitative Data (AIG)
3.1 Displaying Quantitative Variables
3.2 Shape
3.3 Center
3.4 Spread of the Distribution
3.5 Shape, Center, and Spread–A Summary
3.6 Standardizing Variables
3.7 Five-Number Summary and Boxplots
3.8 Comparing Groups,
3.9 Identifying Outliers,
3.10 Time Series Plots
3.11 Transforming Skewed Data
Ethics in Action
Technology Help: Displaying and Summarizing Quantitative Variables
Brief Cases: Detecting the Housing Bubble and Socio-Economic Data on States
Chapter 4
Correlation and Linear Regression (Amazon.com)
4.1 Looking at Scatterplots
4.2 Assigning Roles to Variables in Scatterplots
4.3 Understanding Correlation
4.4 Lurking Variables and Causation
4.5 The Linear Model
4.6 Correlation and the Line
4.7 Regression to the Mean
4.8 Checking the Model
4.9 Variation in the Model and R2
4.10 Reality Check: Is the Regression Reasonable?
4.11 Nonlinear Relationships
Ethics in Action
Technology Help: Correlation and Regression
Brief Cases: Fuel Efficiency, Cost of Living, and Mutual Funds
Case Study I: Paralyzed Veterans of America
Chapter 5
Randomness and Probability (Credit Reports and the Fair Isaacs Corporation)
5.1 Random Phenomena and Probability
5.2 The Nonexistent Law of Averages
5.3 Different Types of Probability
5.4 Probability Rules
5.5 Joint Probability and Contingency Tables
5.6 Conditional Probability
5.7 Constructing Contingency Tables
5.8 Probability Trees
5.9 Reversing the Conditioning: Bayes’ Rule
Ethics in Action
Technology Help: Generating Random Numbers
Brief Case: title to come
Chapter 6
Random Variables and Probability Models (Metropolitan Life Insurance Company)
6.1 Expected Value of a Random Variable
6.2 Standard Deviation of a Random Variable
6.3 Properties of Expected Values and Variances
6.4 Bernoulli Trials
6.5 Discrete Probability Models
Ethics in Action
Technology Help: Random Variables and Probability Models
Brief Case: Investment Options
Chapter 7
The Normal and other Continuous Distributions (The NYSE)
7.1 The Standard Deviation as a Ruler
7.2 The Normal Distribution
7.3 Normal Probability Plots
7.4 The Distribution of Sums of Normals
7.5 The Normal Approximation for the Binomial
7.6 The Other Continuous Random Variables
Ethics in Action
Technology Help: Probability Calculations and Plots
Brief Case: case title to come
Chapter 8
Surveys and Sampling (Roper Polls)
8.1 Three Ideas of Sampling
8.2 Populations and Parameters
8.3 Common Sampling Designs
8.4 The Valid Survey
8.5 How to Sample Badly
Ethics in Action
Technology Help: Random Sampling
Brief Cases: Market Survey Research and The GfK Roper Reports Worldwide Survey
Chapter 9
Sampling Distributions and Confidence Intervals for Proportions (Marketing Credit Cards: The MBNA Story)
9.1 The Distribution of Sample Proportions
9.2 A Confidence Interval
9.3 Margin of Error: Certainty vs. Precision
9.4 Choosing and Sample Size
Ethics in Action
Technology Help: Confidence Intervals for Proportions
Brief Case: Real Estate Simulation
Case Study II:
Chapter 10
Testing Hypotheses about Proportions (Dow Jones Industrial Average)
10.1 Hypotheses
10.2 A Trial as a Hypothesis Test
10.3 P-Values
10.4 The Reasoning of Hypothesis Testing
10.5 Alternative Hypotheses
10.6 P-Values and Decisions: What to Tell About a Hypothesis Test
Ethics in Action
Technology Help: Hypothesis Tests
Brief Cases: Metal Production and Loyalty Program
Chapter 11
Confidence Intervals and Hypothesis Tests for Means (Guinness & Co.)
11.1 The Central Limit Theorem
11.2 The Sampling Distribution of the Mean
11.3 How Sampling Distribution Models Work
11.4 Gossett and the t-Distribution
11.5 A Confidence Interval for Means
11.6 Assumptions and Conditions
11.7 Testing Hypothesis about Means–the One-Sample t-Test
Ethics in Action
Technology Help: Inference for Means
Brief Cases: Real Estate and Donor Profiles
Chapter 12
More About Tests and Intervals (Traveler’s Insurance)
12.1 How to Think About P-Values
12.2 Alpha Levels and Significance
12.3 Critical Values
12.4 Confidence Intervals and Hypothesis Tests
12.5 Two Types of Errors
12.6 Power
Ethics in Action
Technology Help: Hypothesis Tests
Brief Case: brief case title to come
Chapter 13
Comparing Two Means (Visa Global Organization)
13.1 Comparing Two Means
13.2 The Two-Sample t-Test
13.3 Assumptions and Conditions
13.4 A Confidence Interval for the Difference Between Two Means
13.5 The Pooled t-Test
13.6 Paired Data
13.7 Paired Methods
Ethics in Action
Technology Help: Two-Sample Methods
Technology Help: Paired t
Brief Cases: Real Estate and Consumer Spending Patterns (Data Analysis)
Chapter 14
Inference for Counts: Chi-Square Tests (SAC Capital)
14.1 Goodness-of-Fit Tests
14.2 Interpreting Chi-Square Values
14.3 Examining the Residuals
14.4 The Chi-Square Test of Homogeneity
14.5 Comparing Two Proportions
14.6 Chi-Square Test of Independence
Ethics in Action
Technology Help: Chi-Square
Brief Cases: Health Insurance and Loyalty Program
Case Study III: Investment Strategy Segmentation
Chapter 15
Inference for Regression (Nambé Mills)
15.1 A Hypothesis Test and Confidence Interval for the Slope
15.2 Assumptions and Conditions
15.3 Standard Errors for Predicted Values
15.4 Using Confidence and Prediction Intervals
Ethics in Action
Technology Help: Regression Analysis
Brief Cases: Frozen Pizza and Global Warming?
Chapter 16
Understanding Residuals (Kellogg’s)
16.1 Examining Residuals for Groups
16.2 Extrapolation and Prediction
16.3 Unusual and Extraordinary Observations
16.4 Working with Summary Values
16.5 Autocorrelation
16.6 Transforming (Re-expressing) Data
16.7 The Ladder of Powers
Ethics in Action
Technology Help: Examining Residuals
Brief Cases: Gross Domestic Product and Energy Sources
Chapter 17
Multiple Regression (Zillow.com)
17.1 The Multiple Regression Model
17.2 Interpreting Multiple Regression Coefficients
17.3 Assumptions and Conditions for the Multiple Regression Model
17.4 Testing the Multiple Regression Model
17.5 Adjusted R2 and the F-statistic
17.6 The Logistic Regression Model
Ethics in Action
Technology Help: Regression Analysis
Brief Case: Golf Success
Chapter 18
Building Multiple Regression Models (Bolliger and Mabillard)
18.1 Indicator (or Dummy) Variables
18.2 Adjusting for Different Slopes–Interaction Terms
18.3 Multiple Regression Diagnostics
18.4 Building Regr