Quantitative Analysis for Management, 12/e

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.

 

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 Regression Models
18.5 Collinearity
18.6 Quadratic Terms
Ethics in Action
Technology Help: Building Multiple Regression Models
Brief Case: title to come

Chapter 19
Time Series Analysis (Whole Food Market)
19.1 What Is a Time Series?
19.2 Components of a Time Series
19.3 Smoothing Methods
19.4 Summarizing Forecast Error
19.5 Autoregressive Models
19.6 Multiples Regression-based Models
19.7 Choosing a Time Series Forecasting Method
19.8 Interpreting Time Series Models: The Whole Foods Data Revisited
Ethics in Action
Technology Help title to come
Brief Cases: Intel Corporation and Tiffany & Co.

Case Study IV: Health Care Costs

Chapter 20
Design and Analysis of Experiments and Observational Studies (Capital One)
20.1 Observational Studies
20.2 Randomized
Comparative Experiments
20.3 The Four Principles of Experimental Design
20.4 Experimental Designs
20.5 Issues in Experimental Design
20.6 Analyzing a Design in One Factor–The One-Way Analysis of Variance
20.7 Assumptions and Conditions for ANOVA
20.8 Multiple Comparisons
20.9 ANOVA on Observational Data
20.10 Analysis of Multifactor Designs
Ethics in Action
Technology Help: Analysis of Variance
Brief Case: Multifactor Experiment Design


Chapter 21
Quality Control (Sony)
21.1 A Short History of Quality Control
21.2 Control Charts for Individual Observations (Run Charts)
21.3 Control Charts for Measurements:  and R Charts
21.4 Actions for Out-of-Control Processes
21.5 Control Charts for Attributes: p Charts and c Charts
21.6 Philosophies of Quality Control
Ethics in Action
Technology Help: Quality Control Charts
Brief Case: Laptop Touchpad Quality

Chapter 22
Nonparametric Methods (i4cp)
22.1 Ranks
22.2 The Wilcoxon Rank-Sum/Mann-Whitney Statistic
22.3 Kruskal-Wallace Test
22.4 Paired Data: The Wilcoxon Signed-Rank Test
22.5 Friedman Test for a Randomized Block Design
22.6 Kendall’s Tau: Measuring Monotonicity
22.7 Spearman’s Rho
22.8 When Should You Use Nonparametric Methods?
Ethics in Action
Technology Help title to come
Brief Case: Real Estate Reconsidered

Chapter 23
Decision Making and Risk (Data Description, Inc.)
23.1 Actions, States of Nature, and Outcomes
23.2 Payoff Tables and Decisions Trees
23.3 Minimizing Loss and Maximizing Gain
23.4 The Expected Value of an Action
23.5 Expected Value with Perfect Information
23.6 Decisions Made with Sample Information
23.7 Estimating Variation
23.8 Sensitivity
23.9 Simulation
23.10 More Complex Decisions
Ethics in Action
Technology Help title to come
Brief Cases: Texaco-Pennzoil and Insurance Services, Revisited

Chapter 24
Introduction to Data Mining (Paralyzed Veterans of America)
24.1 The Big Data Revolution
24.2 Direct Marketing
24.3 The Goals of Data Mining
24.4 Data Mining Myths
24.5 Successful Data Mining
24.6 Data Mining Problems
24.7 Data Mining Algorithms
24.8 The Data Mining Process
24.9 Summary
Ethics in Action

Case Study V Marketing Experiment
Appendixes

A  Answers
B  Photo Acknowledgments
C  Tables and Selected Formulas

Index