Contents
Part I: Motivations and Foundations
Chapter 1: Quantitative Methods: Should We Bother?
1.1 A Decision Problem Without Uncertainty: Product Mix
1.3 Endogenous vs. Exogenous Uncertainty: Are We Alone?
1.4 Quantitative Models and Methods
1.5 Quantitative Analysis and Problem Solving
2.1 A Motivating Example: Economic Order Quantity
2.8 Rules for Calculating Derivatives
2.9 Using Derivatives for Graphing Functions
2.10 Higher-Order Derivatives and Taylor Expansions
2.11 Convexity and Optimization
3.1 A Motivating Example: Binomial Option Pricing
3.2 Solving Systems of Linear Equations
3.7 Eigenvalues and Eigenvectors
3.9 Calculus in Multiple Dimensions
Part II: Elementary Probability and Statistics
Chapter 4: Descriptive Statistics: On the Way to Elementary Probability
4.2 Organizing and Representing Raw Data
4.4 Cumulative Frequencies and Percentiles
Chapter 5: Probability Theories
5.1 Different Concepts of Probability
5.3 Conditional Probability and Independence
5.4 Total Probability and Bayes’ Theorems
Chapter 6: Discrete Random Variables
6.2 Characterizing Discrete Distributions
6.4 Variance and Standard Deviation
6.5 A Few Useful Discrete Distributions
Chapter 7: Continuous Random Variables
7.1 Building Intuition: From Discrete to Continuous Random Variables
7.2 Cumulative Distribution and Probability Density Functions
7.3 Expected Value and Variance
7.4 Mode, Median, and Quantiles
7.5 Higher-Order Moments, Skewness, and Kurtosis
7.6 A Few Useful Continuous Probability Distributions
7.7 Sums of Independent Random Variables
7.8 Miscellaneous Applications
7.10 Probability Spaces, Measurability, and Information
Chapter 8: Dependence, Correlation, and Conditional Expectation
8.1 Joint and Marginal Distributions
8.2 Independent Random Variables
8.3 Covariance and Correlation
Chapter 9: Inferential Statistics
9.1 Random Samples and Sample Statistics
9.4 Beyond The Mean of One Population
9.5 Checking The Fit of Hypothetical Distributions: The Chi-Square Test
9.8 Stochastic Convergence and The Law of Large Numbers
9.10 Some More Hypothesis Testing Theory
Chapter 10: Simple Linear Regression
10.2 The Need for A Statistical Framework
10.3 The Case of A Nonstochastic Regressor
10.5 A Glimpse of Stochastic Regressors and Heteroskedastic Errors
10.6 A Vector Space Look at Linear Regression
Chapter 11: Inferential Statistics
11.1 Before We Start: Framing The Forecasting Process
11.2 Measuring Forecast Errors
11.3 Time Series Decomposition
11.5 Heuristic Exponential Smoothing
11.6 A Glance At Advanced Time Series Modeling
Part III: Models for Decision Making
Chapter 12: Deterministic Decision Models
12.1 A Taxonomy of Optimization Models
12.2 Building Linear Programming Models
12.3 A Repertoire of Model Formulation Tricks
12.4 Building Integer Programming Models
12.5 Nonlinear Programming Concepts
12.6 A Glance At Solution Methods
Chapter 13: Decision Making Under Risk
13.2 Risk Aversion and Risk Measures
13.3 Two-Stage Stochastic Programming Models
13.4 Multistage Stochastic Linear Programming With Recourse
13.5 Robustness, Regret, and Disappointment
Chapter 14: Multiple Decision Makers, Subjective Probability, and Other Wild Beasts
14.2 Decision Problems with Multiple Decision Makers
14.3 Incentive Misalignment in Supply Chain Management
14.5 Braess’ Paradox for Traffic Networks
14.6 Dynamic Feedback Effects and Herding Behavior
14.7 Subjective Probability: The Bayesian View
Part IV: Advanced Statistical Modeling
Chapter 15: Introduction to Multivariate Analysis
15.1 Issues in Multivariate Analysis
15.2 An Overview of Multivariate Methods
15.3 Matrix Algebra and Multivariate Analysis
Chapter 16: Advanced Regression Models
16.1 Multiple Linear Regression by Least Squares
16.2 Building, Testing, and Using Multiple Linear Regression Models
16.4 A Glance At Nonlinear Regression
Chapter 17: Dealing with Complexity: Data Reduction and Clustering
17.1 The Need for Data Reduction