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Quantitative methods : an introduction for business management / Paolo Brandimarte.

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"This book consists of the following four parts: Motivations and Foundations; Elementary Probability and Statistics; Decision Making Models; and Advanced Statistical Modeling. Part I is introductory, and an initial chapter provides motivation for all of the subsequent chapters by means of simpl...

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Bibliographic Details
Online Access: Full Text (via ProQuest)
Main Author: Brandimarte, Paolo
Format: eBook
Language:English
Published: Hoboken, N.J. : Wiley, ©2011.
Subjects:
Management > Mathematical models.
Table of Contents:
  • Front Matter
  • Motivations and Foundations. Quantitative Methods: Should We Bother?
  • Calculus
  • Linear Algebra
  • Elementary Probability and Statistics. Descriptive Statistics: On the Way to Elementary Probability
  • Probability Theories
  • Discrete Random Variables
  • Continuous Random Variables
  • Dependence, Correlation, and Conditional Expectation
  • Inferential Statistics
  • Simple Linear Regression
  • Time Series Models
  • Models for Decision Making. Deterministic Decision Models
  • Decision Making Under Risk
  • Multiple Decision Makers, Subjective Probability, and Other Wild Beasts
  • Advanced Statistical Modeling. Introduction to Multivariate Analysis
  • Advanced Regression Models
  • Dealing with Complexity: Data Reduction and Clustering
  • Index.
  • Contents note continued: 11.5.1. Stationary demand: three views of a smoother
  • 11.5.2. Stationary demand: initialization and choice of α
  • 11.5.3. Smoothing with trend
  • 11.5.4. Smoothing with multiplicative seasonality
  • 11.5.5. Smoothing with trend and multiplicative seasonality
  • 11.6. glance at advanced time series modeling
  • 11.6.1. Moving-average processes
  • 11.6.2. Autoregressive processes
  • 11.6.3. ARMA and ARIMA processes
  • 11.6.4. Using time series models for forecasting
  • Problems
  • For further reading
  • References
  • pt. III Models for Decision Making
  • 12. Deterministic Decision Models
  • 12.1. taxonomy of optimization models
  • 12.1.1. Linear programming problems
  • 12.1.2. Nonlinear programming problems
  • 12.1.3. Convex programming: difficult vs. easy problems
  • 12.2. Building linear programming models
  • 12.2.1. Production planning with assembly of components
  • 12.2.2. dynamic model for production planning
  • 12.2.3. Blending models
  • 12.2.4. Network optimization
  • 12.3. repertoire of model formulation tricks
  • 12.3.1. Alternative regression models
  • 12.3.2. Goal programming
  • 12.3.3. Multiobjective optimization
  • 12.3.4. Elastic model formulations
  • 12.3.5. Column-based model formulations
  • 12.4. Building integer programming models
  • 12.4.1. Knapsack problem
  • 12.4.2. Modeling logical constraints
  • 12.4.3. Fixed-charge problem and semicontinuous decision variables
  • 12.4.4. Lot-sizing with setup times and costs
  • 12.4.5. Plant location
  • 12.4.6. optimization model for portfolio tracking and compression
  • 12.4.7. Piecewise linear functions
  • 12.5. Nonlinear programming concepts
  • 12.5.1. case of equality constraints: Lagrange multipliers
  • 12.5.2. Dealing with inequality constraints: Karush-Kuhn-Tucker conditions
  • 12.5.3. economic interpretation of Lagrange multipliers: shadow prices
  • 12.6. glance at solution methods
  • 12.6.1. Simplex method
  • 12.6.2. LP-based branch and bound method
  • 12.6.3. impact of model formulation
  • Problems
  • For further reading
  • References
  • 13. Decision Making Under Risk
  • 13.1. Decision trees
  • 13.1.1. Expected value of perfect information
  • 13.2. Risk aversion and risk measures
  • 13.2.1. conceptual tool: the utility function
  • 13.2.2. Mean-risk optimization
  • 13.2.3. Quantile-based risk measures: value at risk
  • 13.3. Two-stage stochastic programming models
  • 13.3.1. two-stage model: assembly-to-order production planning
  • 13.3.2. value of the stochastic solution
  • 13.3.3. mean-risk formulation of the assembly-to-order problem
  • 13.4. Multistage stochastic linear programming with recourse
  • 13.4.1. multistage model: asset-liability management
  • 13.4.2. Asset-liability management with transaction costs
  • 13.4.3. Scenario generation for stochastic programming
  • 13.5. Robustness, regret, and disappointment
  • 13.5.1. Robust optimization
  • 13.5.2. Disappointment and regret in decision making
  • Problems
  • For further reading
  • References
  • 14. Multiple Decision Makers, Subjective Probability, and Other Wild Beasts
  • 14.1. What is uncertainty-- 14.1.1. standard case: decision making under risk
  • 14.1.2. Uncertainty about uncertainty
  • 14.1.3. Do black swans exist-- 14.1.4. Is uncertainty purely exogenous-- 14.2. Decision problems with multiple decision makers
  • 14.3. Incentive misalignment in supply chain management
  • 14.4. Game theory
  • 14.4.1. Games in normal form
  • 14.4.2. Equilibrium in dominant strategies
  • 14.4.3. Nash equilibrium
  • 14.4.4. Simultaneous vs. sequential games
  • 14.5. Braess' paradox for traffic networks
  • 14.6. Dynamic feedback effects and herding behavior
  • 14.7. Subjective probability: the Bayesian view
  • 14.7.1. Bayesian estimation
  • 14.7.2. financial application: The Black-Litterman model
  • Problems
  • For further reading
  • References
  • pt. IV Advanced Statistical Modeling
  • 15. Introduction to Multivariate Analysis
  • 15.1. Issues in multivariate analysis
  • 15.1.1. Visualization
  • 15.1.2. Complexity and redundancy
  • 15.1.3. Different types of variables
  • 15.1.4. Adapting statistical inference procedures
  • 15.1.5. Missing data and outliers
  • 15.2. overview of multivariate methods
  • 15.2.1. Multiple regression models
  • 15.2.2. Principal component analysis
  • 15.2.3. Factor analysis
  • 15.2.4. Cluster analysis
  • 15.2.5. Canonical correlation
  • 15.2.6. Discriminant analysis
  • 15.2.7. Structural equation models with latent variables
  • 15.2.8. Multidimensional scaling
  • 15.2.9. Correspondence analysis
  • 15.3. Matrix algebra and multivariate analysis
  • 15.3.1. Covariance matrices
  • 15.3.2. Measuring distance and the Mahalanobis transformation
  • For further reading
  • References
  • 16. Advanced Regression Models
  • 16.1. Multiple linear regression by least squares
  • 16.2. Building, testing, and using multiple linear regression models
  • 16.2.1. Selecting explanatory variables: collinearity
  • 16.2.2. Testing a multiple regression model
  • 16.2.3. Using regression for forecasting and explanation purposes
  • 16.3. Logistic regression
  • 16.3.1. digression: logit and probit choice models
  • 16.4. glance at nonlinear regression
  • 16.4.1. Polynomial regression
  • 16.4.2. Data transformations
  • Problems
  • For further reading
  • References
  • 17. Dealing with Complexity: Data Reduction and Clustering
  • 17.1. need for data reduction
  • 17.2. Principal component analysis (PCA)
  • 17.2.1. geometric view of PCA
  • 17.2.2. Another view of PCA
  • 17.2.3. small numerical example
  • 17.2.4. Applications of PCA
  • 17.3. Factor analysis
  • 17.4. Cluster analysis
  • 17.4.1. Measuring distance
  • 17.4.2. Hierarchical methods
  • 17.4.3. Nonhierarchical clustering: k-means
  • For further reading
  • References.

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