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Quantitative methods : an introduction for business management /
"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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, N.J. :
Wiley,
©2011.
|
| Temas: | |
| Acceso en línea: | Texto completo Texto completo |
MARC
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| 100 | 1 | |a Brandimarte, Paolo. | |
| 245 | 1 | 0 | |a Quantitative methods : |b an introduction for business management / |c Paolo Brandimarte. |
| 260 | |a Hoboken, N.J. : |b Wiley, |c ©2011. | ||
| 300 | |a 1 online resource (xxiv, 886 pages) : |b illustrations | ||
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| 505 | 0 | |6 880-01 |a 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. | |
| 520 | |a "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 simple, but (hopefully) well-thought, toy examples. The following two chapters lay down necessary foundations in calculus and algebra. Part II consists of a classical course in probability and statistics, and the author stresses the use of many examples and counter-examples. Part III addresses decision making since probability and statistics are used to make decisions. Deterministic models, i.e. typical LP models, are introduced, and the emphasis is on modeling rather than computation by the simplex method. Emphasis is also placed on risk aversion and risk measures, and the author illustrates portfolio management as a main motivator. Part IV builds on Part II and discusses a few multivariate analysis models. To help less mathematically inclined readers, each chapter in Part IV contains an initial section that illustrates and motivates each approach without delving into too many details. These readers may wish to skip the remainder of each chapter. The book's companion Web site includes Microsoft Office Excel workbooks to illustrate concepts. In addition, MATLAB files, additional exercises with solutions are provided online"-- |c Provided by publisher | ||
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record. | |
| 546 | |a English. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Management |x Mathematical models. | |
| 650 | 6 | |a Gestion |x Modèles mathématiques. | |
| 650 | 7 | |a BUSINESS & ECONOMICS |x Training. |2 bisacsh | |
| 650 | 7 | |a Management |x Mathematical models |2 fast | |
| 758 | |i has work: |a Quantitative methods (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGvvBRpWkJrXGfq7WVQCkP |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Brandimarte, Paolo. |t Quantitative methods. |d Hoboken, N.J. : Wiley, ©2011 |z 9780470496343 |w (DLC) 2010045222 |w (OCoLC)662400199 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=698852 |z Texto completo |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9780470496343/?ar |z Texto completo |
| 880 | 0 | 0 | |6 505-01/(S |g Contents note continued: |g 11.5.1. |t Stationary demand: three views of a smoother -- |g 11.5.2. |t Stationary demand: initialization and choice of α -- |g 11.5.3. |t Smoothing with trend -- |g 11.5.4. |t Smoothing with multiplicative seasonality -- |g 11.5.5. |t Smoothing with trend and multiplicative seasonality -- |g 11.6. |t glance at advanced time series modeling -- |g 11.6.1. |t Moving-average processes -- |g 11.6.2. |t Autoregressive processes -- |g 11.6.3. |t ARMA and ARIMA processes -- |g 11.6.4. |t Using time series models for forecasting -- |g Problems -- |g For further reading -- |g References -- |g pt. III |t Models for Decision Making -- |g 12. |t Deterministic Decision Models -- |g 12.1. |t taxonomy of optimization models -- |g 12.1.1. |t Linear programming problems -- |g 12.1.2. |t Nonlinear programming problems -- |g 12.1.3. |t Convex programming: difficult vs. easy problems -- |g 12.2. |t Building linear programming models -- |g 12.2.1. |t Production planning with assembly of components -- |g 12.2.2. |t dynamic model for production planning -- |g 12.2.3. |t Blending models -- |g 12.2.4. |t Network optimization -- |g 12.3. |t repertoire of model formulation tricks -- |g 12.3.1. |t Alternative regression models -- |g 12.3.2. |t Goal programming -- |g 12.3.3. |t Multiobjective optimization -- |g 12.3.4. |t Elastic model formulations -- |g 12.3.5. |t Column-based model formulations -- |g 12.4. |t Building integer programming models -- |g 12.4.1. |t Knapsack problem -- |g 12.4.2. |t Modeling logical constraints -- |g 12.4.3. |t Fixed-charge problem and semicontinuous decision variables -- |g 12.4.4. |t Lot-sizing with setup times and costs -- |g 12.4.5. |t Plant location -- |g 12.4.6. |t optimization model for portfolio tracking and compression -- |g 12.4.7. |t Piecewise linear functions -- |g 12.5. |t Nonlinear programming concepts -- |g 12.5.1. |t case of equality constraints: Lagrange multipliers -- |g 12.5.2. |t Dealing with inequality constraints: Karush-Kuhn-Tucker conditions -- |g 12.5.3. |t economic interpretation of Lagrange multipliers: shadow prices -- |g 12.6. |t glance at solution methods -- |g 12.6.1. |t Simplex method -- |g 12.6.2. |t LP-based branch and bound method -- |g 12.6.3. |t impact of model formulation -- |g Problems -- |g For further reading -- |g References -- |g 13. |t Decision Making Under Risk -- |g 13.1. |t Decision trees -- |g 13.1.1. |t Expected value of perfect information -- |g 13.2. |t Risk aversion and risk measures -- |g 13.2.1. |t conceptual tool: the utility function -- |g 13.2.2. |t Mean-risk optimization -- |g 13.2.3. |t Quantile-based risk measures: value at risk -- |g 13.3. |t Two-stage stochastic programming models -- |g 13.3.1. |t two-stage model: assembly-to-order production planning -- |g 13.3.2. |t value of the stochastic solution -- |g 13.3.3. |t mean-risk formulation of the assembly-to-order problem -- |g 13.4. |t Multistage stochastic linear programming with recourse -- |g 13.4.1. |t multistage model: asset-liability management -- |g 13.4.2. |t Asset-liability management with transaction costs -- |g 13.4.3. |t Scenario generation for stochastic programming -- |g 13.5. |t Robustness, regret, and disappointment -- |g 13.5.1. |t Robust optimization -- |g 13.5.2. |t Disappointment and regret in decision making -- |g Problems -- |g For further reading -- |g References -- |g 14. |t Multiple Decision Makers, Subjective Probability, and Other Wild Beasts -- |g 14.1. |t What is uncertainty-- |g 14.1.1. |t standard case: decision making under risk -- |g 14.1.2. |t Uncertainty about uncertainty -- |g 14.1.3. |t Do black swans exist-- |g 14.1.4. |t Is uncertainty purely exogenous-- |g 14.2. |t Decision problems with multiple decision makers -- |g 14.3. |t Incentive misalignment in supply chain management -- |g 14.4. |t Game theory -- |g 14.4.1. |t Games in normal form -- |g 14.4.2. |t Equilibrium in dominant strategies -- |g 14.4.3. |t Nash equilibrium -- |g 14.4.4. |t Simultaneous vs. sequential games -- |g 14.5. |t Braess' paradox for traffic networks -- |g 14.6. |t Dynamic feedback effects and herding behavior -- |g 14.7. |t Subjective probability: the Bayesian view -- |g 14.7.1. |t Bayesian estimation -- |g 14.7.2. |t financial application: The Black-Litterman model -- |g Problems -- |g For further reading -- |g References -- |g pt. IV |t Advanced Statistical Modeling -- |g 15. |t Introduction to Multivariate Analysis -- |g 15.1. |t Issues in multivariate analysis -- |g 15.1.1. |t Visualization -- |g 15.1.2. |t Complexity and redundancy -- |g 15.1.3. |t Different types of variables -- |g 15.1.4. |t Adapting statistical inference procedures -- |g 15.1.5. |t Missing data and outliers -- |g 15.2. |t overview of multivariate methods -- |g 15.2.1. |t Multiple regression models -- |g 15.2.2. |t Principal component analysis -- |g 15.2.3. |t Factor analysis -- |g 15.2.4. |t Cluster analysis -- |g 15.2.5. |t Canonical correlation -- |g 15.2.6. |t Discriminant analysis -- |g 15.2.7. |t Structural equation models with latent variables -- |g 15.2.8. |t Multidimensional scaling -- |g 15.2.9. |t Correspondence analysis -- |g 15.3. |t Matrix algebra and multivariate analysis -- |g 15.3.1. |t Covariance matrices -- |g 15.3.2. |t Measuring distance and the Mahalanobis transformation -- |g For further reading -- |g References -- |g 16. |t Advanced Regression Models -- |g 16.1. |t Multiple linear regression by least squares -- |g 16.2. |t Building, testing, and using multiple linear regression models -- |g 16.2.1. |t Selecting explanatory variables: collinearity -- |g 16.2.2. |t Testing a multiple regression model -- |g 16.2.3. |t Using regression for forecasting and explanation purposes -- |g 16.3. |t Logistic regression -- |g 16.3.1. |t digression: logit and probit choice models -- |g 16.4. |t glance at nonlinear regression -- |g 16.4.1. |t Polynomial regression -- |g 16.4.2. |t Data transformations -- |g Problems -- |g For further reading -- |g References -- |g 17. |t Dealing with Complexity: Data Reduction and Clustering -- |g 17.1. |t need for data reduction -- |g 17.2. |t Principal component analysis (PCA) -- |g 17.2.1. |t geometric view of PCA -- |g 17.2.2. |t Another view of PCA -- |g 17.2.3. |t small numerical example -- |g 17.2.4. |t Applications of PCA -- |g 17.3. |t Factor analysis -- |g 17.4. |t Cluster analysis -- |g 17.4.1. |t Measuring distance -- |g 17.4.2. |t Hierarchical methods -- |g 17.4.3. |t Nonhierarchical clustering: k-means -- |g For further reading -- |g References. |
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