Book description
An accessible introduction to the essential quantitative methods for making valuable business decisions
Quantitative methodsresearch techniques used to analyze quantitative dataenable professionals to organize and understand numbers and, in turn, to make good decisions. Quantitative Methods: An Introduction for Business Management presents the application of quantitative mathematical modeling to decision making in a business management context and emphasizes not only the role of data in drawing conclusions, but also the pitfalls of undiscerning reliance of software packages that implement standard statistical procedures. With handson applications and explanations that are accessible to readers at various levels, the book successfully outlines the necessary tools to make smart and successful business decisions.
Progressing from beginner to more advanced material at an easytofollow pace, the author utilizes motivating examples throughout to aid readers interested in decision making and also provides critical remarks, intuitive traps, and counterexamples when appropriate.
The book begins with a discussion of motivations and foundations related to the topic, with introductory presentations of concepts from calculus to linear algebra. Next, the core ideas of quantitative methods are presented in chapters that explore introductory topics in probability, descriptive and inferential statistics, linear regression, and a discussion of time series that includes both classical topics and more challenging models. The author also discusses linear programming models and decision making under risk as well as less standard topics in the field such as game theory and Bayesian statistics. Finally, the book concludes with a focus on selected tools from multivariate statistics, including advanced regression models and data reduction methods such as principal component analysis, factor analysis, and cluster analysis.
The book promotes the importance of an analytical approach, particularly when dealing with a complex system where multiple individuals are involved and have conflicting incentives. A related website features Microsoft Excel® workbooks and MATLAB® scripts to illustrate concepts as well as additional exercises with solutions.
Quantitative Methods is an excellent book for courses on the topic at the graduate level. The book also serves as an authoritative reference and selfstudy guide for financial and business professionals, as well as readers looking to reinforce their analytical skills.
Table of contents
 Cover Page
 Title Page
 Copyright
 Contents
 Preface

Part I: Motivations and Foundations
 1: Quantitative Methods: Should We Bother?

2: Calculus
 2.1 A MOTIVATING EXAMPLE: ECONOMIC ORDER QUANTITY
 2.2 A LITTLE BACKGROUND
 2.3 FUNCTIONS
 2.4 CONTINUOUS FUNCTIONS
 2.5 COMPOSITE FUNCTIONS
 2.6 INVERSE FUNCTIONS
 2.7 DERIVATIVES
 2.8 RULES FOR CALCULATING DERIVATIVES
 2.9 USING DERIVATIVES FOR GRAPHING FUNCTIONS
 2.10 HIGHERORDER DERIVATIVES AND TAYLOR EXPANSIONS
 2.11 CONVEXITY AND OPTIMIZATION
 2.12 SEQUENCES AND SERIES
 2.13 DEFINITE INTEGRALS
 REFERENCES
 3: Linear Algebra

Part II: Elementary Probability and Statistics
 4: Descriptive Statistics: On the Way to Elementary Probability
 5: Probability Theories
 6: Discrete Random Variables

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 HIGHERORDER MOMENTS, SKEWNESS, AND KURTOSIS
 7.6 A FEW USEFUL CONTINUOUS PROBABILITY DISTRIBUTIONS
 7.7 SUMS OF INDEPENDENT RANDOM VARIABLES
 7.8 MISCELLANEOUS APPLICATIONS
 7.9 STOCHASTIC PROCESSES
 7.10 PROBABILITY SPACES, MEASURABILITY, AND INFORMATION
 REFERENCES
 8: Dependence, Correlation, and Conditional Expectation

9: Inferential Statistics
 9.1 RANDOM SAMPLES AND SAMPLE STATISTICS
 9.2 CONFIDENCE INTERVALS
 9.3 HYPOTHESIS TESTING
 9.4 BEYOND THE MEAN OF ONE POPULATION
 9.5 CHECKING THE FIT OF HYPOTHETICAL DISTRIBUTIONS: THE CHISQUARE TEST
 9.6 ANALYSIS OF VARIANCE
 9.7 MONTE CARLO SIMULATION
 9.8 STOCHASTIC CONVERGENCE AND THE LAW OF LARGE NUMBERS
 9.9 PARAMETER ESTIMATION
 9.10 SOME MORE HYPOTHESIS TESTING THEORY
 REFERENCES
 10: Simple Linear Regression
 11: Time Series Models

Part III: Models for Decision Making
 12: Deterministic Decision Models
 13: Decision Making Under Risk

14: Multiple Decision Makers, Subjective Probability, and Other Wild Beasts
 14.1 WHAT IS UNCERTAINTY?
 14.2 DECISION PROBLEMS WITH MULTIPLE DECISION MAKERS
 14.3 INCENTIVE MISALIGNMENT IN SUPPLY CHAIN MANAGEMENT
 14.4 GAME THEORY
 14.5 BRAESS' PARADOX FOR TRAFFIC NETWORKS
 14.6 DYNAMIC FEEDBACK EFFECTS AND HERDING BEHAVIOR
 14.7 SUBJECTIVE PROBABILITY: THE BAYESIAN VIEW
 REFERENCES
 Part IV: Advanced Statistical Modeling
 Index
Product information
 Title: Quantitative Methods: An Introduction for Business Management
 Author(s):
 Release date: April 2011
 Publisher(s): Wiley
 ISBN: 9780470496343
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