Progressing from beginner to more advanced material at an easy-to-follow 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 self-study guide for financial and business professionals, as well as readers looking to reinforce their analytical skills.
1. Quantitative Methods: Should We Bother?
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
8. Dependence, Correlation, and Conditional Expectation
9. Inferential Statistics
10. Simple Linear Regression
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
Part IV Advanced Statistical Modeling
15. Introduction to Multivariate Analysis
16. Advanced Regression Models
17. Dealing with Complexity: Data Reduction and Clustering