The book promotes the importance of an analytical approach, particularly when dealing with a complex system where multipleindividuals are involved and have conflicting incentives.
Quantitative methods : an introduction for business management
Quantitative Methods is an excellent book for courses onthe topic at the graduate level. The book also serves as anauthoritative reference and self-study guide for financial andbusiness professionals, as well as readers looking to reinforcetheir analytical skills.
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Quantitative Methods: An Introduction for Business Management
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Feb 06, Charles rated it it was amazing. This is a math book with some business and economic examples included. Data reduction and clustering The author presupposes that the reader is very familiar with the topics before the chapter is encountered. There is no slack cut in terms of the level of difficulty of the mathematics.
When a topic is first stated, it is as a reminder and not as a review. As the list and number of pages indicate, this is a book for a two or three semester course sequence in the mathematics used in high level quantitative methods used in business. Fundamentally, it is a text for courses designed for people that are math majors moving into quantitative business management.
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For that, it is an excellent book. Unlike some other books, no one can criticize this one for being weak on the math, I would suspect that the students using it would argue the contrary. This book was made available for free for review purposes Please re-enter recipient e-mail address es. You may send this item to up to five recipients. The name field is required. Please enter your name. The E-mail message field is required.
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English View all editions and formats Summary: 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. Emphasis is also placed on risk aversion and risk measures, and the author illustrates portfolio management as a main motivator. 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. 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 hands-on 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.
Quantitative Methods: An Introduction for Business Management - Paolo Brandimarte - Google Книги
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.
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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.