Quantitative Methods For Business

Readers don't need to be a mathematician to understand and maximize the power of quantitative methods! Written for the future or current business professional, QUANTITATIVE METHODS FOR BUSINESS, 12E, International Edition by a powerhouse, award-winning author team makes it easy for readers to understand how to most effectively use quantitative methods to make intelligent successful decisions. The book's hallmark problem-scenario approach guides readers through the application of mathematical concepts and techniques, while memorable examples illustrate how and when to use the methods. Readers discover everything needed for success in working with quantitative methods, from a strong managerial orientation to instant online access to Excel worksheets for text examples; The Management Scientist v6.0 and TreePlan; Crystal Ball; Premium Solver for Excel, and LINGO.

Quantitative Methods for Business: The A-Z of QM will enable readers to: *Appreciate the significance of quantitative methods for businesses and the study of business *Understand and apply a wide range of quantitative techniques *Select appropriate quantitative techniques for data analysis, problem solving and decision making *Interpret and communicate the results of quantitative analysis

An accessible introduction to the essential quantitative methods for making valuable business decisions Quantitative methods-research techniques used to analyze quantitative data-enable 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 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. 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.

Taking a non-threatening, non-theoretical approach to a subject students often find difficult, this book avoids rigorous mathematics and concentrates on applying quantitative ideas to the work situation.

The new edition of this highly successful and popular textbook is a comprehensive, easy-to-follow guide to using and interpreting all the quantitative techniques that students will encounter in their later business and financial careers; from fundamental principles through to more advanced applications. Topics are explained in a clear, friendly step-by-step style, accompanied by examples, exercises and activities, making the text ideal for self-tuition or for the student with no experience or confidence in working with numbers. This highly successful learning-by-doing approach, coupled with the book's clear structure, will enable even the most maths-phobic student to understand these essential mathematical skills. Comprehensive in both its scope of coverage and the range of abilities it caters for, this remains a core textbook for undergraduate students of business, management and finance, for whom Quantitative Methods modules will be a key component. It will also appeal to those on related MBA and postgraduate courses. New to this Edition: - Business Modelling 'Moving on...' feature with integrated web and book activities to promote student engagement with the application of mathematical techniques in real-life workplaces - Extensive revamp of two Statistics chapters based on student and lecturer feedback - Crucial updated practical guides to using Excel and SPSS - Integrated companion website resources helps relate theory to real world examples

This book covers a comprehensive range of topics in quantitative methods for business students. The opening chapters introduce basic statistical concepts which are used in later chapters in business and management applications. It also includes applications, examples and exercises in SPSS, Lotus and Excel.

Provides students with all the tools they need to pass the typical Quantitative Methods course. This title includes chapters that focus on a selection of statistical techniques, illustrated with examples from across business, marketing, economics, finance, and public administration, that may appeal to students across the business spectrum.