Data-Driven Business Decisions
- 10h 8m
- Chris J. Lloyd
- John Wiley & Sons (US)
- 2011
A hands-on guide to the use of quantitative methods and software for making successful business decisions
The appropriate use of quantitative methods lies at the core of successful decisions made by managers, researchers, and students in the field of business. Providing a framework for the development of sound judgment and the ability to utilize quantitative and qualitative approaches, Data Driven Business Decisions introduces readers to the important role that data plays in understanding business outcomes, addressing four general areas that managers need to know about: data handling and Microsoft Excel, uncertainty, the relationship between inputs and outputs, and complex decisions with trade-offs and uncertainty.
Grounded in the author's own classroom approach to business statistics, the book reveals how to use data to understand the drivers of business outcomes, which in turn allows for data-driven business decisions. A basic, non-mathematical foundation in statistics is provided, outlining for readers the tools needed to link data with business decisions; account for uncertainty in the actions of others and in patterns revealed by data; handle data in Excel; translate their analysis into simple business terms; and present results in simple tables and charts. The author discusses key data analytic frameworks, such as decision trees and multiple regression, and also explores additional topics, including:
- Use of the Excel functions Solver and Goal Seek
- Partial correlation and auto-correlation
- Interactions and proportional variation in regression models
- Seasonal adjustment and what it reveals
- Basic portfolio theory as an introduction to correlations
Chapters are introduced with case studies that integrate simple ideas into the larger business context, and are followed by further details, raw data, and motivating insights. Algebraic notation is used only when necessary, and throughout the book, the author utilizes real-world examples from diverse areas such as market surveys, finance, economics, and business ethics. Excel add-ins StatproGo and TreePlan are showcased to demonstrate execution of the techniques, and a related website features extensive programming instructions as well as insights, data sets, and solutions to problems included in the material.
Data Driven Business Decisions is an excellent book for MBA quantitative analysis courses or undergraduate general statistics courses. It also serves as a valuable reference for practicing MBAs and practitioners in the fields of statistics, business, and finance.
About the Author
CHRIS J. LLOYD, PhD, is Associate Dean of Research and Professor of Business Statistics in the Melbourne Business School at The University of Melbourne, Australia. Professor Lloyd has extensive international academic and consulting experience in the fields of statistics, data analysis, and market research within both academic and business environments. He has written more than 100 research articles in the areas of categorical data and is the author of Statistical Analysis of Categorical Data, also published by Wiley.
In this Book
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To the Student
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To the Teacher—How to Build a Course around This Book
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How are We Doing? Data-Driven Views of Business Performance
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What Stands Out and Why? Who Wins? Data-Driven Views of Performance Dynamics
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Dealing with Uncertainty and Chance
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Let the Data Change Your Views—The Bayes Method
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Valuing an Uncertain Payoff
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Business Problems That Depend on Knowing “How Many”
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Business Problems That Depend on Knowing “How Much”
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Making Complex Decisions with Trees
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Data, Estimation, and Statistical Reliability
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Managing Mean Performance
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Are These Customers Different? Did the Intervention Work? Looking at Changes in Mean Performance
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What is My Brand Recognition? Will it Sell? Analyzing Counts and Proportions
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Using the Relationship between Shares to Build a Portfolio
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Investigating Relationships between Business Variables
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Describing the Effect of a Business Input—Linear Regression
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The Reliability of Regression-Based Decisions
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Multicausal Relationships and Multiple Regression
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Product Features, Nonlinear Relationships, and Market Segments
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Analyzing Data That is Collected Regularly over Time
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Extending Regression Models—The Sky is the Limit
