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Data-Driven Business Decisions

Book Description

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. The enclosed CD contains the complete book in electronic format, including all presented data, supplemental material on the discussed case files, and links to exercises and solutions.

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.

Table of Contents

  1. Cover Page
  2. Title Page
  3. Copyright
  4. Dedication
  5. Contents
  6. Preface
  7. To the Student
  8. To the Teacher: How to Build a Course Around This Book
  9. CHAPTER 1: How Are We Doing? Data-Driven Views of Business Performance
    1. 1.0. Introduction: What Is the Issue?
    2. 1.1. Setting Out Business Data
    3. 1.2. Different Kinds of Variables
    4. 1.3. The Idea of a Distribution
    5. 1.4. Typical Performance: The Sample Mean
    6. 1.5. Uncertainty in Performance: SD
    7. 1.6. Changing Units
    8. 1.7. Shapes of Distributions
    9. 1.8. Summary
  10. CHAPTER 2: What Stands Out and Why? Who Wins? Data-Driven Views of Performance Dynamics
    1. 2.0. Introduction: What Is the Issue?
    2. 2.1. Different Layouts of Business Data
    3. 2.2. Comparing Performance across Different Segments
    4. 2.3. Complex Comparisons: Using Pivotables
    5. 2.4. Unusually High or Low Outcomes: z -Scores
    6. 2.5. Homogeneous Peer Groups
    7. 2.6. Combining Different Performance Measures
    8. 2.7. Summary
  11. CHAPTER 3: Dealing with Uncertainty and Chance
    1. 3.0. Introduction: What Is the Issue?
    2. 3.1. Framing What Could Happen: Outcomes and Events
    3. 3.2. How Likely Is It? Probability Basics
    4. 3.3. Market Segments and Behavior; Probability Tables
    5. 3.4. Example in Health Care: Testing for a Disease
    6. 3.5. Conditional Probability
    7. 3.6. How Strong Is the Relationship? Measuring Dependence
    8. 3.7. Probability Trees
    9. 3.8. Summary
  12. CHAPTER 4: Let the Data Change Your Views: The Bayes Method
    1. 4.0. Introduction: What Is the Issue?
    2. 4.1. The Bayes Method in Pictures
    3. 4.2. The Bayes Method as an Algorithm
    4. 4.3. Example 1: A Simple Gambling Game
    5. 4.4. Example 2: Bayes in the Courtroom
    6. 4.5. Some Typical Business Applications
    7. 4.6. Summary
  13. CHAPTER 5: Valuing an Uncertain Payoff
    1. 5.0. INTRODUCTION: WHAT IS THE ISSUE?
    2. 5.1. What is a Probability Distribution?
    3. 5.2. Displaying a Probability Distribution
    4. 5.3. The Mean of a Distribution
    5. 5.4. EXAMPLE: Fines and Violations
    6. 5.5. Why Use the Mean?
    7. 5.6. The Standard Deviation of a Distribution
    8. 5.7. Comparing Two Distributions
    9. 5.8. Conditional Distributions and Means
    10. 5.9. Summary
  14. CHAPTER 6: Business Problems That Depend on Knowing “How Many”
    1. 6.0. Introduction: What is the Issue?
    2. 6.1. The Binomial Distribution
    3. 6.2. The Mean and Standard Deviation
    4. 6.3. The Negative Binomial Distribution
    5. 6.4. The Poisson Distribution
    6. 6.5. Some Typical Business Applications
    7. 6.6. Summary
  15. CHAPTER 7: Business Problems That Depend on Knowing “How Much”
    1. 7.0. Introduction: What Is the Issue?
    2. 7.1. The Normal Distribution
    3. 7.2. Calculating Normal Probabilities in Excel
    4. 7.3. Combining Normal Variables
    5. 7.4. Comparing Two Normal Distributions
    6. 7.5. The Standard Normal Distribution
    7. 7.6. EXAMPLE 3: Dealing with Uncertain Demand
    8. 7.7. Dealing with Proportional Variation
    9. 7.8. Summary
  16. CHAPTER 8: Making Complex Decisions with Trees
    1. 8.0. Introduction: What Is the Issue?
    2. 8.1. Elements of Decision Trees
    3. 8.2. Solving the Decision Tree
    4. 8.3. Multistage Decision Trees
    5. 8.4. Valuing a Decision Option
    6. 8.5. The Cost of Uncertainty
    7. 8.6. Summary
  17. CHAPTER 9: Data, Estimation, and Statistical Reliability
    1. 9.0. Introduction: What Is the Issue?
    2. 9.1. Describing the Past and the Future
    3. 9.2. How Were the Data Generated?
    4. 9.3. Law of Large Numbers
    5. 9.4. The Variability of the Sample Mean
    6. 9.5. The Standard Error of the Mean
    7. 9.6. The Normal Limit Theorem
    8. 9.7. Samples and Populations
    9. 9.8. Summary
  18. CHAPTER 10: Managing Mean Performance
    1. 10.0. Introduction: What Is the Issue?
    2. 10.1. Benchmarking Mean Performance
    3. 10.2. The Statistical Size of a Deviation
    4. 10.3. Decision Making, Hypothesis Testing, and p -Values
    5. 10.4. Confidence Intervals
    6. 10.5. One-Sided and Two-Sided Tests
    7. 10.6. Using StatproGo
    8. 10.7. Why Standard Deviation Matters
    9. 10.8. Assessing Detection Power
    10. 10.9. Summary
  19. CHAPTER 11: Are These Customers Different? Did the Intervention Work? Looking at Changes in Mean Performance
    1. 11.0. Introduction: What Is The Issue?
    2. 11.1. How Variable Is a Difference?
    3. 11.2. Describing Changes in Mean Performance
    4. 11.3. Example 2: Is Product Placement Worth It?
    5. 11.4. Performing the t -Test with StatproGo
    6. 11.5. Different Standard Deviations
    7. 11.6. Analyzing Matched-Pairs Data
    8. 11.7. Summary
  20. CHAPTER 12: What Is My Brand Recognition? Will It Sell? Analyzing Counts and Proportions
    1. 12.0. Introduction: What Is The Issue?
    2. 12.1. How Accurate Are Percentages?
    3. 12.2. Tests and Confidence Intervals for Proportions
    4. 12.3. Assessing Changes in Proportions
    5. 12.4. Using StatproGo
    6. 12.5. Alternative Methods
    7. 12.6. Summary
  21. CHAPTER 13: Using the Relationship between Shares to Build a Portfolio
    1. 13.0. Introduction: What Is the Issue?
    2. 13.1. How to Measure Financial Growth
    3. 13.2. Risk and Return: Both Matter
    4. 13.3. Correlation and Industry Structure
    5. 13.4. The Riskiness of a Portfolio
    6. 13.5. Balancing Risk and Return
    7. 13.6. Controlling Risk with TBs
    8. 13.7. Summary
  22. CHAPTER 14: Investigating Relationships between Business Variables
    1. 14.0. Introduction: What Is the Issue?
    2. 14.1. Measuring Association with Correlation
    3. 14.2. Looking at Complex Relationships
    4. 14.3. Interpreting Correlations
    5. 14.4. What Is Autocorrelation?
    6. 14.5. Untangling Relationships with Partial Correlation
    7. 14.6. Summary
  23. CHAPTER 15: Describing the Effect of a Business Input: Linear Regression
    1. 15.0. Introduction: What Is the Issue?
    2. 15.1. Linear Relationships
    3. 15.2. The Line of Best Fit
    4. 15.3. Computing the Least Squares Line
    5. 15.4. The Regression Model
    6. 15.5. How Reliable Is the Regression Line?
    7. 15.6. Summary
  24. CHAPTER 16: The Reliability of Regression-Based Decisions
    1. 16.0. Introduction: What Is the Issue?
    2. 16.1. Three Kinds of Questions That Regression Answers
    3. 16.2. Estimating the Effect of a Change
    4. 16.3. Estimating the Trend Mean
    5. 16.4. Prediction
    6. 16.5. Prediction Errors and What They Tell You
    7. 16.6. Summary
  25. CHAPTER 17: Multicausal Relationships and Multiple Regression
    1. 17.0. Introduction: What Is the Issue?
    2. 17.1. Multilinear Relationships
    3. 17.2. Multiple Regression
    4. 17.3. Model Assessment
    5. 17.4. Prediction and Trend Estimation
    6. 17.5. Summary
  26. CHAPTER 18: Product Features, Nonlinear Relationships, and Market Segments
    1. 18.0. Introduction: What Is the Issue?
    2. 18.1. Accounting for Yes–No Features
    3. 18.2. Quadratic Relationships
    4. 18.3. Quadratic Regression
    5. 18.4. Allowing for Segments and Groups
    6. 18.5. Automatic Model Selection
    7. 18.6. Summary
  27. CHAPTER 19: Analyzing Data That Is Collected Regularly Over Time
    1. 19.0. Introduction: What Is the Issue?
    2. 19.1. Measuring Growth and Seasonality
    3. 19.2. How Is the Growth Rate Changing?
    4. 19.3. Seasonally Adjusting Data
    5. 19.4. Delayed Effects
    6. 19.5. Predicting the Future (Using Autoregression)
    7. 19.6. Summary
  28. CHAPTER 20: Extending Regression Models: The Sky Is the Limit
    1. 20.0. Introduction: What Is the Issue?
    2. 20.1. Inputs That Have Varying Effects: Interactions
    3. 20.2. Inputs That Have Proportional Impacts
    4. 20.3. Case Study: How Effective Are Catalog Mail-Outs?
    5. 20.4. More on Time Series
    6. 20.5. Summary
  29. Index