Data Driven Business Decisions
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More About This Title Data Driven Business Decisions


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.


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.


Preface XIII

To the Student XV

To the Teacher: How to Build a Course Around This Book XVII

Chapter 1 How Are We Doing? Data-Driven Views of Business Performance 1

1.1 Setting Out Business Data 2

1.2 Different Kinds of Variables 5

1.3 The Idea of a Distribution 8

1.4 Typical Performance: The Sample Mean 13

1.5 Uncertainty in Performance: SD 15

1.6 Changing Units 18

1.7 Shapes of Distributions 20

Chapter 2 What Stands Out and Why? Who Wins? Data-Driven Views of Performance Dynamics 25

2.1 Different Layouts of Business Data 27

2.2 Comparing Performance across Different Segments 29

2.3 Complex Comparisons: Using Pivotables 30

2.4 Unusually High or Low Outcomes: z-Scores 35

2.5 Homogeneous Peer Groups 39

2.6 Combining Different Performance Measures 41

Chapter 3 Dealing with Uncertainty and Chance 51

3.1 Framing What Could Happen: Outcomes and Events 52

3.2 How Likely Is It? Probability Basics 55

3.3 Market Segments and Behavior; Probability Tables 57

3.4 Example in Health Care: Testing for a Disease 59

3.5 Conditional Probability 61

3.6 How Strong Is the Relationship? Measuring Dependence 66

3.7 Probability Trees 71

Chapter 4 Let the Data Change Your Views: The Bayes Method 79

4.1 The Bayes Method in Pictures 80

4.2 The Bayes Method as an Algorithm 81

4.3 Example 1: A Simple Gambling Game 83

4.4 Example 2: Bayes in the Courtroom 87

4.5 Some Typical Business Applications 90

Chapter 5 Valuing an Uncertain Payoff 97

5.1 What Is a Probability Distribution? 98

5.2 Displaying a Probability Distribution 100

5.3 The Mean of a Distribution 103

5.4 Example: Fines and Violations 104

5.5 Why Use the Mean? 107

5.6 The Standard Deviation of a Distribution 109

5.7 Comparing Two Distributions 112

5.8 Conditional Distributions and Means 114

Chapter 6 Business Problems That Depend on Knowing “How Many” 121

6.1 The Binomial Distribution 123

6.2 The Mean and Standard Deviation 125

6.3 The Negative Binomial Distribution 128

6.4 The Poisson Distribution 132

6.5 Some Typical Business Applications 135

Chapter 7 Business Problems That Depend on Knowing “How Much” 141

7.1 The Normal Distribution 142

7.2 Calculating Normal Probabilities in Excel 145

7.3 Combining Normal Variables 149

7.4 Comparing Two Normal Distributions 152

7.5 The Standard Normal Distribution 153

7.6 Example 3: Dealing with Uncertain Demand 156

7.7 Dealing with Proportional Variation 160

Chapter 8 Making Complex Decisions with Trees 169

8.1 Elements of Decision Trees 171

8.2 Solving the Decision Tree 175

8.3 Multistage Decision Trees 181

8.4 Valuing a Decision Option 186

8.5 The Cost of Uncertainty 188

Chapter 9 Data, Estimation, and Statistical Reliability 195

9.1 Describing the Past and the Future 197

9.2 How Were the Data Generated? 199

9.3 Law of Large Numbers 200

9.4 The Variability of the Sample Mean 201

9.5 The Standard Error of the Mean 204

9.6 The Normal Limit Theorem 208

9.7 Samples and Populations 212

Chapter 10 Managing Mean Performance 219

10.1 Benchmarking Mean Performance 221

10.2 The Statistical Size of a Deviation 224

10.3 Decision Making, Hypothesis Testing, and p-Values 226

10.4 Confidence Intervals 230

10.5 One-Sided and Two-Sided Tests 232

10.6 Using StatproGo 232

10.7 Why Standard Deviation Matters 234

10.8 Assessing Detection Power 235

Chapter 11 Are These Customers Different? Did the Intervention Work? Looking at Changes in Mean Performance 243

11.1 How Variable Is a Difference? 245

11.2 Describing Changes in Mean Performance 247

11.3 Example 2: Is Product Placement Worth It? 249

11.4 Performing the t-Test with StatproGo 255

11.5 Different Standard Deviations 258

11.6 Analyzing Matched-Pairs Data 261

Chapter 12 What Is My Brand Recognition? Will It Sell? Analyzing Counts and Proportions 271

12.1 How Accurate Are Percentages? 272

12.2 Tests and Confidence Intervals for Proportions 277

12.3 Assessing Changes in Proportions 280

12.4 Using StatproGo 284

12.5 Alternative Methods 284

Chapter 13 Using the Relationship between Shares to Build a Portfolio 293

13.1 How to Measure Financial Growth 295

13.2 Risk and Return: Both Matter 298

13.3 Correlation and Industry Structure 300

13.4 The Riskiness of a Portfolio 306

13.5 Balancing Risk and Return 310

13.6 Controlling Risk with TBs 312

Chapter 14 Investigating Relationships between Business Variables 319

14.1 Measuring Association with Correlation 320

14.2 Looking at Complex Relationships 324

14.3 Interpreting Correlations 328

14.4 What Is Autocorrelation? 331

14.5 Untangling Relationships with Partial Correlation 334

Chapter 15 Describing the Effect of a Business Input: Linear Regression 341

15.1 Linear Relationships 342

15.2 The Line of Best Fit 344

15.3 Computing the Least Squares Line 347

15.4 The Regression Model 351

15.5 How Reliable Is the Regression Line? 354

Chapter 16 The Reliability of Regression-Based Decisions 365

16.1 Three Kinds of Questions that Regression Answers 366

16.2 Estimating the Effect of a Change 369

16.3 Estimating the Trend Mean 370

16.4 Prediction 373

16.5 Prediction Errors and What They Tell You 374

Chapter 17 Multicausal Relationships and Multiple Regression 387

17.1 Multilinear Relationships 390

17.2 Multiple Regression 393

17.3 Model Assessment 400

17.4 Prediction and Trend Estimation 404

Chapter 18 Product Features, Nonlinear Relationships, and Market Segments 413

18.1 Accounting for Yes–No Features 415

18.2 Quadratic Relationships 417

18.3 Quadratic Regression 421

18.4 Allowing for Segments and Groups 425

18.5 Automatic Model Selection 429

Chapter 19 Analyzing Data That Is Collected Regularly Over Time 437

19.1 Measuring Growth and Seasonality 439

19.2 How Is the Growth Rate Changing? 443

19.3 Seasonally Adjusting Data 445

19.4 Delayed Effects 448

19.5 Predicting the Future (Using Autoregression) 453

Chapter 20 Extending Regression Models: The Sky Is the Limit 461

20.1 Inputs That Have Varying Effects: Interactions 462

20.2 Inputs That Have Proportional Impacts 470

20.3 Case Study: How Effective Are Catalog Mail-Outs? 474

20.4 More on Time Series 478

Index 485