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.
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
