How to Measure Anything
by Douglas W. HubbardRent Book
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Summary
Author Biography
Table of Contents
| Preface | p. xi |
| Acknowledgments | p. xv |
| Measurement: The Solution Exists | |
| The Intangibles and the Challenge | p. 3 |
| An Intuitive Measurement Habit: Eratosthenes, Enrico, & Emily | p. 7 |
| How an Ancient Greek Measured the Size of Earth | p. 8 |
| Estimating: Be Like Fermi | p. 9 |
| Experiments: Not Just for Adults | p. 12 |
| Notes on What to Learn from Eratosthenes, Enrico, and Emily | p. 16 |
| The Illusion of Intangibles: Why Immeasurables Aren't | p. 19 |
| The Concept of Measurement | p. 20 |
| The Object of Measurement | p. 24 |
| The Methods of Measurement | p. 27 |
| Economic Objections to Measurement | p. 33 |
| The Broader Objection to the Usefulness of "Statistics" | p. 34 |
| Ethical Objections to Measurement | p. 36 |
| Toward a Universal Approach to Measurement | p. 39 |
| Before You Measure | |
| Clarifying the Measurement Problem | p. 43 |
| Getting the Language Right: What "Uncertainty" and "Risk" Really Mean | p. 45 |
| Examples of Clarification: Lessons for Business from, of All Places, Government | p. 47 |
| Calibrated Estimates: How Much Do You Know Now? | p. 53 |
| Testing Your Ability to Assess Odds | p. 53 |
| Calibration Exercise | p. 55 |
| Further Improvements on Calibration | p. 59 |
| Conceptual Obstacles to Calibration | p. 61 |
| The Effects of Calibration | p. 65 |
| Measuring Risk: Introduction to the Monte Carlo Simulation | p. 71 |
| An Example of the Monte Carlo Method and Risk | p. 74 |
| Tools and Other Resources for Monte Carlo Simulations | p. 79 |
| The Risk Paradox | p. 82 |
| Measuring the Value of Information | p. 85 |
| The Chance of Being Wrong and the Cost of Being Wrong: Expected Opportunity Loss | p. 86 |
| The Value of Information for Ranges | p. 89 |
| The Imperfect World: The Value of Partial Uncertainty Reduction | p. 92 |
| The Epiphany Equation: The Value of a Measurement Changes Everything | p. 95 |
| Summarizing Uncertainty, Risk, and Information Value: The First Measurements | p. 99 |
| Measurement Methods | |
| The Transition: From What to Measure to How to Measure | p. 103 |
| Tools of Observation: Introduction to the Instrument of Measurement | p. 105 |
| Decomposition | p. 109 |
| Secondary Research: Assuming You Weren't the First to Measure It | p. 113 |
| The Basic Methods of Observation: If One Doesn't Work, Try the Next | p. 115 |
| Measure Just Enough | p. 117 |
| Consider the Error | p. 119 |
| Choose and Design the Instrument | p. 124 |
| Sampling Reality: How Observing Some Things Tells Us about All Things | p. 127 |
| Building an Intuition for Random Sampling: The Jelly Bean Example | p. 132 |
| A Little About Little Samples: A Beer Brewer's Approach | p. 133 |
| The Easiest Sample Statistics Ever | p. 138 |
| A Biased Sample of Sampling Methods | p. 141 |
| Measure to the Threshold | p. 147 |
| Experiment | p. 151 |
| Seeing Relationships in the Data: An Introduction to Regression Modeling | p. 155 |
| Bayes: Adding to What You Know Now | p. 161 |
| Simple Bayesian Statistics | p. 161 |
| Using Your Natural Bayesian Instinct | p. 165 |
| Heterogeneous Benchmarking: A "Brand Damage" Application | p. 170 |
| Getting a Bit More Technical: Bayesian Inversion for Ranges | p. 174 |
| Beyond the Basics | |
| Preference and Attitudes: The Softer Side of Measurement | p. 183 |
| Observing Opinions, Values, and the Pursuit of Happiness | p. 183 |
| A Willingness to Pay: Measuring Value via Trade-offs | p. 187 |
| Putting It All on the Line: Quantifying Risk Tolerance | p. 192 |
| Quantifying Subjective Trade-offs: Dealing with Multiple Conflicting Preferences | p. 195 |
| Keeping the Big Picture in Mind: Profit Maximization versus Subjective Trade-offs | p. 199 |
| The Ultimate Measurement Instrument: Human Judges | p. 203 |
| Homo absurdus: The Weird Reasons behind Our Decisions | p. 204 |
| Getting Organized: A Performance Evaluation Example | p. 207 |
| Surprisingly Simple Linear Models | p. 208 |
| How to Standardize Any Evaluation: Rasch Models | p. 210 |
| Removing Human Inconsistency: The Lens Model | p. 214 |
| Panacea or Placebo?: Questionable Methods of Measurement | p. 219 |
| Comparing the Methods | p. 226 |
| New Measurement Instruments for Management | p. 229 |
| The Twenty-First-Century Tracker: Keeping Tabs with Technology | p. 229 |
| Measuring the World: The Internet as an Instrument | p. 232 |
| Prediction Markets: Wall Street Efficiency Applied to Measurements | p. 235 |
| A Universal Measurement Method: Applied Information Economics | p. 243 |
| Bringing the Pieces Together | p. 244 |
| Case: The Value of the System That Monitors Your Drinking Water | p. 248 |
| Case: Forecasting Fuel for the Marine Corps | p. 253 |
| Ideas for Getting Started: A Few Final Examples | p. 260 |
| Summarizing the Philosophy | p. 267 |
| Calibration Tests | p. 269 |
| Index | p. 279 |
| Table of Contents provided by Ingram. All Rights Reserved. |
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