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Learning, unlearning and re-learning curves /
Learning, Unlearning and Re-learning Curves (Volume IV of the Working Guides to Estimating & Forecasting series) focuses in on Learning Curves, and the various tried and tested models of Wright, Crawford, DeJong, Towill-Bevis and others. It explores the differences and similarities between the v...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Abingdon, Oxon ; New York, NY :
Routledge,
2018.
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| Colección: | Working guides to estimating & forecasting ;
volume 4 |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- Cover; Title; Copyright; Dedication; Contents; List of Figures; List of Tables; Foreword; 1 Introduction and objectives; 1.1 Why write this book? Who might find it useful? Why five volumes?; 1.1.1 Why write this series? Who might find it useful?; 1.1.2 Why five volumes?; 1.2 Features you'll find in this book and others in this series; 1.2.1 Chapter context; 1.2.2 The lighter side (humour); 1.2.3 Quotations; 1.2.4 Definitions; 1.2.5 Discussions and explanations with a mathematical slant for Formula-philes; 1.2.6 Discussions and explanations without a mathematical slant for Formula-phobes
- 1.2.7 Caveat augur1.2.8 Worked examples; 1.2.9 Useful Microsoft Excel functions and facilities; 1.2.10 References to authoritative sources; 1.2.11 Chapter reviews; 1.3 Overview of chapters in this volume; 1.4 Elsewhere in the 'Working Guide to Estimating & Forecasting' series; 1.4.1 Volume I: Principles, Process and Practice of Professional Number Juggling; 1.4.2 Volume II: Probability, Statistics and Other Frightening Stuff; 1.4.3 Volume III: Best Fit Lines and Curves, and Some Mathe^Magical Transformations; 1.4.4 Volume IV: Learning, Unlearning and Re-Learning Curves
- 1.4.5 Volume V: Risk, Opportunity, Uncertainty and Other Random Models1.5 Final thoughts and musings on this volume and series; References; 2 Quantity-based Learning Curves; 2.1 A brief history of the Learning Curve as a formal relationship; 2.2 Two basic Learning Curve models (Wright and Crawford); 2.2.1 Wright Cumulative Average Learning Curve; 2.2.2 Crawford Unit Learning Curve; 2.2.3 Wright and Crawford Learning Curves compared; 2.2.4 What's so special about the doubling rule?; 2.2.5 Learning Curve regression
- What appears to be Wright, may in fact be wrong!
- 2.3 Variations on the basic Learning Curve models2.3.1 Dejong Unit Learning Curve; 2.3.2 Dejong-Wright Cumulative Average Hybrid Learning Curve; 2.3.3 Stanford-B Unit Learning Curve; 2.3.4 Stanford-Wright Cumulative Average Hybrid Learning Curve; 2.3.5 S-Curve Unit Learning Curve; 2.3.6 S-Curve-Wright Cumulative Average Hybrid Learning Curve; 2.4 Where and when to apply learning and how much?; 2.4.1 To what kind of task can a Learning Curve be applied?; 2.4.2 Additive and non-additive properties of Learning Curves; 2.4.3 Calibrating or measuring observed learning
- 2.4.4 What it we don't have any actuals? Rules of Thumb rates of learning2.5 Changing the rate of learning
- Breakpoints; 2.5.1 Dealing with a breakpoint in a Unit Learning Curve calculation; 2.5.2 Dealing with a breakpoint in a Cumulative Average Learning Curve calculation; 2.6 Learning Curves: Stepping up and stepping down; 2.6.1 Step-points in a Unit Learning Curve calculation; 2.6.2 Step-points in a Cumulative Average Learning Curve calculation; 2.7 Cumulative values of Crawford Unit Learning Curves; 2.7.1 Conway-Schultz Cumulative approximation; 2.7.2 Jones Cumulative approximation