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Learning, unlearning and re-learning curves

Author: Alan Jones
Publisher: Abingdon, Oxon ; New York, NY : Routledge, 2018.
Series: Working guides to estimating & forecasting, volume 4
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
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 various models and examines the key properties that Estimators and Forecasters can exploit. A discussion about Learning  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Jones, Alan (Alan R.), 1953- author.
Learning, unlearning and re-learning curves
Abingdon, Oxon ; New York, NY : Routledge, 2018
(DLC) 2017059089
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Alan Jones
ISBN: 9781351661478 1351661477 9781351661461 1351661469 9781315160092 1315160099 9781351661454 1351661450
OCLC Number: 1019833988
Description: 1 online resource.
Contents: 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
Series Title: Working guides to estimating & forecasting, volume 4
Responsibility: Alan Jones.

Abstract:

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 various models and examines the key properties that Estimators and Forecasters can exploit. A discussion about Learning Curve Cost Drivers leads to the consideration of a little used but very powerful technique of Learning Curve modelling called Segmentation, which looks at an organisation's complex learning curve as the product of multiple shallower learning curves. Perhaps the biggest benefit is that it simplifies the calculations in Microsoft Excel where there is a change in the rate of learning observed or expected. The same technique can be used to model and calibrate discontinuities in the learning process that result in setbacks and uplifts in time or cost. This technique is compared with other, better known techniques such as Anderlohr's. Equivalent Unit Learning is another, relative new technique that can be used alongside traditional completed unit learning to give an early warning of changes in the rates of learning. Finally, a Learning Curve can be exploited to estimate the penalty of collaborative working across multiple partners. Supported by a wealth of figures and tables, this is a valuable resource for estimators, engineers, accountants, project risk specialists, as well as students of cost engineering.

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