MARC Record from marc_columbia

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010 $a 2018000703
035 $a(OCoLC)on1019833988
035 $a(NNC)15128815
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020 $a9781351661478$q(pdf)
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020 $a9781351661461$q(epub)
020 $a1351661469
020 $a9781315160092$q(eBook)
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020 $z9781138064973 (hardback : alk. paper)
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024 7 $a10.4324/9781315160092$2doi
035 $a(OCoLC)1019833988$z(OCoLC)1053981313$z(OCoLC)1055694815$z(OCoLC)1065348092
037 $a9781351661461$bIngram Content Group
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072 7 $aBUS$x082000$2bisacsh
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082 00 $a658.3/124$223
049 $aZCUA
100 1 $aJones, Alan$q(Alan R.),$d1953-$eauthor.
245 10 $aLearning, unlearning and re-learning curves /$cAlan Jones.
264 1 $aAbingdon, Oxon ;$aNew York, NY :$bRoutledge,$c2018.
300 $a1 online resource.
336 $atext$btxt$2rdacontent
337 $acomputer$bn$2rdamedia
338 $aonline resource$bnc$2rdacarrier
490 0 $aWorking guides to estimating & forecasting ;$vvolume 4
504 $aIncludes bibliographical references and index.
588 $aDescription based on print version record and CIP data provided by publisher.
505 0 $aCover; 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
505 8 $a1.2.7 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
505 8 $a1.4.5 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!
505 8 $a2.3 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
505 8 $a2.4.4 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
520 3 $aLearning, 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.
650 0 $aLearning curve (Industrial engineering)
650 0 $aIndustrial productivity$xStatistical methods.
650 6 $aCourbe d'apprentissage (Génie industriel)
650 6 $aProductivité$xMéthodes statistiques.
650 7 $aBUSINESS & ECONOMICS$xIndustrial Management.$2bisacsh
650 7 $aBUSINESS & ECONOMICS$xManagement.$2bisacsh
650 7 $aBUSINESS & ECONOMICS$xManagement Science.$2bisacsh
650 7 $aBUSINESS & ECONOMICS$xOrganizational Behavior.$2bisacsh
650 7 $aIndustrial productivity$xStatistical methods.$2fast$0(OCoLC)fst00971540
650 7 $aLearning curve (Industrial engineering)$2fast$0(OCoLC)fst00994889
655 0 $aElectronic books.
655 4 $aElectronic books.
776 08 $iPrint version:$aJones, Alan (Alan R.), 1953- author.$tLearning, unlearning and re-learning curves$dAbingdon, Oxon ; New York, NY : Routledge, 2018$z9781138064973$w(DLC) 2017059089
856 40 $uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio15128815$zTaylor & Francis eBooks
852 8 $blweb$hEBOOKS