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Best Fit Lines & Curves: And Some Mathe-Magical Transformations [Hardback]

(Oregon State University, Botany & Plant Pathology, USA)
  • Formāts: Hardback, 530 pages, height x width: 234x156 mm, weight: 910 g, 227 Tables, black and white; 225 Line drawings, black and white
  • Sērija : Working Guides to Estimating & Forecasting
  • Izdošanas datums: 10-Sep-2018
  • Izdevniecība: Routledge
  • ISBN-10: 1138065005
  • ISBN-13: 9781138065000
  • Hardback
  • Cena: 100,22 €
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  • Formāts: Hardback, 530 pages, height x width: 234x156 mm, weight: 910 g, 227 Tables, black and white; 225 Line drawings, black and white
  • Sērija : Working Guides to Estimating & Forecasting
  • Izdošanas datums: 10-Sep-2018
  • Izdevniecība: Routledge
  • ISBN-10: 1138065005
  • ISBN-13: 9781138065000
Best Fit Lines and Curves, and Some Mathe-Magical Transformations (Volume III of the Working Guides to Estimating & Forecasting series) concentrates on techniques for finding the Best Fit Line or Curve to some historical data allowing us to interpolate or extrapolate the implied relationship that will underpin our prediction. A range of simple Moving Measures are suggested to smooth the underlying trend and quantify the degree of noise or scatter around that trend. The advantages and disadvantages are discussed and a simple way to offset the latent disadvantage of most Moving Measure Techniques is provided.

Simple Linear Regression Analysis, a more formal numerical technique that calculates the line of best fit subject to defined goodness of fit criteria. Microsoft Excel is used to demonstrate how to decide whether the line of best fit is a good fit, or just a solution in search of some data. These principles are then extended to cover multiple cost drivers, and how we can use them to quantify 3-Point Estimates.

With a deft sleight of hand, certain commonly occurring families of non-linear relationships can be transformed mathe-magically into linear formats, allowing us to exploit the powers of Regression Analysis to find the Best Fit Curves. The concludes with an exploration of the ups and downs of seasonal data (Time Series Analysis). 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.

Recenzijas

"In the Working Guides to Estimating and Forecasting Alan has managed to capture the full spectrum of relevant topics with simple explanations, practical examples and academic rigor, while injecting humour into the narrative." Dale Shermon, Chairman, Society of Cost Analysis and Forecasting (SCAF).

"If estimating has always baffled you, this innovative well illustrated and user friendly book will prove a revelation to its mysteries. To confidently forecast, minimise risk and reduce uncertainty we need full disclosure into the science and art of estimating. Thankfully, and at long last the "Working Guides to Estimating & Forecasting" are exactly that, full of practical examples giving clarity, understanding and validity to the techniques. These are comprehensive step by step guides in understanding the principles of estimating using experientially based models to analyse the most appropriate, repeatable, transparent and credible outcomes. Each of the five volumes affords a valuable tool for both corporate reference and an outstanding practical resource for the teaching and training of this elusive and complex subject. I wish I had access to such a thorough reference when I started in this discipline over 15 years ago, I am looking forward to adding this to my library and using it with my team." - Tracey L Clavell, Head of Estimating & Pricing, BAE Systems Australia

"At last, a comprehensive compendium on these engineering math subjects, essential to both the new and established "cost engineer"! As expected the subjects are presented with the authors usual wit and humour on complex and daunting "mathematically challenging" subjects. As a professional trainer within the MOD Cost Engineering community trying to embed this into my students, I will be recommending this series of books as essential bedtime reading." - Steve Baker, Senior Cost Engineer, DE&S MOD

"Alan has been a highly regarded member of the Cost Estimating and forecasting profession for several years. He is well known for an ability to reduce difficult topics and cost estimating methods down to something that is easily digested. As a master of this communication he would most often be found providing training across the cost estimating and forecasting tools and at all levels of expertise. With this 5-volume set, Working Guides to Estimating and Forecasting, Alan has brought his normal verbal training method into a written form. Within their covers Alan steers away from the usual dry academic script into establishing an almost 1:1 relationship with the reader. For my money a recommendable read for all levels of the Cost Estimating and forecasting profession and those who simply want to understand what is in the blackbox just a bit more." - Prof Robert Mills, Margin Engineering, Birmingham City University. MACOSTE, SCAF, ICEAA.

"Finally, a book to fill the gap in cost estimating and forecasting! Although other publications exist in this field, they tend to be light on detail whilst also failing to cover many of the essential aspects of estimating and forecasting. Jones covers all this and more from both a theoretical and practical point of view, regularly drawing on his considerable experience in the defence industry to provide many practical examples to support his comments. Heavily illustrated throughout, and often presented in a humorous fashion, this is a must read for those who want to understand the importance of cost estimating within the broader field of project management." - Dr Paul Blackwell, Lecturer in Management of Projects, The University of Manchester, UK.

"Alan Jones provides a useful guidebook and navigation aid for those entering the field of estimating as well as an overview for more experienced practitioners. His humorous asides supplement a thorough explanation of techniques to liven up and illuminate an area which has little attention in the literature, yet is the basis of robust project planning and successful delivery. Alans talent for explaining the complicated science and art of estimating in practical terms is testament to his knowledge of the subject and to his experience in teaching and training." - Therese Lawlor-Wright, Principal Lecturer in Project Management at the University of Cumbria

"Alan Jones has created an in depth guide to estimating and forecasting that I have not seen historically. Anyone wishing to improve their awareness in this field should read this and learn from the best." Richard Robinson, Technical Principal for Estimating, Mott MacDonald

"The book series of Working Guides to Estimating and Forecasting is an essential read for students, academics and practitioners who interested in developing a good understanding of cost estimating and forecasting from real-life perspectives". Professor Essam Shehab, Professor of Digital Manufacturing and Head of Cost Engineering, Cranfield University, UK.

"In creating the Working Guides to Estimating and Forecasting, Alan has captured the core approaches and techniques required to deliver robust and reliable estimates in a single series. Some of the concepts can be challenging, however, Alan has delivered them to the reader in a very accessible way that supports lifelong learning. Whether you are an apprentice, academic or a seasoned professional, these working guides will enhance your ability to understand the alternative approaches to generating a well-executed, defensible estimate, increasing your ability to support competitive advantage in your organisation." - Professor Andrew Langridge, Royal Academy of Engineering Visiting Professor in Whole Life Cost Engineering and Cost Data Management, University of Bath, UK.

"Alan Joness "Working Guides to Estimating and Forecasting" provides an excellent guide for all levels of cost estimators from the new to the highly experienced. Not only does he cover the underpinning good practice for the field, his books will take you on a journey from cost estimating basics through to how estimating should be used in manufacturing the future reflecting on a whole life cycle approach. He has written a must-read book for anyone starting cost estimating as well as for those who have been doing estimates for years. Read this book and learn from one of the best." - Linda Newnes, Professor of Cost Engineering, University of Bath, UK.

List of Figures xv
List of Tables xxi
Foreword xxxi
1 Introduction and objectives 1(14)
1.1 Why write this book? Who might find it useful? Why five volumes?
1(1)
1.1.1 Why write this series? Who might find it useful?
1(1)
1.1.2 Why five volumes?
2(1)
1.2 Features you'll find in this book and others in this series
2(5)
1.2.1
Chapter context
3(1)
1.2.2 The lighter side (humour)
3(1)
1.2.3 Quotations
3(1)
1.2.4 Definitions
3(1)
1.2.5 Discussions and explanations with a mathematical slant for Formula-philes
4(1)
1.2.6 Discussions and explanations without a mathematical slant for Formula-phobes
5(1)
1.2.7 Caveat augur
5(1)
1.2.8 Worked examples
6(1)
1.2.9 Useful Microsoft Excel functions and facilities
6(1)
1.2.10 References to authoritative sources
7(1)
1.2.11
Chapter reviews
7(1)
1.3 Overview of chapters in this volume
7(1)
1.4 Elsewhere in the 'Working Guide to Estimating & Forecasting' series
8(5)
1.4.1 Volume I: Principles, Process and Practice of Professional Number Juggling
9(1)
1.4.2 Volume II: Probability, Statistics and Other Frightening Stuff
10(1)
1.4.3 Volume III: Best Fit Lines and Curves, and Some Mathe-Magical Transformations
11(1)
1.4.4 Volume IV Learning, Unlearning and Re-learning curves
11(1)
1.4.5 Volume V Risk, Opportunity, Uncertainty and Other Random Models
12(1)
1.5 Final thoughts and musings on this volume and series
13(1)
References
14(1)
2 Linear and nonlinear properties (!) of straight lines 15(30)
2.1 Basic linear properties
15(6)
2.1.1 Inter-relation between slope and intercept
18(1)
2.1.2 The difference between two straight lines is a straight line
19(2)
2.2 The Cumulative Value (nonlinear) property of a linear sequence
21(22)
2.2.1 The Cumulative Value of a Discrete Linear Function
21(5)
2.2.2 The Cumulative Value of a Continuous Linear Function
26(8)
2.2.3 Exploiting the Quadratic Cumulative Value of a straight line
34(9)
2.3
Chapter review
43(1)
Reference
44(1)
3 Trendsetting with some Simple Moving Measures 45(68)
3.1 Going all trendy: The could and the should
45(3)
3.1.1 When should we consider trend smoothing?
45(2)
3.1.2 When is trend smoothing not appropriate?
47(1)
3.2 Moving Averages
48(33)
3.2.1 Use of Moving Averages
49(1)
3.2.2 When not to use Moving Averages
49(1)
3.2.3 Simple Moving Average
50(4)
3.2.4 Weighted Moving Average
54(4)
3.2.5 Choice of Moving Average Interval: Is there a better way than guessing?
58(8)
3.2.6 Can we take the Moving Average of a Moving Average?
66(2)
3.2.7 A creative use for Moving Averages - A case of forward thinking
68(2)
3.2.8 Dealing with missing data
70(1)
3.2.9 Uncertainty Range around the Moving Average
71(10)
3.3 Moving Medians
81(4)
3.3.1 Choosing the Moving Median Interval
83(1)
3.3.2 Dealing with missing data
84(1)
3.3.3 Uncertainty Range around the Moving Median
84(1)
3.4 Other Moving Measures of Central Tendency
85(4)
3.4.1 Moving Geometric Mean
87(1)
3.4.2 Moving Harmonic Mean
87(1)
3.4.3 Moving Mode
88(1)
3.5 Exponential Smoothing
89(7)
3.5.1 An unfortunate dichotomy
89(3)
3.5.2 Choice of Smoothing Constant, or Choice of Damping Factor
92(2)
3.5.3 Uses for Exponential Smoothing
94(1)
3.5.4 Double and Triple Exponential Smoothing
95(1)
3.6 Cumulative Average and Cumulative Smoothing
96(14)
3.6.1 Use of Cumulative Averages
97(4)
3.6.2 Dealing with missing data
101(2)
3.6.3 Cumulative Averages with batch data
103(1)
3.6.4 Being slightly more creative - Cumulative Average on a sliding scale
103(2)
3.6.5 Cumulative Smoothing
105(5)
3.7
Chapter review
110(2)
References
112(1)
4 Simple and Multiple Linear Regression 113(98)
4.1 What is Regression Analysis?
113(9)
4.1.1 Least Squares Best Fit
115(5)
4.1.2 Two key sum-to-zero properties of Least Squares
120(2)
4.2 Simple Linear Regression
122(7)
4.2.1 Simple Linear Regression using basic Excel functions
123(2)
4.2.2 Simple Linear Regression using the Data Analysis Add-in Tool Kit in Excel
125(2)
4.2.3 Simple Linear Regression using advanced Excel functions
127(2)
4.3 Multiple Linear Regression
129(9)
4.3.1 Using categorical data in Multiple Linear Regression
131(2)
4.3.2 Multiple Linear Regression using the Data Analysis Add-in Tool Kit in Excel
133(3)
4.3.3 Multiple Linear Regression using advanced Excel functions
136(2)
4.4 Dealing with Outliers in Regression Analysis?
138(2)
4.5 How good is our Regression? Six key measures
140(39)
4.5.1 Coefficient of Determination (R-Square): A measure of linearity?!
141(8)
4.5.2 F-Statistic: A measure of chance occurrence
149(7)
4.5.3 t-Statistics: Measures of Relevance or Significant Contribution
156(6)
4.5.4 Regression through the origin
162(9)
4.5.5 Role of common sense as a measure of goodness of fit
171(1)
4.5.6 Coefficient of Variation as a measure of tightness of fit
172(2)
4.5.7 White's Test for heteroscedasticity...and, by default, homoscedasticity
174(5)
4.6 Prediction and Confidence Intervals - Measures of uncertainty
179(14)
4.6.1 Prediction Intervals and Confidence Intervals: What's the difference?
180(2)
4.6.2 Calculating Prediction Limits and Confidence Limits for Simple Linear Regression
182(3)
4.6.3 Calculating Prediction Limits and Confidence Limits for Multi-Linear Regression
185(8)
4.7 Stepwise Regression
193(16)
4.7.1 Backward Elimination
197(4)
4.7.2 Forward Selection
201(5)
4.7.3 Backward or Forward Selection -Which should we use?
206(2)
4.7.4 Choosing the best model when we are spoilt for choice
208(1)
4.8
Chapter review
209(1)
References
210(1)
5 Linear transformation: Making bent lines straight 211(73)
5.1 Logarithms
212(10)
5.1.1 Basic properties of powers
213(3)
5.1.2 Basic properties of logarithms
216(6)
5.2 Basic linear transformation: Four Standard Function types
222(22)
5.2.1 Linear functions
223(2)
5.2.2 Logarithmic Functions
225(5)
5.2.3 Exponential Functions
230(3)
5.2.4 Power Functions
233(4)
5.2.5 Transforming with Microsoft Excel
237(5)
5.2.6 Is the transformation really better, or just a mathematical sleight of hand?
242(2)
5.3 Advanced linear transformation: Generalised Function types
244(13)
5.3.1 Transforming Generalised Logarithmic Functions
245(4)
5.3.2 Transforming Generalised Exponential Functions
249(1)
5.3.3 Transforming Generalised Power Functions
250(3)
5.3.4 Reciprocal Functions - Special cases of Generalised Power Functions
253(1)
5.3.5 Transformation options
254(3)
5.4 Finding the Best Fit Offset Constant
257(14)
5.4.1 Transforming Generalised Function Types into Standard Functions
259(1)
5.4.2 Using the Random-Start Bisection Method (Technique)
260(3)
5.4.3 Using Microsoft Excel's Goal Seek or Solver
263(8)
5.5 Straightening out Earned Value Analysis...or EVM Disintegration
271(8)
5.5.1 EVM terminology
271(3)
5.5.2 Taking a simpler perspective
274(5)
5.6 Linear transformation based on Cumulative Value Disaggregation
279(2)
5.7
Chapter review
281(2)
References
283(1)
6 Transforming Nonlinear Regression 284(96)
6.1 Simple Linear Regression of a linear transformation
284(16)
6.1.1 Simple Linear Regression with a Logarithmic Function
288(3)
6.1.2 Simple Linear Regression with an Exponential Function
291(7)
6.1.3 Simple Linear Regression with a Power Function
298(1)
6.1.4 Reversing the transformation of Logarithmic, Exponential and Power Functions
299(1)
6.2 Multiple Linear Regression of a multi-linear transformation
300(23)
6.2.1 Multi-linear Regression using linear and linearised Logarithmic Functions
302(10)
6.2.2 Multi-Linear Regression using linearised Exponential and Power Functions
312(11)
6.3 Stepwise Regression and multi-linear transformations
323(10)
6.3.1 Stepwise Regression by Backward Elimination with linear transformations
323(7)
6.3.2 Stepwise Regression by Forward Selection with linear transformations
330(3)
6.4 Is the Best Fit really the better fit?
333(4)
6.5 Regression of Transformed Generalised Nonlinear Functions
337(22)
6.5.1 Linear Regression of a Transformed Generalised Logarithmic Function
342(6)
6.5.2 Linear Regression of a Transformed Generalised Exponential Function
348(3)
6.5.3 Linear Regression of a Transformed Generalised Power Function
351(6)
6.5.4 Generalised Function transformations: Avoiding the pitfalls and tripwires
357(2)
6.6 Pseudo Multi-linear Regression of Polynomial Functions
359(19)
6.6.1 Offset Quadratic Regression of the Cumulative of a straight line
361(7)
6.6.2 Example of a questionable Cubic Regression of three linear variables
368(10)
6.7
Chapter review
378(1)
References
379(1)
7 Least Squares Nonlinear Curve Fitting without the logs 380(27)
7.1 Curve Fitting by Least Squares...without the logarithms
381(25)
7.1.1 Fitting data to Discrete Probability Distributions
381(10)
7.1.2 Fitting data to Continuous Probability Distributions
391(8)
7.1.3 Revisiting the Gamma Distribution Regression
399(7)
7.2
Chapter review
406(1)
Reference
406(1)
8 The ups and downs of Time Series Analysis 407(63)
8.1 The bits and bats...and buts of a Time Series
408(3)
8.1.1 Conducting a Time Series Analysis
411(1)
8.2 Alternative Time Series Models
411(4)
8.2.1 Additive/Subtractive Time Series Model
412(1)
8.2.2 Multiplicative Time Series Model
413(2)
8.3 Classical Decomposition: Determining the underlying trend
415(22)
8.3.1 See-Saw...Regression flaw?
416(4)
8.3.2 Moving Average Seasonal Smoothing
420(2)
8.3.3 Cumulative Average Seasonal Smoothing
422(2)
8.3.4 What happens when our world is not perfect? Do any of these trends work?
424(6)
8.3.5 Exponential trends and seasonal funnels
430(6)
8.3.6 Meandering trends
436(1)
8.4 Determining the seasonal variations by Classical Decomposition
437(6)
8.4.1 The Additive/Subtractive Model
438(2)
8.4.2 The Multiplicative Model
440(3)
8.5 Multi-Linear Regression: A holistic approach to Time Series?
443(18)
8.5.1 The Additive/Subtractive Linear Model
444(5)
8.5.2 The Additive/Subtractive Exponential Model
449(3)
8.5.3 The Multiplicative Linear Model
452(4)
8.5.4 The Multiplicative Exponential Model
456(4)
8.5.5 Multi-Linear Regression: Reviewing the options to make an informed decision
460(1)
8.6 Excel Solver technique for Time Series Analysis
461(7)
8.6.1 The Perfect World scenario
462(3)
8.6.2 The Real World scenario
465(3)
8.6.3 Wider examples of the Solver technique
468(1)
8.7
Chapter review
468(1)
Reference
469(1)
Glossary of estimating and forecasting terms 470(19)
Legend for Microsoft Excel Worked Example Tables in Greyscale 489(2)
Index 491
Alan R. Jones is Principal Consultant at Estimata Limited, aconsultancy service specialising in Estimating Skills Training. He is a Certified Cost Estimator/Analyst (US) and Certified Cost Engineer (CCE) (UK). Prior to setting up his own business, he enjoyed a 40-year career in the UK aerospace and defence industry as an estimatorAlan is a Fellow of the Association of Cost Engineers and a member of the International Cost Estimating and Analysis Association. Historically (some four decades ago), Alan was a graduate in Mathematics from Imperial College of Science and Technology in London, and was an MBA Prize-winner at the Henley Management College.