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Probability, Statistics and Other Frightening Stuff [Kõva köide]

(Oregon State University, Botany & Plant Pathology, USA)
  • Formaat: Hardback, 500 pages, kõrgus x laius: 234x156 mm, kaal: 852 g, 130 Tables, black and white; 231 Line drawings, black and white
  • Sari: Working Guides to Estimating & Forecasting
  • Ilmumisaeg: 10-Sep-2018
  • Kirjastus: Routledge
  • ISBN-10: 113806503X
  • ISBN-13: 9781138065031
  • Formaat: Hardback, 500 pages, kõrgus x laius: 234x156 mm, kaal: 852 g, 130 Tables, black and white; 231 Line drawings, black and white
  • Sari: Working Guides to Estimating & Forecasting
  • Ilmumisaeg: 10-Sep-2018
  • Kirjastus: Routledge
  • ISBN-10: 113806503X
  • ISBN-13: 9781138065031

Probability, Statistics and Other Frightening Stuff (Volume II of the Working Guides to Estimating & Forecasting series) considers many of the commonly used Descriptive Statistics in the world of Estimating and Forecasting. It considers values consider representative of the ‘middle ground’ (Measures of Central Tendency), and the degree of data scatter (Measures of Dispersion and Shape) around the ‘middle ground’ values.

A number of Probability Distributions are discussed, where they might be used, and some fascinating and useful ‘rules of thumb’ or short-cut properties that Estimators and Forecasters can exploit in plying their trade. With the help of a ‘Correlation Chicken’, the concept of partial correlation is explained, and how the Estimator or Forecaster can exploit this in reflecting varying levels of independence and imperfect dependence between an output or predicted value (such as cost) and an input or predictor variable such as size.

Under the guise of "’Tails’ of the Unexpected" the book concludes with two chapters devoted to Hypothesis Testing (or knowing when to accept or reject the validity of an assumed estimating relationship), and a number of Statistically-based tests to help the Estimator to decide whether to include or exclude a data point as an ‘outlier’, one that appear not to be representative of that which the Estimator is tasked to produce. This is a valuable resource for estimators, engineers, accountants, project risk specialists as well as students of cost engineering.

Arvustused

"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 xvii
List of Tables xxii
Foreword xxvi
1 Introduction and objectives 1(14)
1.1 Why write this book? Who might find it useful? Why five volumes?
2(1)
1.1.1 Why write this series? Who might find it useful?
2(1)
1.1.2 Why five volumes?
2(1)
1.2 Features you'll find in this book and others in this series
3(4)
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
4(1)
1.2.5 Discussions and explanations with a mathematical slant for Formula-philes
5(1)
1.2.6 Discussions and explanations without a mathematical slant for Formula-phobes
5(1)
1.2.7 Caveat augur
6(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
10(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 Measures of Central Tendency: Means, Modes, Medians 15(52)
2.1 'S' is for shivers, statistics and spin
15(2)
2.1.1 Cutting through the mumbo-jumbo: What is or are statistics?
16(1)
2.1.2 Are there any types of statistics that are not 'Descriptive'?
17(1)
2.1.3 Samples, populations and the dreaded statistical bias
17(1)
2.2 Measures of Central Tendency
17(2)
2.2.1 What do we mean by 'Mean'?
18(1)
2.2.2 Can we take the average of an average?
19(1)
2.3 Arithmetic Mean - the Simple Average
19(11)
2.3.1 Properties of Arithmetic Means: A potentially unachievable value!
21(2)
2.3.2 Properties of Arithmetic Means: An unbiased representative value of the whole
23(2)
2.3.3 Why would we not want to use the Arithmetic Mean?
25(1)
2.3.4 Is an Arithmetic Mean useful where there is an upward or downward trend?
26(1)
2.3.5 Average of averages: Can we take the Arithmetic Mean of an Arithmetic Mean?
27(3)
2.4 Geometric Mean
30(11)
2.4.1 Basic rules and properties of a Geometric Mean
30(1)
2.4.2 When might we want to use a Geometric Mean?
31(2)
2.4.3 Finding a steady state rate of growth or decay with a Geometric Mean
33(6)
2.4.4 Using a Geometric Mean as a Cross-Driver Comparator
39(1)
2.4.5 Using a Geometric Mean with certain Non-Linear Regressions
39(1)
2.4.6 Average of averages: Can we take the Geometric Mean of a Geometric Mean?
40(1)
2.5 Harmonic Mean
41(7)
2.5.1 Surely estimators would never use the Harmonic Mean?
42(3)
2.5.2 Cases where the Harmonic Mean and the Arithmetic Mean are both inappropriate
45(1)
2.5.3 Average of averages: Can we take the Harmonic Mean of a Harmonic Mean?
45(3)
2.6 Quadratic Mean: Root Mean Square
48(3)
2.6.1 When would we ever use a Quadratic Mean?
48(3)
2.7 Comparison of Arithmetic, Geometric, Harmonic and Quadratic Means
51(1)
2.8 Mode
52(8)
2.8.1 When would we use the Mode instead of the Arithmetic Mean?
54(1)
2.8.2 What does it mean if we observe more than one Mode?
54(1)
2.8.3 What if we have two modes that occur at adjacent values?
55(1)
2.8.4 Approximating the theoretical Mode when there is no real observable Mode!
56(4)
2.9 Median
60(2)
2.9.1 Primary use of the Median
61(1)
2.9.2 Finding the Median
61(1)
2.10 Choosing a representative value: The 5-Ms
62(3)
2.10.1 Some properties of the 5-Ms
63(2)
2.11
Chapter review
65(1)
References
66(1)
3 Measures of Dispersion and Shape 67(58)
3.1 Measures of Dispersion or scatter around a central value
67(1)
3.2 Minimum, Maximum and Range
68(2)
3.3 Absolute Deviations
70(9)
3.3.1 Mean or Average Absolute Deviation (AAD)
71(2)
3.3.2 Median Absolute Deviation (MAD)
73(4)
3.3.3 Is there a Mode Absolute Deviation?
77(1)
3.3.4 When would we use an Absolute Deviation?
77(2)
3.4 Variance and Standard Deviation
79(20)
3.4.1 Variance and Standard Deviation - compensating for small samples
84(7)
3.4.2 Coefficient of Variation
91(2)
3.4.3 The Range Rule - is it myth or magic?
93(6)
3.5 Comparison of deviation-based Measures of Dispersion
99(2)
3.6 Confidence Levels, Limits and Intervals
101(5)
3.6.1 Open and Closed Confidence Level Ranges
104(2)
3.7 Quantiles: Quartiles, Quintiles, Deciles and Percentiles
106(9)
3.7.1 A few more words about Quartiles
109(3)
3.7.2 A few thoughts about Quintiles
112(1)
3.7.3 And a few words about Deciles
113(1)
3.7.4 Finally, a few words about Percentiles
114(1)
3.8 Other Measures of Shape: Skewness and Peakedness
115(8)
3.8.1 Measures of Skewness
116(4)
3.8.2 Measures of Peakedness or Flatness - Kurtosis
120(3)
3.9
Chapter review
123(1)
References
124(1)
4 Probability Distributions 125(130)
4.1 Probability
126(12)
4.1.1 Discrete Distributions
127(4)
4.1.2 Continuous Distributions
131(6)
4.1.3 Bounding Distributions
137(1)
4.2 Normal Distributions
138(9)
4.2.1 What is a Normal Distribution?
138(1)
4.2.2 Key properties of a Normal Distribution
139(4)
4.2.3 Where is the Normal Distribution observed? When can, or should, it be used?
143(2)
4.2.4 Probability Density Function and Cumulative Distribution Function
145(1)
4.2.5 Key stats and facts about the Normal Distribution
146(1)
4.3 Uniform Distributions
147(8)
4.3.1 Discrete Uniform Distributions
147(2)
4.3.2 Continuous Uniform Distributions
149(1)
4.3.3 Key properties of a Uniform Distribution
150(3)
4.3.4 Where is the Uniform Distribution observed? When can, or should, it be used?
153(1)
4.3.5 Key stats and facts about the Uniform Distribution
154(1)
4.4 Binomial and Bernoulli Distributions
155(7)
4.4.1 What is a Binomial Distribution?
155(1)
4.4.2 What is a Bernoulli Distribution?
156(1)
4.4.3 Probability Mass Function and Cumulative Distribution Function
157(2)
4.4.4 Key properties of a Binomial Distribution
159(2)
4.4.5 Where is the Binomial Distribution observed? When can, or should, it be used?
161(1)
4.4.6 Key stats and facts about the Binomial Distribution
161(1)
4.5 Beta Distributions
162(14)
4.5.1 What is a Beta Distribution?
162(2)
4.5.2 Probability Density Function and Cumulative Distribution Function
164(3)
4.5.3 Key properties of a Beta Distribution
167(2)
4.5.4 PERT-Beta or Project Beta Distributions
169(5)
4.5.5 Where is the Beta Distribution observed? When can, or should, it be used?
174(1)
4.5.6 Key stats and facts about the Beta Distribution
175(1)
4.6 Triangular Distributions
176(10)
4.6.1 What is a Triangular Distribution?
176(1)
4.6.2 Probability Density Function and Cumulative Distribution Function
176(2)
4.6.3 Key properties of a Triangular Distribution
178(7)
4.6.4 Where is the Triangular Distribution observed? When can, or should, it be used?
185(1)
4.6.5 Key stats and facts about the Triangular Distribution
185(1)
4.7 Lognormal Distributions
186(9)
4.7.1 What is a Lognormal Distribution?
186(3)
4.7.2 Probability Density Function and Cumulative Distribution Function
189(1)
4.7.3 Key properties of a Lognormal Distribution
190(3)
4.7.4 Where is the Lognormal Distribution observed? When can, or should, it be used?
193(1)
4.7.5 Key stats and facts about the Lognormal Distribution
194(1)
4.8 Weibull Distributions
195(12)
4.8.1 What is a Weibull Distribution?
195(1)
4.8.2 Probability Density Function and Cumulative Distribution Function
196(2)
4.8.3 Key properties of a Weibull Distribution
198(4)
4.8.4 Where is the Weibull Distribution observed? When can, or should, it be used?
202(3)
4.8.5 Key stats and facts about the Weibull Distribution
205(2)
4.9 Poisson Distributions
207(10)
4.9.1 What is a Poisson Distribution?
207(3)
4.9.2 Probability Mass Function and Cumulative Distribution Function
210(1)
4.9.3 Key properties of a Poisson Distribution
210(4)
4.9.4 Where is the Poisson Distribution observed? When can, or should, it be used?
214(2)
4.9.5 Key stats and facts about the Poisson Distribution
216(1)
4.10 Gamma and Chi-Squared Distributions
217(12)
4.10.1 What is a Gamma Distribution?
217(3)
4.10.2 What is a Chi-Squared Distribution?
220(1)
4.10.3 Probability Density Function and Cumulative Distribution Function
220(3)
4.10.4 Key properties of Gamma and Chi-Squared Distributions
223(3)
4.10.5 Where are the Gamma and Chi-Squared Distributions used?
226(2)
4.10.6 Key stats and facts about the Gamma and Chi-Squared Distributions
228(1)
4.11 Exponential Distributions
229(6)
4.11.1 What is an Exponential Distribution?
229(1)
4.11.2 Probability Density Function and Cumulative Distribution Function
229(1)
4.11.3 Key properties of an Exponential Distribution
230(3)
4.11.4 Where is the Exponential Distribution observed? When can, or should, it be used?
233(1)
4.11.5 Key stats and facts about the Exponential Distribution
234(1)
4.12 Pareto Distributions
235(15)
4.12.1 What is a Pareto Distribution?
235(1)
4.12.2 Probability Density Function and Cumulative Distribution Function
235(2)
4.12.3 The Pareto Principle: How does it fit in with the Pareto Distribution?
237(4)
4.12.4 Key properties of a Pareto Distribution
241(5)
4.12.5 Where is the Pareto Distribution observed? When can, or should, it be used?
246(3)
4.12.6 Key stats and facts about the Pareto Distribution
249(1)
4.13 Choosing an appropriate distribution
250(3)
4.14
Chapter review
253(1)
References
253(2)
5 Measures of Linearity, Dependence and Correlation 255(85)
5.1 Covariance
257(7)
5.2 Linear Correlation or Measures of Linear Dependence
264(20)
5.2.1 Pearson's Correlation Coefficient
264(6)
5.2.2 Pearson's Correlation Coefficient - key properties and limitations
270(9)
5.2.3 Correlation is not causation
279(2)
5.2.4 Partial Correlation: Time for some Correlation Chicken
281(1)
5.2.5 Coefficient of Determination
282(2)
5.3 Rank Correlation
284(27)
5.3.1 Spearman's Rank Correlation Coefficient
286(9)
5.3.2 If Spearman's Rank Correlation is so much trouble, why bother?
295(2)
5.3.3 Interpreting Spearman's Rank Correlation Coefficient
297(4)
5.3.4 Kendall's Tau Rank Correlation Coefficient
301(6)
5.3.5 If Kendall's Tau Rank Correlation is so much trouble, why bother?
307(4)
5.4 Correlation: What if you want to 'Push' it not 'Pull' it?
311(25)
5.4.1 The Pushy Pythagorean Technique or restricting the scatter around a straight line
312(5)
5.4.2 'Controlling Partner' Technique
317(5)
5.4.3 Equivalence of the Pushy Pythagorean and Controlling Partner Techniques
322(1)
5.4.4 'Equal Partners' Technique
323(5)
5.4.5 Copulas
328(8)
5.5
Chapter review
336(3)
References
339(1)
6 Tails of the unexpected (1): Hypothesis Testing 340(52)
6.1 Hypothesis Testing
341(3)
6.1.1 Tails of the unexpected
342(2)
6.2 Z-Scores and Z-Tests
344(12)
6.2.1 Standard Error
345(5)
6.2.2 Example: Z-Testing the Mean value of a Normal Distribution
350(2)
6.2.3 Example: Z-Testing the Median value of a Beta Distribution
352(4)
6.3 Student's t-Distribution and t-Tests
356(11)
6.3.1 Student's t-Distribution
356(3)
6.3.2 t-Tests
359(2)
6.3.3 Performing a t-Test in Microsoft Excel on a single sample
361(3)
6.3.4 Performing a t-Test in Microsoft Excel to compare two samples
364(3)
6.4 Mann-Whitney U-Tests
367(4)
6.5 Chi-Squared Tests or f-Tests
371(4)
6.5.1 Chi-Squared Distribution revisited
371(1)
6.5.2 Chi-Squared Test
371(4)
6.6 F-Distribution and F-Tests
375(5)
6.6.1 F-Distribution
375(2)
6.6.2 F-Test
377(1)
6.6.3 Primary use of the F-Distribution
377(3)
6.7 Checking for Normality
380(10)
6.7.1 Q-Q Plots
380(6)
6.7.2 Using a Chi-Squared Test for Normality
386(3)
6.7.3 Using the Jarque-Bera Test for Normality
389(1)
6.8
Chapter review
390(1)
References
391(1)
7 Tails of the unexpected (2): Outing the outliers 392(51)
7.1 Outing the outliers: Detecting and dealing with outliers
392(7)
7.1.1 Mitigation of Type I and Type II outlier errors
396(3)
7.2 Tukey Fences
399(9)
7.2.1 Tukey Slimline Fences - for larger samples and less tolerance of outliers?
407(1)
7.3 Chauvenet's Criterion
408(8)
7.3.1 Variation on Chauvenet's Criterion for small sample sizes (SSS)
413(1)
7.3.2 Taking a Q-Q perspective on Chauvenet's Criterion for small sample sizes (SSS)
414(2)
7.4 Peirce's Criterion
416(3)
7.5 Iglewicz and Hoaglin's MAD Technique
419(6)
7.6 Grubbs' Test
425(4)
7.7 Generalised Extreme Studentised Deviate (GESD)
429(1)
7.8 Dixon's Q-Test
430(2)
7.9 Doing the JB Swing - using Skewness and Excess Kurtosis to identify outliers
432(5)
7.10 Outlier tests - a comparison
437(3)
7.11
Chapter review
440(1)
References
440(3)
Glossary of estimating and forecasting terms 443(19)
Legend for Microsoft Excel Worked Example Tables in Greyscale 462(1)
Index 463
Alan R. Jones is Principal Consultant at Estimata Limited, an estimating consultancy service. He is a Certified Cost Estimator/Analyst (US) and Certified Cost Engineer (CCE) (UK). Prior to setting up his own business, he has enjoyed a 40-year career in the UK aerospace and defence industry as an estimator, culminating in the role of Chief Estimator at BAE Systems. Alan 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 (. . . that was slightly more recent, being only two decades ago). Oh, how time flies when you are enjoying yourself.