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xiii | |
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xviii | |
Foreword |
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xxii | |
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1 Introduction and objectives |
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1 | (14) |
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1.1 Why write this book? Who might find it useful? Why five volumes? |
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1 | (1) |
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1.1.1 Why write this series? Who might find it useful? |
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1 | (1) |
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2 | (1) |
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1.2 Features you'll find in this book and others in this series |
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2 | (5) |
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3 | (1) |
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1.2.2 The lighter side (humour) |
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3 | (1) |
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3 | (1) |
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3 | (1) |
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1.2.5 Discussions and explanations with a mathematical slant for Formula-philes |
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4 | (1) |
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1.2.6 Discussions and explanations without a mathematical slant for Formula-phobes |
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5 | (1) |
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6 | (1) |
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6 | (1) |
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1.2.9 Useful Microsoft Excel functions and facilities |
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6 | (1) |
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1.2.10 References to authoritative sources |
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7 | (1) |
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7 | (1) |
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1.3 Overview of chapters in this volume |
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7 | (1) |
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1.4 Elsewhere in the `Working Guide to Estimating & Forecasting' series |
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8 | (5) |
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1.4.1 Volume I: Principles, Process and Practice of Professional Number Juggling |
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9 | (1) |
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1.4.2 Volume II: Probability, Statistics and Other Frightening Stuff |
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10 | (1) |
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1.4.3 Volume III: Best Fit Lines and Curves, and Some Mathe-Magical Transformations |
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11 | (1) |
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1.4.4 Volume IV: Learning, Unlearning and Re-Learning Curves |
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12 | (1) |
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1.4.5 Volume V: Risk, Opportunity, Uncertainty and Other Random Models |
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13 | (1) |
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1.5 Final thoughts and musings on this volume and series |
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13 | (2) |
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14 | (1) |
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2 Norden-Rayleigh Curves for solution development |
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15 | (80) |
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2.1 Norden-Rayleigh Curves: Who, what, where, when and why? |
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15 | (15) |
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2.1.1 Probability Density Function and Cumulative Distribution Function |
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17 | (1) |
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18 | (3) |
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2.1.3 How does a Norden-Rayleigh Curve differ from the Rayleigh Distribution? |
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21 | (5) |
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2.1.4 Some practical limitations of the Norden-Rayleigh Curve |
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26 | (4) |
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2.2 Breaking the Norden-Rayleigh `Rules' |
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30 | (19) |
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2.2.1 Additional objectives: Phased development (or the `camelling') |
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30 | (1) |
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2.2.2 Correcting an overly optimistic view of the problem complexity: The Square Rule |
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31 | (4) |
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2.2.3 Schedule slippage due to resource ramp-up delays: The Pro Rata Product Rule |
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35 | (1) |
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2.2.4 Schedule slippage due to premature resource reduction |
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36 | (13) |
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2.3 Beta Distribution: A practical alternative to Norden-Rayleigh |
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49 | (8) |
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2.3.1 PERT-Beta Distribution: A viable alternative to Norden-Rayleigh? |
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55 | (1) |
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2.3.2 Resource profdes with Norden-Rayleigh Curves and Beta Distribution PDFs |
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56 | (1) |
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2.4 Triangular Distribution: Another alternative to Norden-Rayleigh |
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57 | (1) |
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2.5 Truncated Weibull Distributions and their Beta equivalents |
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58 | (5) |
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2.5.1 Truncated Weibull Distributions for solution development |
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58 | (3) |
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2.5.2 General Beta Distributions for solution development |
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61 | (2) |
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2.6 Estimates to Completion with Norden-Rayleigh Curves |
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63 | (29) |
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2.6.1 Guess and Iterate Technique |
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64 | (4) |
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2.6.2 Norden-Rayleigh Curve fitting with Microsoft Excel Solver |
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68 | (5) |
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2.6.3 Linear transformation and regression |
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73 | (8) |
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2.6.4 Exploiting Weibull Distribution's double log linearisation constraint |
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81 | (9) |
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2.6.5 Estimates to Completion -- Review and conclusion |
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90 | (2) |
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92 | (3) |
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93 | (2) |
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3 Monte Carlo Simulation and other random thoughts |
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95 | (88) |
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3.1 Monte Carlo Simulation: Who, what, why, where, when and how |
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95 | (33) |
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3.1.1 Origins of Monte Carlo Simulation: Myth and mirth |
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95 | (1) |
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3.1.2 Relevance to estimators and planners |
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96 | (1) |
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3.1.3 Key principle: Input variables with an uncertain future |
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97 | (4) |
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3.1.4 Common pitfalls to avoid |
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101 | (2) |
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3.1.5 Is our Monte Carlo output normal? |
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103 | (9) |
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3.1.6 Monte Carlo Simulation: A model of accurate imprecision |
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112 | (4) |
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3.1.7 What if we don't know what the true Input Distribution Functions are? |
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116 | (12) |
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3.2 Monte Carlo Simulation and correlation |
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128 | (19) |
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3.2.1 Independent random uncertain events -- How real is that? |
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128 | (3) |
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3.2.2 Modelling semi-independent uncertain events (bees and hedgehogs) |
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131 | (4) |
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3.2.3 Chain-Linked Correlation models |
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135 | (5) |
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3.2.4 Hub-Linked Correlation models |
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140 | (3) |
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3.2.5 Using a Hub-Linked model to drive a background isometric correlation |
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143 | (1) |
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3.2.6 Which way should we go? |
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143 | (3) |
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3.2.7 A word of warning about negative correlation in Monte Carlo Simulation |
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146 | (1) |
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3.3 Modelling and analysis of Risk, Opportunity and Uncertainty |
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147 | (30) |
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3.3.1 Sorting the wheat from the chaff |
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149 | (2) |
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3.3.2 Modelling Risk Opportunity and Uncertainty in a single model |
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151 | (7) |
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3.3.3 Mitigating Risks, realising Opportunities and contingency planning |
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158 | (4) |
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3.3.4 Getting our Risks, Opportunities and Uncertainties in a tangle |
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162 | (3) |
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3.3.5 Dealing with High Probability Risks |
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165 | (1) |
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3.3.6 Beware of False Prophets: Dealing with Low Probability High Impact Risks |
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166 | (4) |
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3.3.7 Using Risk or Opportunity to model extreme values of Uncertainty |
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170 | (1) |
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3.3.8 Modelling Probabilities of Occurrence |
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171 | (3) |
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3.3.9 Other random techniques for evaluating Risk, Opportunity and Uncertainty |
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174 | (3) |
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3.4 ROU Analysis: Choosing appropriate values with confidence |
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177 | (4) |
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3.4.1 Monte Carlo Risk and Opportunity Analysis is fundamentally flawed! |
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178 | (3) |
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181 | (2) |
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182 | (1) |
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4 Risk, Opportunity and Uncertainty: A holistic perspective |
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183 | (18) |
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4.1 Top-down Approach to Risk, Opportunity and Uncertainty |
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183 | (6) |
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183 | (2) |
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4.1.2 Marching Army Technique: Cost-schedule related variability |
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185 | (1) |
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4.1.3 Assumption Uplift Factors: Cost variability independent of schedule variability |
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186 | (2) |
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4.1.4 Lateral Shift Factors: Schedule variability independent of cost variability |
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188 | (1) |
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4.1.5 An integrated Top-down Approach |
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189 | (1) |
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4.2 Bridging into the unknown: Slipping and Sliding Technique |
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189 | (7) |
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4.3 Using an Estimate Maturity Assessment as a guide to ROU maturity |
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196 | (2) |
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198 | (3) |
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200 | (1) |
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5 Factored Value Technique for Risks and Opportunities |
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201 | (6) |
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201 | (2) |
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5.2 A slightly better way |
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203 | (1) |
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204 | (1) |
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205 | (2) |
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206 | (1) |
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5 Introduction to Critical Path and Schedule Risk Analysis |
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207 | (15) |
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6.1 What is Critical Path Analysis? |
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207 | (3) |
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6.2 Finding a Critical Path using Binary Activity Paths in Microsoft Excel |
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210 | (3) |
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6.3 Using Binary Paths to find the latest start and finish times, and float |
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213 | (2) |
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6.4 Using a Critical Path to Manage Cost and Schedule |
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215 | (3) |
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6.5 Modelling variable Critical Paths using Monte Carlo Simulation |
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218 | (2) |
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220 | (2) |
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221 | (1) |
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7 Finally, after a long wait ... Queueing Theory |
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222 | (42) |
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7.1 Types of queues and service discipline |
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224 | (3) |
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227 | (4) |
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7.3 Simple single channel queues (M/M/1 and M/G/1) |
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231 | (11) |
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7.3.1 Example of Queueing Theory in action M/M/1 or M/G/1 |
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236 | (6) |
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7.4 Multiple channel queues (M/M/c) |
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242 | (5) |
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7.4.1 Example of Queueing Theory in action M/M/c or M/G/c |
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243 | (4) |
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7.5 How do we spot a Poisson Process? |
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247 | (10) |
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7.6 When is Weibull viable? |
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257 | (2) |
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7.7 Can we have a Poisson Process with an increasing/decreasing trend? |
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259 | (3) |
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262 | (2) |
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263 | (1) |
Epilogue |
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264 | (1) |
Glossary of estimating and forecasting terms |
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265 | (19) |
Legend for Microsoft Excel Worked Example Tables in Creyscale |
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284 | (1) |
Index |
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285 | |