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xiv | |
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xvi | |
Foreword |
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xix | |
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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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2 | (1) |
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1.1.1 Why write this series? Who might find it useful? |
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2 | (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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3 | (4) |
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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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4 | (1) |
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1.2.5 Discussions and explanations with a mathematical slant for Formula-philes |
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5 | (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 faculties |
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7 | (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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9 | (2) |
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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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11 | (1) |
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1.4.5 Volume V: Risk, Opportunity, Uncertainty and Other Random Models |
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12 | (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 Methods, approaches, techniques and related terms |
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15 | (28) |
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2.1 What is the difference between a method, approach and technique? |
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15 | (1) |
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16 | (2) |
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2.3 Estimating Approaches |
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18 | (5) |
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19 | (1) |
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20 | (2) |
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22 | (1) |
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23 | (11) |
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2.4.1 Analogical or Analogous Method |
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23 | (6) |
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29 | (3) |
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2.4.3 `Trusted Source' Method |
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32 | (1) |
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2.4.4 Methods that are arguably not methods (in their own right) |
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32 | (2) |
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2.5 Estimating Techniques |
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34 | (1) |
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2.6 Estimating Procedures |
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34 | (2) |
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2.7 Combining Approaches and Methods |
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36 | (3) |
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2.7.1 Choice of Estimating Approach for a chosen Estimating Element |
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37 | (1) |
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2.7.2 Choice of Estimating Method for a chosen Estimating Approach |
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38 | (1) |
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2.7.3 Choice of Estimating Technique for a chosen Estimating Method |
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39 | (1) |
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39 | (4) |
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42 | (1) |
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3 Estimate TRACEability and health checks |
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43 | (31) |
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3.1 Basis of Estmiate, TRACEability and estimate maturity |
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43 | (3) |
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3.1.1 Building bridges between two estimates |
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45 | (1) |
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3.2 Estimate and Schedule Maturity Assessments (or health checks) |
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46 | (8) |
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3.2.1 Estimate Maturity Assessment (EMA) |
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46 | (5) |
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3.2.2 Schedule Maturity Assessment (SMA) |
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51 | (1) |
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3.2.3 Cost and Schedule Integration Maturity Assessment (CASIMA) |
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52 | (2) |
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3.3 Good Practice Spreadsheet Modelling (GPSM) |
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54 | (15) |
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3.3.1 Level of documentation (T, M) |
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55 | (1) |
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3.3.2 No hidden worksheets, columns or rows (T, M) |
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55 | (1) |
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3.3.3 Colour coded cells and worksheet tabs (U, S) |
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56 | (3) |
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3.3.4 Locked calculation cells and protected worksheets and workbooks (S) |
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59 | (1) |
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3.3.5 No hard-coded constants unless axiomatic (M) |
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59 | (2) |
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3.3.6 Left to Right and Top to Bottom readability flow (U) |
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61 | (2) |
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3.3.7 Avoid data generated by macros ... Unless there is a genuine benefit (S, T) |
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63 | (1) |
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3.3.8 Avoid Array Formulae (T, U, M) |
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63 | (1) |
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3.3.9 Avoid dynamic links to external data (S) |
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64 | (1) |
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3.3.10 Use Named Ranges for frequently used table arrays (M, U) |
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65 | (1) |
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3.3.11 Use full syntax within Excel (M) |
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66 | (1) |
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3.3.12 Break complex calculations into smaller simpler steps (T, M) |
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66 | (1) |
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3.3.13 Column and row alignment across worksheets (T) |
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66 | (1) |
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3.3.14 Unambiguous units of measure (U) |
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67 | (1) |
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3.3.15 Input data validation (U) |
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67 | (1) |
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3.3.16 Independent model verification and validation (S) |
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67 | (2) |
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3.4 Inherent Risk in Spreadsheets (IRiS) |
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69 | (3) |
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72 | (2) |
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73 | (1) |
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4 Primary and Secondary Drivers; Accuracy and precision |
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74 | (19) |
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4.1 Thank goodness for Juran and Pareto |
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74 | (1) |
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4.1.1 What's the drive behind the Drivers? |
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74 | (1) |
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75 | (2) |
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4.2.1 Internal and external Drivers |
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76 | (1) |
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77 | (1) |
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4.4 Practical issues with Drivers |
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78 | (5) |
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4.4.1 Sub-classification of Primary Drivers |
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81 | (1) |
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4.4.2 Avoid pseudo-drivers |
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82 | (1) |
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4.4.3 Things are rarely black or white |
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83 | (1) |
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4.5 Accuracy and precision of Primary and Secondary Drivers |
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83 | (5) |
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4.5.1 Accuracy, precision and Drivers -- A Pareto perspective |
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86 | (1) |
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4.5.2 Cone of Uncertainty |
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86 | (2) |
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4.6 3-Point Estimates as a measure of relative accuracy and uncertainty |
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88 | (1) |
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4.7 Precision as an expression of appropriate or inappropriate exactness |
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89 | (2) |
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91 | (2) |
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92 | (1) |
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5 Factors, Rates, Ratios and estimating by analogy |
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93 | (42) |
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93 | (3) |
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94 | (1) |
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5.1.2 The views of others |
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95 | (1) |
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5.1.3 Underlying Linear Relationship |
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96 | (1) |
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96 | (5) |
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101 | (4) |
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105 | (1) |
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5.5 Dealing with multiple Rates, Factors (and Ratios) |
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106 | (17) |
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5.5.1 Anomalous analogies |
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107 | (2) |
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5.5.2 Analogies with an additive model |
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109 | (7) |
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5.5.3 Analogies with a multiplicative model |
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116 | (7) |
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5.6 Sensitivity Analysis on Factors, Rates and Ratios |
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123 | (10) |
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5.6.1 Choosing a Sensitivity Range quantitatively |
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123 | (2) |
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5.6.2 Choosing a Sensitivity Range around a measure of Central Tendency |
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125 | (4) |
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5.6.3 The triangulation option |
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129 | (1) |
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5.6.4 Choosing a Sensitivity Range around a High-end or Low-end Metric |
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129 | (1) |
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5.6.5 Choosing a Sensitivity Range when all else fails |
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130 | (3) |
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133 | (2) |
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134 | (1) |
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6 Data normalisation - Levelling the playing field |
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135 | (63) |
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6.1 Classification of data sources - Primary, Secondary and Tertiary Data |
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137 | (4) |
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137 | (1) |
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138 | (1) |
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139 | (2) |
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141 | (1) |
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6.2 Types of normalisation Methods and Techniques |
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141 | (1) |
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6.3 Normalisation can be a multi-dimensional problem |
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142 | (11) |
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143 | (1) |
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6.3.2 Volume, quantity or throughput related -- Economies of Scale |
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143 | (2) |
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6.3.3 Scale conversion - Fixed and Variable Factors |
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145 | (2) |
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6.3.4 Date or time related |
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147 | (1) |
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148 | (2) |
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6.3.6 Key groupings - Role related |
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150 | (1) |
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6.3.7 Scope related (subjective) |
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151 | (2) |
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6.3.8 Complexity -- Judgement related (subjective) |
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153 | (1) |
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6.4 The estimator as a time traveller |
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153 | (25) |
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6.4.1 Use of time-based indices `Now and Then' |
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154 | (5) |
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6.4.2 Time-based Weighted Indices |
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159 | (6) |
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6.4.3 Time-based Chain-linked Weighted Indices |
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165 | (4) |
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6.4.4 The doubling rule for escalation |
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169 | (3) |
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6.4.5 Composite Index: Is that not just a Weighted Index by another name? |
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172 | (1) |
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6.4.6 Using the appropriate appropriation approach |
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173 | (5) |
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6.4.7 Use of time as an indicator of other changes |
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178 | (1) |
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6.5 Discounted Cash Flow -- Normalising investment opportunities |
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178 | (14) |
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6.5.1 Discounted Cash Flow -- A form of time travel for accountants |
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178 | (3) |
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6.5.2 Net Present Value (NPV) |
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181 | (7) |
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6.5.3 Internal Rate of Return (IRR) |
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188 | (3) |
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191 | (1) |
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6.5.5 Strengths and weaknesses of different DCF techniques |
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191 | (1) |
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6.6 Special types of formulaic normalisation techniques |
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192 | (1) |
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6.7 Layering of normalisation for differences in analogies |
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193 | (3) |
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196 | (2) |
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197 | (1) |
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7 Pseudo-quantitative qualitative estimating techniques |
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198 | (8) |
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198 | (2) |
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7.2 Driver Cross-Impact Analysis |
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200 | (4) |
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7.3 A brief word or two about solution optimisation |
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204 | (1) |
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205 | (1) |
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205 | (1) |
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8 Benford's Law as a potential measure of cost bias |
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206 | (9) |
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8.1 Scale Invariance of Benford's Law |
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210 | (2) |
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8.2 Potential use of Benford's Law in estimating |
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212 | (2) |
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214 | (1) |
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214 | (1) |
Glossary of estimating and forecasting terms |
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215 | (19) |
Legend for Microsoft Excel Worked Example Tables in Greyscale |
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234 | (1) |
Index |
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235 | |