Tables

2.1 Cumulative Distribution Function of a Standard Rayleigh Probability Distribution
2.2 Norden-Rayleigh Curves are Independent of the Time Scale
2.3 Stretching Time to Model the Optimistic View of Premature Resource Reduction
2.4 Determining the Marching Army Parameters
2.5 Determining the Marching Army Parameters – Alternative Model
2.6 Marching Army Parameters and Penalties
2.7 Marching Army Parameters and Penalties
2.8 Options for Modelling Premature Resource Reduction by Disaggregation (Bactrian Camel)
2.9 Summary of Cost and Schedule Penalties Using Disaggregation Technique
2.10 Calculation of the Modal Re-Positioning Parameters
2.11 Summary of Cost and Schedule Penalties Using the Modal Re-Positioning Technique
2.12 Best Fit Beta Distribution for a Truncated Rayleigh Distribution Using Excel's Solver
2.13 Solver Results for the Best Fit Beta Distribution for a Range of NRC Truncation Ratios
2.14 Triangular Distribution Approximation to a Norden-Rayleigh Curve
2.15 Options for Creating EACs for Norden-Rayleigh Curves
2.16 Solver Model Set-Up for Norden-Rayleigh Curve Forecast
2.17 Solver Model Results for Norden-Rayleigh Curve Forecast
2.18 Solver Model Setup tor Linear Transformation of a Norden-Rayleigh Curve
2.19 Solver Model Results for Linear Transformation of a Norden-Rayleigh Curve (1)
2.20 Solver Model Results for Linear Transformation of a Norden-Rayleigh Curve (2)
2.21 Solver Outturn Creep Follows the Average of the Two Schedule Slippage Cost Rules
2.22 Transformation of a ‘Perfect’ Norden-Rayleigh Curve to a Linear Form
2.23 Solver Setup Exploiting the Linear Transformation Property of a Norden-Rayleigh Curve
2.24 Solver Results Exploiting the Linear Transformation Property of a Norden-Rayleigh Curve (1)
2.25 Solver Results Exploiting the Linear Transformation Property of a Norden-Rayleigh Curve (2)
2.26 Solver Outturn Creep Follows a Weighted Average of the Two Schedule Slippage Cost Rules
2.27 Comparison of Outturn Predictions Using Four Techniques
3.1 Sum of the Values of Two Dice Based on 10,800 Random Rolls (Twice)
3.2 Example Monte Carlo Simulation of Ten Independently Distributed Cost Variables
3.3 Comparison of Summary Output Statistics Between Two Corresponding Simulations
3.4 Example Distributions that Might be Substituted by Other Distributions of a Similar Shape
3.5 Beta and Triangular Distributions with Similar Shapes
3.6 Comparison of Two Monte Carlo Simulations with Appropriate Substituted Distributions
3.7 Comparison of Two Monte Carlo Simulations with Inappropriate Substituted Distributions (1)
3.8 Comparison of Two Monte Carlo Simulations with Inappropriate Substituted Distributions (2)
3.9 Making an Appropriate Informed Choice of Distribution for Monte Carlo Simulation
3.10 Choosing Beta Distribution Parameters for Monte Carlo Simulation Based on the Mode
3.11 Comparison of Monte Carlo Simulations with 100% and 0% (Independent) Correlated Events
3.12 Monte Carlo Output Correlation with 50% Chain-Linked Input Correlation
3.13 Output Correlation Matrix where the Chain-Link Sequence has been Reversed
3.14 Monte Carlo Output Correlation with 50% Hub-Linked Input Correlation
3.15 Monte Carlo Output Correlation with 50% Isometric Hub-Linked Input Correlation
3.16 Comparison of Chain-Linked, Hub-Linked and Background Isometric Correlation Models
3.17 Monte Carlo Output Correlation with 50% Negative Chain-Linked Input Correlation
3.18 Monte Carlo Input Data with a Single Risk at 50% Probability of Occurrence
3.19 Monte Carlo Input Data with Four Risks and One Opportunity
3.20 Risk Exposure and Risk and Opportunity Ranking Factor
3.21 Swapping an Uncertainty Range with a Paired Opportunity and Risk Around a Fixed Value
3.22 Residual Risk Exposure
3.23 Example Probabilities Aligned with Qualitative Assessments of Risk/Opportunity Likelihood
3.24 The Known Unknown Matrix
3.25 The Known Unknown Matrix–Monte Carlo View
4.1 Top-Down Approach Using March Army Technique and Uplift Factors
4.2 Slipping and Sliding Technique as an Aid to Budgeting
4.3 Estimate Maturity Assessment for Defined Risks and Opportunities
4.4 Estimate Maturity Assessment as a Guide to Risk, Opportunity & Uncertainty Technique
5.1 Factored Most Likely Value Technique for Baseline and Risk Contingency
5.2 Factored Mean Value Technique for Baseline and Risk Contingency
6.1 Activity Start and End Date Options
6.2 Binary Activity Matrix Representing Each Activity Path Through the Network
6.3 Calculation of Earliest Finish and Start Dates in an Activity Network
6.4 Calculation of Latest Finish and Start Dates in an Activity Network
6.5 Calculation of Activity Float in an Activity Network
6.6 Calculation of Activity Float in an Activity Network
6.7 Uncertainty Ranges Around Activity Durations
6.8 Critical Path Uncertainty
7.1 Kendall's Notation to Characterise Various Queueing Systems
7.2 Example Simple Repair Facility with a Single Service Channel and Unbounded Repair Times
7.3 Example of Simple Repair Facility with a Bounded Repair Time Distribution
7.4 Example of a 3-Channel Repair Facility with an Unbounded Repair Time Distribution
7.5 Example of a 3-Channel Repair Facility with a Bounded Repair Time Distribution
7.6 Comparing the Observed Repair Arisings per Month with a Poisson Distribution
7.7 Calculating the Chi-Squared Test for Goodness of Fit (Long Hand) over a 12 Month Period
7.8 Calculating the Chi-Squared Test for Goodness of Fit (Long Hand) over a 12 Month Period (2)
7.9 Calculating the Chi-Squared Test for Goodness of Fit (Long Hand) over a 12 Month Period (3)
7.10 Calculating the Chi-Squared Test for Goodness of Fit (Long Hand) over a 24 Month Period (1)
7.11 Calculating the Chi-Squared Test for Goodness of Fit (Long Hand) over a 24 Month Period (2)
7.12 Some Potential Techniques for Analysing Arising Rate Trends
7.13 Example of a G/M/1 Queueing Model with an Initially Decreasing Arising Rate
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3.139.86.18