Cost Estimation

Chapter 1: “Looking Back: Reflections on Cost Estimating”

• • Reference

Chapter 2: Introduction to Cost Estimating

• • 2.1 Introduction
• • 2.2 What Is Cost Estimating?
• • 2.3 What Are the Characteristics of a Good Cost Estimate?
• • 2.4 Importance of Cost Estimating in DoW and in Congress. Why Do We Do Cost Estimating?
• • 2.4.1 Importance of Cost Estimating to Congress
• • 2.5 An Overview of the DoW Acquisition Process
• • 2.6 Acquisition Categories (ACATs)
• • 2.7 Cost-Estimating Terminology
• • 2.8 Summary
• • References
• • Applications and Questions

Chapter 3: Non-DoW Acquisition and the Cost-Estimating Process

• • 3.1 Introduction
• • 3.2 Who Practices Cost Estimation?
• • 3.3 The Government Accountability Office (GAO) and the 12-STEP Process
• • 3.4 Cost Estimating in Other Non-DoW Agencies and Organizations
• • 3.4.1 The Intelligence Community (IC)
• • 3.4.2 National Aeronautics and Space Administration (NASA)
• • 3.4.3 The Department of Energy (DOE)
• • 3.4.4 Federally Funded Research and Development Centers (FFRDCs)
• • 3.4.5 The MITRE Corporation
• • 3.4.6 RAND Corporation
• • 3.4.7 University Affiliated Research Centers (UARCs)
• • 3.4.8 Commercial Firms
• • 3.4.9 Cost Estimating Book of Knowledge (CEBoK)
• • 3.5 The Cost-Estimating Process
• • 3.5.1 Definition and Planning: Knowing the Purpose of the Estimate
• • 3.5.2 Definition and Planning: Defining the System
• • 3.5.3 Definition and Planning: Establishing the Ground Rules and Assumptions
• • 3.5.4 Definition and Planning: Selecting the Estimating Approach
• • 3.5.5 Definition and Planning: Putting the Team Together
• • 3.6 Data Collection
• • 3.7 Formulation of the Estimate
• • 3.8 Review and Documentation
• • 3.9 Work Breakdown Structure (WBS)
• • 3.9.1 Program Work Breakdown Structure
• • 3.9.2 Military-Standard (MIL-STD) 881F
• • 3.10 Cost Element Structure (CES)
• • 3.11 Summary
• • References
• • Applications and Questions

Chapter 4: Data Sources

• • 4.1 Introduction
• • 4.2 Background and Considerations to Data Collection
• • 4.2.1 Cost Data
• • 4.2.2 Technical Data
• • 4.2.3 Programmatic Data
• • 4.2.4 Risk Data
• • 4.3 Cost Reports and Earned Value Management (EVM)
• • 4.3.1 Cost and Software Data Reporting (CSDR) and FlexFiles
• • 4.3.2 Contract Performance Report (CPR)
• • 4.3.3 EVM in Action
• • 4.3.4 Selected Acquisition Reports, DAES, and Other Oversight Tools
• • 4.4 Cost Databases
• • 4.4.1 Cost Assessment Data Enterprise (CADE)
• • 4.4.2 Defense Acquisition Visibility Environment (DAVE)
• • 4.4.3 Enterprise Visibility and Management of Operating and Support Costs (EVAMOSC)
• • 4.5 Summary
• • References
• • Applications and Questions

Chapter 5: Data Normalization

• • 5.1 Introduction
• • 5.2 The Role of Data Normalization in Cost Estimating
• • 5.3 Normalizing for Content
• • 5.4 Normalizing for Quantity
• • 5.5 Normalizing for Inflation
• • 5.6 DoW Appropriations and Background
• • 5.7 Constant Year Dollars (CY$)
• • 5.8 Base Year Dollars (BY$)
• • 5.9 DoW Inflation Indices
• • 5.10 Then-Year Dollars (TY$)
• • 5.11 Using the Joint Inflation Calculator (JIC)
• • 5.12 Expenditure (Outlay) Profile
• • 5.13 Escalation
• • 5.14 Summary
• • References
• • Applications and Questions

Chapter 6: Statistics for Cost Estimators

• • 6.1 Introduction
• • 6.2 Background to Statistics
• • 6.3 Margin of Error
• • 6.4 Taking a Sample
• • 6.5 Measures of Central Tendency
• • 6.6 Dispersion Statistics
• • 6.7 Coefficient of Variation
• • 6.8 Summary
• • References
• • General Reference
• • Applications and Questions

Chapter 7: Single-Variable Linear Regression Analysis

• • 7.1 Introduction
• • 7.2 Home Buying Example
• • 7.3 Regression Background and Terminology
• • 7.4 Evaluating a Regression
• • 7.5 Standard Error (SE)
• • 7.6 Coefficient of Variation (CV)
• • 7.7 Analysis of Variance (ANOVA)
• • 7.8 Coefficient of Determination (R 2)
• • 7.9 F-Statistic and t-Statistics
• • 7.10 Regression Hierarchy
• • 7.10.1 Hierarchy of Regression
• • 7.11 Staying within the Range of Your Data
• • 7.12 Treatment of Outliers
• • 7.12.1 Handling Outliers with Respect to X (The Independent Variable Data)
• • 7.12.2 Handling Outliers with Respect to Y (The Dependent Variable Data)
• • 7.13 Residual Analysis
• • 7.14 “Deeper Dive: Beyond the Basics”
• • 7.15 “Solar Array Panel” Case Study (Continued, Part 2)
• • 7.16 Summary
• • Reference
• • Applications and Questions

Chapter 8: Multivariable Linear Regression Analysis

• • 8.1 Introduction
• • 8.2 Background of Multivariable Linear Regression
• • 8.3 Home Prices Example
• • 8.4 Multicollinearity (MC)
• • 8.5 Detecting Multicollinearity (MC), Method #1: Widely Varying Regression Slope Coefficients
• • 8.6 Detecting Multicollinearity, Method #2: Correlation Matrix
• • 8.7 Multicollinearity Example, MC #1: Home Prices
• • 8.8 Determining Statistical Relationships between Independent Variables
• • 8.9 Multicollinearity Example, MC #2: Weapon Systems
• • 8.10 Conclusions of Multicollinearity
• • 8.11 Multivariable Regression Guidelines
• • 8.12 Deeper Dive: Beyond the Basics
• • 8.13 “Solar Array Panel” Case Study (Continued, Part 3)
• • 8.14 Summary
• • Applications and Questions

Chapter 9: Intrinsically Linear Regression

• • 9.1 Introduction
• • 9.2 Background of Intrinsically Linear Regression
• • 9.3 The Multiplicative Model
• • 9.4 Data Transformation
• • 9.5 Interpreting the Regression Results
• • 9.6 Deeper Dive: Beyond the Basics
• • 9.7 Summary
• • References
• • Applications and Questions

Chapter 10: Learning Curves: Unit Theory

• • 10.1 Introduction
• • 10.2 Learning Curve, Scenario # 1
• • 10.3 Cumulative Average Theory Overview
• • 10.4 Unit Theory Overview
• • 10.5 Unit Theory
• • 10.6 Estimating Lot Costs
• • 10.7 Fitting a Curve Using Lot Data
• • 10.7.1 Lot Midpoint
• • 10.7.2 Average Unit Cost (AUC)
• • 10.8 Alternative LMP and Lot Cost Calculations
• • 10.9 Deeper Dive: Beyond the Basics
• • 10.10 Summary
• • References
• • Applications and Questions

Chapter 11: Learning Curves: Cumulative Average Theory

• • 11.1 Introduction
• • 11.2 Background of Cumulative Average Theory (CAT)
• • 11.3 Cumulative Average Theory
• • 11.4 Estimating Lot Costs
• • 11.5 Cumulative Average Theory, Final Example
• • 11.6 Unit Theory vs. Cumulative Average Theory
• • 11.6.1 Learning Curve Selection
• • 11.7 Summary
• • Applications and Questions

Chapter 12: Learning Curves: Production Breaks/Lost Learning

• • 12.1 Introduction
• • 12.2 The Lost Learning Process
• • 12.3 Production Break Scenario
• • 12.4 The Anderlohr Method
• • 12.5 Production Break Example
• • 12.6 The Retrograde Method, Example 12.1 (Part 2)
• • 12.7 Summary
• • References
• • Applications and Questions

Chapter 13: Wrap Rates and Step-Down Functions

• • 13.1 Introduction
• • 13.2 Wrap Rate Overview
• • 13.3 Wrap Rate Components
• • 13.3.1 Direct Labor Wage Rate
• • 13.3.2 Overhead Rate
• • 13.3.3 Other Costs
• • 13.4 Wrap Rate, Final Example (Example 13.2)
• • 13.5 Summary of Wrap Rates
• • 13.6 Introduction to Step-Down Functions
• • 13.7 Step-Down Function Theory
• • 13.8 Step-Down Function Example 13.1
• • 13.9 Summary of Step-Down Functions
• • Reference
• • Applications and Questions

Chapter 14: Cost Factors and the Analogy Technique

• • 14.1 Introduction
• • 14.2 Cost Factors Scenario
• • 14.3 Cost Factors
• • 14.4 Which Factor to Use?
• • 14.5 Cost Factors Handbooks
• • 14.6 Unified Facilities Criteria (UFC)
• • 14.7 Summary of Cost Factors
• • 14.8 Introduction to the Analogy Technique
• • 14.9 Background of Analogy
• • 14.10 Methodology
• • 14.11 Example 14.2, Part 1: The Historical WBS

Chapter 15: Software Cost Estimation

Chapter 16: Cost–Benefit Analysis and Risk and Uncertainty

Chapter 17: Epilogue

Chapter APPENDIX: Answers to Questions