GREGORY K. MISLICK is a Senior Lecturer in the Department of Operations Research at the Naval Postgraduate School (NPS), where he has taught since 2000. His expertise includes life cycle cost estimation, regression analysis, learning curves, data analysis, and optimization. He has advised numerous cost and aviation theses, was an Associate Dean at NPS and is the Program Manager of the Master’s degree in Cost Estimating and Analysis curriculum. DANIEL A. NUSSBAUM is a Professor in the Department of Operations Research at NPS and Chair of the Energy Academic Group. With four decades of experience in financial estimating and analysis for the U.S. Federal government, his research spans cost/benefit analysis, life cycle cost modeling, and financial modeling for strategic decision making. KAREN R. MISLICK is a Senior Lecturer in the Department of Operations Research at NPS where she teaches cost estimating, scheduling, earned value management, Agile development, and data visualization and storytelling. She previously spent two decades at the Government Accountability Office, leading cost, schedule, and earned value analyses and over 250 audits across major federal programs. She is Level III certified in both cost estimating and financial management.

• Foreword xiiiAbout the Authors xviiPreface xixAcronyms xxvChapter 1 “Looking Back: Reflections on Cost Estimating” 1Reference 12Chapter 2 Introduction to Cost Estimating 132.1 Introduction 132.2 What Is Cost Estimating? 132.3 What Are the Characteristics of a Good Cost Estimate? 152.4 Importance of Cost Estimating in DoW and in Congress. Why Do We Do Cost Estimating? 172.4.1 Importance of Cost Estimating to Congress 182.5 An Overview of the DoW Acquisition Process 202.6 Acquisition Categories (ACATs) 262.7 Cost-Estimating Terminology 292.8 Summary 36References 36Applications and Questions 37Chapter 3 Non-DoW Acquisition and the Cost-Estimating Process 393.1 Introduction 393.2 Who Practices Cost Estimation? 403.3 The Government Accountability Office (GAO) and the 12-STEP Process 413.4 Cost Estimating in Other Non-DoW Agencies and Organizations 443.4.1 The Intelligence Community (IC) 443.4.2 National Aeronautics and Space Administration (NASA) 453.4.3 The Department of Energy (DOE) 453.4.4 Federally Funded Research and Development Centers (FFRDCs) 463.4.5 The MITRE Corporation 473.4.6 RAND Corporation 473.4.7 University Affiliated Research Centers (UARCs) 473.4.8 Commercial Firms 483.4.9 Cost Estimating Book of Knowledge (CEBoK) 483.5 The Cost-Estimating Process 503.5.1 Definition and Planning: Knowing the Purpose of the Estimate 513.5.2 Definition and Planning: Defining the System 533.5.3 Definition and Planning: Establishing the Ground Rules and Assumptions 553.5.4 Definition and Planning: Selecting the Estimating Approach 563.5.5 Definition and Planning: Putting the Team Together 593.6 Data Collection 593.7 Formulation of the Estimate 603.8 Review and Documentation 603.9 Work Breakdown Structure (WBS) 613.9.1 Program Work Breakdown Structure 613.9.2 Military-Standard (MIL-STD) 881F 643.10 Cost Element Structure (CES) 653.11 Summary 67References 67Applications and Questions 68Chapter 4 Data Sources 714.1 Introduction 714.2 Background and Considerations to Data Collection 714.2.1 Cost Data 744.2.2 Technical Data 744.2.3 Programmatic Data 744.2.4 Risk Data 754.3 Cost Reports and Earned Value Management (EVM) 764.3.1 Cost and Software Data Reporting (CSDR) and FlexFiles 764.3.2 Contract Performance Report (CPR) 824.3.3 EVM in Action 854.3.4 Selected Acquisition Reports, DAES, and Other Oversight Tools 884.4 Cost Databases 894.4.1 Cost Assessment Data Enterprise (CADE) 904.4.2 Defense Acquisition Visibility Environment (DAVE) 914.4.3 Enterprise Visibility and Management of Operating and Support Costs (EVAMOSC) 914.5 Summary 92References 93Applications and Questions 93Chapter 5 Data Normalization 955.1 Introduction 955.2 The Role of Data Normalization in Cost Estimating 955.3 Normalizing for Content 975.4 Normalizing for Quantity 995.5 Normalizing for Inflation 1025.6 DoW Appropriations and Background 1065.7 Constant Year Dollars (CY$) 1085.8 Base Year Dollars (BY$) 1105.9 DoW Inflation Indices 1125.10 Then-Year Dollars (TY$) 1175.11 Using the Joint Inflation Calculator (JIC) 1215.12 Expenditure (Outlay) Profile 1235.13 Escalation 1275.14 Summary 127References 128Applications and Questions 128Chapter 6 Statistics for Cost Estimators 1316.1 Introduction 1316.2 Background to Statistics 1316.3 Margin of Error 1326.4 Taking a Sample 1366.5 Measures of Central Tendency 1376.6 Dispersion Statistics 1396.7 Coefficient of Variation 1456.8 Summary 146References 147General Reference 147Applications and Questions 147Chapter 7 Single-Variable Linear Regression Analysis 1497.1 Introduction 1497.2 Home Buying Example 1497.3 Regression Background and Terminology 1547.4 Evaluating a Regression 1597.5 Standard Error (SE) 1597.6 Coefficient of Variation (CV) 1617.7 Analysis of Variance (ANOVA) 1627.8 Coefficient of Determination (R 2) 1647.9 F-Statistic and t-Statistics 1657.10 Regression Hierarchy 1687.10.1 Hierarchy of Regression 1687.11 Staying within the Range of Your Data 1707.12 Treatment of Outliers 1717.12.1 Handling Outliers with Respect to X (The Independent Variable Data) 1727.12.2 Handling Outliers with Respect to Y (The Dependent Variable Data) 1737.13 Residual Analysis 1757.14 “Deeper Dive: Beyond the Basics” 1787.15 “Solar Array Panel” Case Study (Continued, Part 2) 1817.16 Summary 185Reference 185Applications and Questions 185Chapter 8 Multivariable Linear Regression Analysis 1878.1 Introduction 1878.2 Background of Multivariable Linear Regression 1878.3 Home Prices Example 1898.4 Multicollinearity (MC) 1948.5 Detecting Multicollinearity (MC), Method #1: Widely Varying Regression Slope Coefficients 1958.6 Detecting Multicollinearity, Method #2: Correlation Matrix 1968.7 Multicollinearity Example, MC #1: Home Prices 1978.8 Determining Statistical Relationships between Independent Variables 1998.9 Multicollinearity Example, MC #2: Weapon Systems 2008.10 Conclusions of Multicollinearity 2038.11 Multivariable Regression Guidelines 2048.12 Deeper Dive: Beyond the Basics 2068.13 “Solar Array Panel” Case Study (Continued, Part 3) 2068.14 Summary 209Applications and Questions 210Chapter 9 Intrinsically Linear Regression 2139.1 Introduction 2139.2 Background of Intrinsically Linear Regression 2139.3 The Multiplicative Model 2179.4 Data Transformation 2189.5 Interpreting the Regression Results 2229.6 Deeper Dive: Beyond the Basics 2239.7 Summary 227References 228Applications and Questions 228Chapter 10 Learning Curves: Unit Theory 23110.1 Introduction 23110.2 Learning Curve, Scenario # 1 23110.3 Cumulative Average Theory Overview 23310.4 Unit Theory Overview 23410.5 Unit Theory 23810.6 Estimating Lot Costs 24110.7 Fitting a Curve Using Lot Data 24510.7.1 Lot Midpoint 24610.7.2 Average Unit Cost (AUC) 24810.8 Alternative LMP and Lot Cost Calculations 25710.9 Deeper Dive: Beyond the Basics 25810.10 Summary 260References 260Applications and Questions 261Chapter 11 Learning Curves: Cumulative Average Theory 26311.1 Introduction 26311.2 Background of Cumulative Average Theory (CAT) 26311.3 Cumulative Average Theory 26511.4 Estimating Lot Costs 26911.5 Cumulative Average Theory, Final Example 27011.6 Unit Theory vs. Cumulative Average Theory 27311.6.1 Learning Curve Selection 27411.7 Summary 275Applications and Questions 276Chapter 12 Learning Curves: Production Breaks/Lost Learning 27712.1 Introduction 27712.2 The Lost Learning Process 27812.3 Production Break Scenario 27812.4 The Anderlohr Method 27912.5 Production Break Example 28112.6 The Retrograde Method, Example 12.1 (Part 2) 28312.7 Summary 290References 291Applications and Questions 291Chapter 13 Wrap Rates and Step-Down Functions 29313.1 Introduction 29313.2 Wrap Rate Overview 29313.3 Wrap Rate Components 29513.3.1 Direct Labor Wage Rate 29513.3.2 Overhead Rate 29613.3.3 Other Costs 29713.4 Wrap Rate, Final Example (Example 13.2) 29813.5 Summary of Wrap Rates 29913.6 Introduction to Step-Down Functions 29913.7 Step-Down Function Theory 30013.8 Step-Down Function Example 13.1 30013.9 Summary of Step-Down Functions 303Reference 303Applications and Questions 303Chapter 14 Cost Factors and the Analogy Technique 30514.1 Introduction 30514.2 Cost Factors Scenario 30514.3 Cost Factors 30614.4 Which Factor to Use? 30914.5 Cost Factors Handbooks 31014.6 Unified Facilities Criteria (UFC) 31014.7 Summary of Cost Factors 31114.8 Introduction to the Analogy Technique 31214.9 Background of Analogy 31214.10 Methodology 31314.11 Example 14.2, Part 1: The Historical WBS 31414.12 Example 14.2, Part 2: The New WBS 31614.13 Summary of the Analogy Technique 319Reference 320Applications and Questions 320Chapter 15 Software Cost Estimation 32115.1 Introduction 32115.2 Background on Software Cost Estimation 32115.3 What Is Software? 32215.4 The WBS Elements in a Typical Software Cost-Estimating Task 32315.5 Software Costing Characteristics and Concerns 32415.6 Measuring Software Size: Source Lines of Code (SLOC) and Function Points (FPs) 32515.6.1 Source Lines of Code (SLOC) 32515.6.2 Function Point (FP) Analysis 32715.7 The Software Cost-Estimating Process 32815.8 Problems with Software Cost Estimating: Cost Growth 32915.9 Commercial Software Availability 33015.9.1 COTS in the Software Environment 33115.10 Waterfall vs. Agile: A New Paradigm 33215.11 Post-Development Software Maintenance Costs 33415.12 Summary 334References 334Applications and Questions 334Chapter 16 Cost–Benefit Analysis and Risk and Uncertainty 33716.1 Introduction 33716.2 Cost–Benefit Analysis (CBA) and Net Present Value (NPV) Overview 33716.3 Time Value of Money 34016.4 Example 16.1. Net Present Value 34416.5 Risk and Uncertainty Overview 34816.6 Considerations for Handling Risk and Uncertainty 35016.7 How Do the Uncertainties Affect Our Estimate? 35216.8 Cumulative Cost and Monte Carlo Simulation 35416.9 Suggested Resources on Risk and Uncertainty Analysis 35716.10 Summary 357References 358Applications and Questions 358Chapter 17 Epilogue 359Looking Back 359Key Takeaways 360Lessons from History 360A Growing Profession 361Looking to the Future: AI and the Changing Landscape 362How AIIs Already Helping 362Why Human Expertise Still Matters 362The Rise of Purpose-Built Tools 362The Road Ahead 363Closing Thoughts 363Answers to Questions 365Index 377

GREGORY K. MISLICK is a Senior Lecturer in the Department of Operations Research at the Naval Postgraduate School (NPS), where he has taught since 2000. His expertise includes life cycle cost estimation, regression analysis, learning curves, data analysis, and optimization. He has advised numerous cost and aviation theses, was an Associate Dean at NPS and is the Program Manager of the Master’s degree in Cost Estimating and Analysis curriculum. DANIEL A. NUSSBAUM is a Professor in the Department of Operations Research at NPS and Chair of the Energy Academic Group. With four decades of experience in financial estimating and analysis for the U.S. Federal government, his research spans cost/benefit analysis, life cycle cost modeling, and financial modeling for strategic decision making. KAREN R. MISLICK is a Senior Lecturer in the Department of Operations Research at NPS where she teaches cost estimating, scheduling, earned value management, Agile development, and data visualization and storytelling. She previously spent two decades at the Government Accountability Office, leading cost, schedule, and earned value analyses and over 250 audits across major federal programs. She is Level III certified in both cost estimating and financial management.

• Foreword xiiiAbout the Authors xviiPreface xixAcronyms xxvChapter 1 “Looking Back: Reflections on Cost Estimating” 1Reference 12Chapter 2 Introduction to Cost Estimating 132.1 Introduction 132.2 What Is Cost Estimating? 132.3 What Are the Characteristics of a Good Cost Estimate? 152.4 Importance of Cost Estimating in DoW and in Congress. Why Do We Do Cost Estimating? 172.4.1 Importance of Cost Estimating to Congress 182.5 An Overview of the DoW Acquisition Process 202.6 Acquisition Categories (ACATs) 262.7 Cost-Estimating Terminology 292.8 Summary 36References 36Applications and Questions 37Chapter 3 Non-DoW Acquisition and the Cost-Estimating Process 393.1 Introduction 393.2 Who Practices Cost Estimation? 403.3 The Government Accountability Office (GAO) and the 12-STEP Process 413.4 Cost Estimating in Other Non-DoW Agencies and Organizations 443.4.1 The Intelligence Community (IC) 443.4.2 National Aeronautics and Space Administration (NASA) 453.4.3 The Department of Energy (DOE) 453.4.4 Federally Funded Research and Development Centers (FFRDCs) 463.4.5 The MITRE Corporation 473.4.6 RAND Corporation 473.4.7 University Affiliated Research Centers (UARCs) 473.4.8 Commercial Firms 483.4.9 Cost Estimating Book of Knowledge (CEBoK) 483.5 The Cost-Estimating Process 503.5.1 Definition and Planning: Knowing the Purpose of the Estimate 513.5.2 Definition and Planning: Defining the System 533.5.3 Definition and Planning: Establishing the Ground Rules and Assumptions 553.5.4 Definition and Planning: Selecting the Estimating Approach 563.5.5 Definition and Planning: Putting the Team Together 593.6 Data Collection 593.7 Formulation of the Estimate 603.8 Review and Documentation 603.9 Work Breakdown Structure (WBS) 613.9.1 Program Work Breakdown Structure 613.9.2 Military-Standard (MIL-STD) 881F 643.10 Cost Element Structure (CES) 653.11 Summary 67References 67Applications and Questions 68Chapter 4 Data Sources 714.1 Introduction 714.2 Background and Considerations to Data Collection 714.2.1 Cost Data 744.2.2 Technical Data 744.2.3 Programmatic Data 744.2.4 Risk Data 754.3 Cost Reports and Earned Value Management (EVM) 764.3.1 Cost and Software Data Reporting (CSDR) and FlexFiles 764.3.2 Contract Performance Report (CPR) 824.3.3 EVM in Action 854.3.4 Selected Acquisition Reports, DAES, and Other Oversight Tools 884.4 Cost Databases 894.4.1 Cost Assessment Data Enterprise (CADE) 904.4.2 Defense Acquisition Visibility Environment (DAVE) 914.4.3 Enterprise Visibility and Management of Operating and Support Costs (EVAMOSC) 914.5 Summary 92References 93Applications and Questions 93Chapter 5 Data Normalization 955.1 Introduction 955.2 The Role of Data Normalization in Cost Estimating 955.3 Normalizing for Content 975.4 Normalizing for Quantity 995.5 Normalizing for Inflation 1025.6 DoW Appropriations and Background 1065.7 Constant Year Dollars (CY$) 1085.8 Base Year Dollars (BY$) 1105.9 DoW Inflation Indices 1125.10 Then-Year Dollars (TY$) 1175.11 Using the Joint Inflation Calculator (JIC) 1215.12 Expenditure (Outlay) Profile 1235.13 Escalation 1275.14 Summary 127References 128Applications and Questions 128Chapter 6 Statistics for Cost Estimators 1316.1 Introduction 1316.2 Background to Statistics 1316.3 Margin of Error 1326.4 Taking a Sample 1366.5 Measures of Central Tendency 1376.6 Dispersion Statistics 1396.7 Coefficient of Variation 1456.8 Summary 146References 147General Reference 147Applications and Questions 147Chapter 7 Single-Variable Linear Regression Analysis 1497.1 Introduction 1497.2 Home Buying Example 1497.3 Regression Background and Terminology 1547.4 Evaluating a Regression 1597.5 Standard Error (SE) 1597.6 Coefficient of Variation (CV) 1617.7 Analysis of Variance (ANOVA) 1627.8 Coefficient of Determination (R 2) 1647.9 F-Statistic and t-Statistics 1657.10 Regression Hierarchy 1687.10.1 Hierarchy of Regression 1687.11 Staying within the Range of Your Data 1707.12 Treatment of Outliers 1717.12.1 Handling Outliers with Respect to X (The Independent Variable Data) 1727.12.2 Handling Outliers with Respect to Y (The Dependent Variable Data) 1737.13 Residual Analysis 1757.14 “Deeper Dive: Beyond the Basics” 1787.15 “Solar Array Panel” Case Study (Continued, Part 2) 1817.16 Summary 185Reference 185Applications and Questions 185Chapter 8 Multivariable Linear Regression Analysis 1878.1 Introduction 1878.2 Background of Multivariable Linear Regression 1878.3 Home Prices Example 1898.4 Multicollinearity (MC) 1948.5 Detecting Multicollinearity (MC), Method #1: Widely Varying Regression Slope Coefficients 1958.6 Detecting Multicollinearity, Method #2: Correlation Matrix 1968.7 Multicollinearity Example, MC #1: Home Prices 1978.8 Determining Statistical Relationships between Independent Variables 1998.9 Multicollinearity Example, MC #2: Weapon Systems 2008.10 Conclusions of Multicollinearity 2038.11 Multivariable Regression Guidelines 2048.12 Deeper Dive: Beyond the Basics 2068.13 “Solar Array Panel” Case Study (Continued, Part 3) 2068.14 Summary 209Applications and Questions 210Chapter 9 Intrinsically Linear Regression 2139.1 Introduction 2139.2 Background of Intrinsically Linear Regression 2139.3 The Multiplicative Model 2179.4 Data Transformation 2189.5 Interpreting the Regression Results 2229.6 Deeper Dive: Beyond the Basics 2239.7 Summary 227References 228Applications and Questions 228Chapter 10 Learning Curves: Unit Theory 23110.1 Introduction 23110.2 Learning Curve, Scenario # 1 23110.3 Cumulative Average Theory Overview 23310.4 Unit Theory Overview 23410.5 Unit Theory 23810.6 Estimating Lot Costs 24110.7 Fitting a Curve Using Lot Data 24510.7.1 Lot Midpoint 24610.7.2 Average Unit Cost (AUC) 24810.8 Alternative LMP and Lot Cost Calculations 25710.9 Deeper Dive: Beyond the Basics 25810.10 Summary 260References 260Applications and Questions 261Chapter 11 Learning Curves: Cumulative Average Theory 26311.1 Introduction 26311.2 Background of Cumulative Average Theory (CAT) 26311.3 Cumulative Average Theory 26511.4 Estimating Lot Costs 26911.5 Cumulative Average Theory, Final Example 27011.6 Unit Theory vs. Cumulative Average Theory 27311.6.1 Learning Curve Selection 27411.7 Summary 275Applications and Questions 276Chapter 12 Learning Curves: Production Breaks/Lost Learning 27712.1 Introduction 27712.2 The Lost Learning Process 27812.3 Production Break Scenario 27812.4 The Anderlohr Method 27912.5 Production Break Example 28112.6 The Retrograde Method, Example 12.1 (Part 2) 28312.7 Summary 290References 291Applications and Questions 291Chapter 13 Wrap Rates and Step-Down Functions 29313.1 Introduction 29313.2 Wrap Rate Overview 29313.3 Wrap Rate Components 29513.3.1 Direct Labor Wage Rate 29513.3.2 Overhead Rate 29613.3.3 Other Costs 29713.4 Wrap Rate, Final Example (Example 13.2) 29813.5 Summary of Wrap Rates 29913.6 Introduction to Step-Down Functions 29913.7 Step-Down Function Theory 30013.8 Step-Down Function Example 13.1 30013.9 Summary of Step-Down Functions 303Reference 303Applications and Questions 303Chapter 14 Cost Factors and the Analogy Technique 30514.1 Introduction 30514.2 Cost Factors Scenario 30514.3 Cost Factors 30614.4 Which Factor to Use? 30914.5 Cost Factors Handbooks 31014.6 Unified Facilities Criteria (UFC) 31014.7 Summary of Cost Factors 31114.8 Introduction to the Analogy Technique 31214.9 Background of Analogy 31214.10 Methodology 31314.11 Example 14.2, Part 1: The Historical WBS 31414.12 Example 14.2, Part 2: The New WBS 31614.13 Summary of the Analogy Technique 319Reference 320Applications and Questions 320Chapter 15 Software Cost Estimation 32115.1 Introduction 32115.2 Background on Software Cost Estimation 32115.3 What Is Software? 32215.4 The WBS Elements in a Typical Software Cost-Estimating Task 32315.5 Software Costing Characteristics and Concerns 32415.6 Measuring Software Size: Source Lines of Code (SLOC) and Function Points (FPs) 32515.6.1 Source Lines of Code (SLOC) 32515.6.2 Function Point (FP) Analysis 32715.7 The Software Cost-Estimating Process 32815.8 Problems with Software Cost Estimating: Cost Growth 32915.9 Commercial Software Availability 33015.9.1 COTS in the Software Environment 33115.10 Waterfall vs. Agile: A New Paradigm 33215.11 Post-Development Software Maintenance Costs 33415.12 Summary 334References 334Applications and Questions 334Chapter 16 Cost–Benefit Analysis and Risk and Uncertainty 33716.1 Introduction 33716.2 Cost–Benefit Analysis (CBA) and Net Present Value (NPV) Overview 33716.3 Time Value of Money 34016.4 Example 16.1. Net Present Value 34416.5 Risk and Uncertainty Overview 34816.6 Considerations for Handling Risk and Uncertainty 35016.7 How Do the Uncertainties Affect Our Estimate? 35216.8 Cumulative Cost and Monte Carlo Simulation 35416.9 Suggested Resources on Risk and Uncertainty Analysis 35716.10 Summary 357References 358Applications and Questions 358Chapter 17 Epilogue 359Looking Back 359Key Takeaways 360Lessons from History 360A Growing Profession 361Looking to the Future: AI and the Changing Landscape 362How AIIs Already Helping 362Why Human Expertise Still Matters 362The Rise of Purpose-Built Tools 362The Road Ahead 363Closing Thoughts 363Answers to Questions 365Index 377

GREGORY K. MISLICK is a Senior Lecturer in the Department of Operations Research at the Naval Postgraduate School (NPS), where he has taught since 2000. His expertise includes life cycle cost estimation, regression analysis, learning curves, data analysis, and optimization. He has advised numerous cost and aviation theses, was an Associate Dean at NPS and is the Program Manager of the Master’s degree in Cost Estimating and Analysis curriculum. DANIEL A. NUSSBAUM is a Professor in the Department of Operations Research at NPS and Chair of the Energy Academic Group. With four decades of experience in financial estimating and analysis for the U.S. Federal government, his research spans cost/benefit analysis, life cycle cost modeling, and financial modeling for strategic decision making. KAREN R. MISLICK is a Senior Lecturer in the Department of Operations Research at NPS where she teaches cost estimating, scheduling, earned value management, Agile development, and data visualization and storytelling. She previously spent two decades at the Government Accountability Office, leading cost, schedule, and earned value analyses and over 250 audits across major federal programs. She is Level III certified in both cost estimating and financial management.

• Foreword xiiiAbout the Authors xviiPreface xixAcronyms xxvChapter 1 “Looking Back: Reflections on Cost Estimating” 1Reference 12Chapter 2 Introduction to Cost Estimating 132.1 Introduction 132.2 What Is Cost Estimating? 132.3 What Are the Characteristics of a Good Cost Estimate? 152.4 Importance of Cost Estimating in DoW and in Congress. Why Do We Do Cost Estimating? 172.4.1 Importance of Cost Estimating to Congress 182.5 An Overview of the DoW Acquisition Process 202.6 Acquisition Categories (ACATs) 262.7 Cost-Estimating Terminology 292.8 Summary 36References 36Applications and Questions 37Chapter 3 Non-DoW Acquisition and the Cost-Estimating Process 393.1 Introduction 393.2 Who Practices Cost Estimation? 403.3 The Government Accountability Office (GAO) and the 12-STEP Process 413.4 Cost Estimating in Other Non-DoW Agencies and Organizations 443.4.1 The Intelligence Community (IC) 443.4.2 National Aeronautics and Space Administration (NASA) 453.4.3 The Department of Energy (DOE) 453.4.4 Federally Funded Research and Development Centers (FFRDCs) 463.4.5 The MITRE Corporation 473.4.6 RAND Corporation 473.4.7 University Affiliated Research Centers (UARCs) 473.4.8 Commercial Firms 483.4.9 Cost Estimating Book of Knowledge (CEBoK) 483.5 The Cost-Estimating Process 503.5.1 Definition and Planning: Knowing the Purpose of the Estimate 513.5.2 Definition and Planning: Defining the System 533.5.3 Definition and Planning: Establishing the Ground Rules and Assumptions 553.5.4 Definition and Planning: Selecting the Estimating Approach 563.5.5 Definition and Planning: Putting the Team Together 593.6 Data Collection 593.7 Formulation of the Estimate 603.8 Review and Documentation 603.9 Work Breakdown Structure (WBS) 613.9.1 Program Work Breakdown Structure 613.9.2 Military-Standard (MIL-STD) 881F 643.10 Cost Element Structure (CES) 653.11 Summary 67References 67Applications and Questions 68Chapter 4 Data Sources 714.1 Introduction 714.2 Background and Considerations to Data Collection 714.2.1 Cost Data 744.2.2 Technical Data 744.2.3 Programmatic Data 744.2.4 Risk Data 754.3 Cost Reports and Earned Value Management (EVM) 764.3.1 Cost and Software Data Reporting (CSDR) and FlexFiles 764.3.2 Contract Performance Report (CPR) 824.3.3 EVM in Action 854.3.4 Selected Acquisition Reports, DAES, and Other Oversight Tools 884.4 Cost Databases 894.4.1 Cost Assessment Data Enterprise (CADE) 904.4.2 Defense Acquisition Visibility Environment (DAVE) 914.4.3 Enterprise Visibility and Management of Operating and Support Costs (EVAMOSC) 914.5 Summary 92References 93Applications and Questions 93Chapter 5 Data Normalization 955.1 Introduction 955.2 The Role of Data Normalization in Cost Estimating 955.3 Normalizing for Content 975.4 Normalizing for Quantity 995.5 Normalizing for Inflation 1025.6 DoW Appropriations and Background 1065.7 Constant Year Dollars (CY$) 1085.8 Base Year Dollars (BY$) 1105.9 DoW Inflation Indices 1125.10 Then-Year Dollars (TY$) 1175.11 Using the Joint Inflation Calculator (JIC) 1215.12 Expenditure (Outlay) Profile 1235.13 Escalation 1275.14 Summary 127References 128Applications and Questions 128Chapter 6 Statistics for Cost Estimators 1316.1 Introduction 1316.2 Background to Statistics 1316.3 Margin of Error 1326.4 Taking a Sample 1366.5 Measures of Central Tendency 1376.6 Dispersion Statistics 1396.7 Coefficient of Variation 1456.8 Summary 146References 147General Reference 147Applications and Questions 147Chapter 7 Single-Variable Linear Regression Analysis 1497.1 Introduction 1497.2 Home Buying Example 1497.3 Regression Background and Terminology 1547.4 Evaluating a Regression 1597.5 Standard Error (SE) 1597.6 Coefficient of Variation (CV) 1617.7 Analysis of Variance (ANOVA) 1627.8 Coefficient of Determination (R 2) 1647.9 F-Statistic and t-Statistics 1657.10 Regression Hierarchy 1687.10.1 Hierarchy of Regression 1687.11 Staying within the Range of Your Data 1707.12 Treatment of Outliers 1717.12.1 Handling Outliers with Respect to X (The Independent Variable Data) 1727.12.2 Handling Outliers with Respect to Y (The Dependent Variable Data) 1737.13 Residual Analysis 1757.14 “Deeper Dive: Beyond the Basics” 1787.15 “Solar Array Panel” Case Study (Continued, Part 2) 1817.16 Summary 185Reference 185Applications and Questions 185Chapter 8 Multivariable Linear Regression Analysis 1878.1 Introduction 1878.2 Background of Multivariable Linear Regression 1878.3 Home Prices Example 1898.4 Multicollinearity (MC) 1948.5 Detecting Multicollinearity (MC), Method #1: Widely Varying Regression Slope Coefficients 1958.6 Detecting Multicollinearity, Method #2: Correlation Matrix 1968.7 Multicollinearity Example, MC #1: Home Prices 1978.8 Determining Statistical Relationships between Independent Variables 1998.9 Multicollinearity Example, MC #2: Weapon Systems 2008.10 Conclusions of Multicollinearity 2038.11 Multivariable Regression Guidelines 2048.12 Deeper Dive: Beyond the Basics 2068.13 “Solar Array Panel” Case Study (Continued, Part 3) 2068.14 Summary 209Applications and Questions 210Chapter 9 Intrinsically Linear Regression 2139.1 Introduction 2139.2 Background of Intrinsically Linear Regression 2139.3 The Multiplicative Model 2179.4 Data Transformation 2189.5 Interpreting the Regression Results 2229.6 Deeper Dive: Beyond the Basics 2239.7 Summary 227References 228Applications and Questions 228Chapter 10 Learning Curves: Unit Theory 23110.1 Introduction 23110.2 Learning Curve, Scenario # 1 23110.3 Cumulative Average Theory Overview 23310.4 Unit Theory Overview 23410.5 Unit Theory 23810.6 Estimating Lot Costs 24110.7 Fitting a Curve Using Lot Data 24510.7.1 Lot Midpoint 24610.7.2 Average Unit Cost (AUC) 24810.8 Alternative LMP and Lot Cost Calculations 25710.9 Deeper Dive: Beyond the Basics 25810.10 Summary 260References 260Applications and Questions 261Chapter 11 Learning Curves: Cumulative Average Theory 26311.1 Introduction 26311.2 Background of Cumulative Average Theory (CAT) 26311.3 Cumulative Average Theory 26511.4 Estimating Lot Costs 26911.5 Cumulative Average Theory, Final Example 27011.6 Unit Theory vs. Cumulative Average Theory 27311.6.1 Learning Curve Selection 27411.7 Summary 275Applications and Questions 276Chapter 12 Learning Curves: Production Breaks/Lost Learning 27712.1 Introduction 27712.2 The Lost Learning Process 27812.3 Production Break Scenario 27812.4 The Anderlohr Method 27912.5 Production Break Example 28112.6 The Retrograde Method, Example 12.1 (Part 2) 28312.7 Summary 290References 291Applications and Questions 291Chapter 13 Wrap Rates and Step-Down Functions 29313.1 Introduction 29313.2 Wrap Rate Overview 29313.3 Wrap Rate Components 29513.3.1 Direct Labor Wage Rate 29513.3.2 Overhead Rate 29613.3.3 Other Costs 29713.4 Wrap Rate, Final Example (Example 13.2) 29813.5 Summary of Wrap Rates 29913.6 Introduction to Step-Down Functions 29913.7 Step-Down Function Theory 30013.8 Step-Down Function Example 13.1 30013.9 Summary of Step-Down Functions 303Reference 303Applications and Questions 303Chapter 14 Cost Factors and the Analogy Technique 30514.1 Introduction 30514.2 Cost Factors Scenario 30514.3 Cost Factors 30614.4 Which Factor to Use? 30914.5 Cost Factors Handbooks 31014.6 Unified Facilities Criteria (UFC) 31014.7 Summary of Cost Factors 31114.8 Introduction to the Analogy Technique 31214.9 Background of Analogy 31214.10 Methodology 31314.11 Example 14.2, Part 1: The Historical WBS 31414.12 Example 14.2, Part 2: The New WBS 31614.13 Summary of the Analogy Technique 319Reference 320Applications and Questions 320Chapter 15 Software Cost Estimation 32115.1 Introduction 32115.2 Background on Software Cost Estimation 32115.3 What Is Software? 32215.4 The WBS Elements in a Typical Software Cost-Estimating Task 32315.5 Software Costing Characteristics and Concerns 32415.6 Measuring Software Size: Source Lines of Code (SLOC) and Function Points (FPs) 32515.6.1 Source Lines of Code (SLOC) 32515.6.2 Function Point (FP) Analysis 32715.7 The Software Cost-Estimating Process 32815.8 Problems with Software Cost Estimating: Cost Growth 32915.9 Commercial Software Availability 33015.9.1 COTS in the Software Environment 33115.10 Waterfall vs. Agile: A New Paradigm 33215.11 Post-Development Software Maintenance Costs 33415.12 Summary 334References 334Applications and Questions 334Chapter 16 Cost–Benefit Analysis and Risk and Uncertainty 33716.1 Introduction 33716.2 Cost–Benefit Analysis (CBA) and Net Present Value (NPV) Overview 33716.3 Time Value of Money 34016.4 Example 16.1. Net Present Value 34416.5 Risk and Uncertainty Overview 34816.6 Considerations for Handling Risk and Uncertainty 35016.7 How Do the Uncertainties Affect Our Estimate? 35216.8 Cumulative Cost and Monte Carlo Simulation 35416.9 Suggested Resources on Risk and Uncertainty Analysis 35716.10 Summary 357References 358Applications and Questions 358Chapter 17 Epilogue 359Looking Back 359Key Takeaways 360Lessons from History 360A Growing Profession 361Looking to the Future: AI and the Changing Landscape 362How AIIs Already Helping 362Why Human Expertise Still Matters 362The Rise of Purpose-Built Tools 362The Road Ahead 363Closing Thoughts 363Answers to Questions 365Index 377