CCEA® Prep Track
Basics & Techniques
The Basics & Techniques session introduces an overview of cost estimating and analysis and the reasons for doing cost estimates, as well as four essential cost estimating techniques most often used to develop realistic and credible estimates. Additionally, we will review cost estimating products and related topics such as schedule and operations and support estimating, providing the background information and fundamental knowledge needed for the other CCEA® Prep sessions.
Data Collection & Normalization
This session covers the Core Knowledge section of Module 04 Data Collection of CEBoK®. All estimating techniques and cost estimating models require credible data before they can be used effectively. In this module we will discuss the various types of data, processes needed to collect and analyze the data used in parametric applications, as well as data types, sources, and adjustment techniques.
This session covers the Core Knowledge section of Module 05 Inflation and Index Numbers of CEBoK®. Proper inflation analysis is essential to the success of any cost estimate or economic analysis. Calculating inflation correctly and understanding the fundamental concepts will enable you to produce cost estimates that are timely, accurate, and credible to support your program’s lifecycle needs. It will also empower you to communicate with key stakeholders on the need to adjust your financial estimates based on changes in the economy.
Learning Curve Analysis
This is a training track presentation of the CEBoK® Module 7 (Learning Curves). The presentation will cover the key ideas, analytical constructs and applications of the learning curve module. The target audience are those preparing to take the ICEAA certification exam. Beyond the theoretical information, we will present the study questions for Module 7 with steps required to solve the problems using only a calculator as is required on the certification exam.
Linear Regression Analysis
This course introduces the basic concepts of regression and provides a demonstration of a simple linear ordinary least squares model. This session focuses on the basics required to build and evaluate a simple linear model such as a Cost Estimating Relationship (CER). Key concepts include correlation, minimizing error, homoscedasticity, statistical significance, goodness of fit, confidence intervals, uncertainty, and analysis of variance. The better you understand these concepts, the better you will be able to make inferences about cost data and employ more complicated regression techniques.
This session will provide motivation for the need for risk analysis and introduce the basic types and uses of risk. It will focus on the practical execution of the general risk analysis process: develop a point estimate; identify the risk areas in the point estimate; determine uncertainty around the point estimate; apply correlation between uncertainty distributions; run the Monte Carlo simulation; assess the reasonableness of results; calculate, allocate, and phase risk dollars; and show the results.
Analysis, Probability, & Statistics
This session discusses the analytical steps to take after obtaining a set of cost data and covers techniques for displaying and analyzing data graphically and statistical and graphical analysis of univariate and bivariate data sets. Other topics include measures of central tendency and dispersion and important probability distributions. We also introduce the concept of a random variable; Monte Carlo simulation; and the differences between the normal and lognormal distributions. Finally, we discuss hypothesis testing.
The goal of the Manufacturing Cost Estimating module is to arm the student with a set of techniques used to address issues unique to estimating in the manufacturing environment. It will be our objective in this module to raise a few of the most common general issues, considerations and concerns the estimator must be aware of in a typical major manufacturing environment and to provide techniques for addressing them. Depending on time and interest of attendees, example problems can be worked as exam preparation.
Chad John Lucas
This session covers the Core Knowledge section of Module 12 Software Cost Estimating of CEBoK®. It will be of particular interest to anyone studying for the ICEAA certification exam. The session provides an introduction to the basics of the software development process and how to estimate the related effort. The key ideas of Software Cost Estimating include the cost drivers of size, complexity, and capability. In the sizing area, we’ll focus on the competing metrics of Source Lines Of Code (SLOC), Equivalent Source Lines Of Code (ESLOC), and Function Points. We’ll also discuss the primary software development methodologies – waterfall, Agile, evolutionary, and spiral – and how to model them from a cost estimating perspective.
This session covers the Core Knowledge section of Module 13 Economic Analysis of CEBoK®. It will be of particular interest to anyone studying for the ICEAA certification exam. The session provides a practitioner’s perspective for conducting an economic analysis (EA) by reviewing EA concepts, terminology, variables and measures-of-merit. By accounting for monetized costs, monetized benefits, opportunity costs and time-value-of-money (“discounting”), an EA enables one to calculate economic measures-of-merit.
Contracts & Cost Management
This session explores the basics of contract pricing. We explore various contract types and the factors and considerations related to choosing a contract type. We also explore fee, shared risk, cost-price proposal preparation, the makeup of a good Basis of Estimate (BOE), and evaluation efforts. This session also provides an introduction to cost management. Some methods discussed include Total Ownership Cost (TOC), Cost As an Independent Variable (CAIV), Target Costing, and Activity Based Costing (ABC).
This session will provide an introduction to the basic concepts of earned value management (EVM), with a focus on implementation, governance, and practical application in support of a project or program. Specific topics will include basic EVM components and data elements, as well as standard earned value analysis techniques. We will use practice problems throughout the presentation to demonstrate and reinforce the basic principles of EVM.
Advanced Risk Analysis
This session will delve into advanced topics in risk, including a short and entertaining refresher on basic cost risk analysis. We will perform an analysis of historical risk data by studying the effect of program size on cost / schedule growth and how we can use this information in our risk analysis. This session covers the Related and Advanced Topics section of Module 09 Cost and Schedule Risk Analysis of CEBoK®.
Advanced Regression Techniques
This session reviews the main forms of non-linear regression encountered in cost estimating and unit space transformations. We then discuss multiple and multivariate regression. After an overview of MSR, MSE, and other model statistics, we will dive into topics such as multicollinearity, variable and dimension reduction, selecting the best multiple regression model, canonical correlations and orthogonal regression. This advanced course covers a breadth of topics but does not dive into linear algebra or mathematical proofs.
Schedule Risk Analysis
Project scheduling is only the start of understanding when your project will finish. This workshop shows how simulating uncertain durations allow the user to determine how much overrun there might be at different target levels of certainty. We will discuss using Monte Carlo simulations to conduct schedule risk analysis. Sources of uncertainty will be discussed. The incorporation of risk registers inputs will be addressed.
R for Cost Estimators
This session builds upon the concepts introduced in Earned Value Management System (EVMS) Basic Concepts. It assumes a basic familiarity with EVM data elements, and with calculation of variances and performance indices. The session focuses on critical analysis of EVM data, including a discussion of how traditional earned value analytical techniques can produce misleading or incomplete indicators of performance. Alternative techniques for assessing program performance are discussed, including heuristic equations and visual displays of information that can provide an early indication to decision-makers as to whether a particular effort is headed for success or failure.
Advanced Economic Analysis
This course follows a similar methodology presented in CEB10 Economic Analysis Basic, but includes additional examples on how to: depict incremental cash flow diagrams, account for non-monetary benefits, and use the content from a cash flow diagram to calculate Net Present Value (NPV) and Equivalent Uniform Annual Worth (EUAW). This course concludes with an example and discussion on how to select the preferred alternative based upon not one but three economic measures-of-merit.
Contractor Cost Data for DoD and NASA
The bulk of cost analysis involves the collection, storage, normalize, and use of contractor cost data which consists of inputs from the prime contractor and major subcontractors who design and build aerospace and weapon systems. NASA and the Department of Defense have specialized cost reporting formats. NASA uses the Cost Analysis Data Requirements (CADRe) format, while DoD uses the 1921 and FlexFiles formats. This workshop will discuss the formats and provides tips on how to make optimal use of them.
Intro to Machine Learning
Christian B. Smart
Machine learning is a broad topic that encompasses traditional parametric techniques such as regression analysis but includes many others not widely used by cost estimators. This workshop will provide an overview of machine learning with a focus on supervised and unsupervised learning, along with a brief overview of reinforcement learning. Examples in R will be provided.
Art and Science of Parametrics
The statistician George Box is famous for remarking that “all models are wrong, but some are useful.” All parametric cost models have their pros and cons. They should be based on the correct application of statistics. There is not a cut-and-dried process to develop useful parametric models. A significant amount of judgment (“art”) must be used in conjunction with sound statistics (“science”). This workshop discusses both art and science aspects of parametric modeling, as there are many good ways to develop models, but no single best way.
R Part II