Book description
Best Fit Lines and Curves, and Some MatheMagical Transformations (Volume III of the Working Guides to Estimating & Forecasting series) concentrates on techniques for finding the Best Fit Line or Curve to some historical data allowing us to interpolate or extrapolate the implied relationship that will underpin our prediction. A range of simple ‘Moving Measures’ are suggested to smooth the underlying trend and quantify the degree of noise or scatter around that trend. The advantages and disadvantages are discussed and a simple way to offset the latent disadvantage of most Moving Measure Techniques is provided.
Simple Linear Regression Analysis, a more formal numerical technique that calculates the line of best fit subject to defined ‘goodness of fit’ criteria. Microsoft Excel is used to demonstrate how to decide whether the line of best fit is a good fit, or just a solution in search of some data. These principles are then extended to cover multiple cost drivers, and how we can use them to quantify 3Point Estimates.
With a deft sleight of hand, certain commonly occurring families of nonlinear relationships can be transformed mathemagically into linear formats, allowing us to exploit the powers of Regression Analysis to find the Best Fit Curves. The concludes with an exploration of the ups and downs of seasonal data (Time Series Analysis). Supported by a wealth of figures and tables, this is a valuable resource for estimators, engineers, accountants, project risk specialists as well as students of cost engineering.
Table of contents
 Cover
 Title
 Copyright
 Dedication
 Contents
 List of Figures
 List of Tables
 Foreword

1 Introduction and objectives
 1.1 Why write this book? Who might find it useful? Why five volumes?

1.2 Features you'll find in this book and others in this series
 1.2.1 Chapter context
 1.2.2 The lighter side (humour)
 1.2.3 Quotations
 1.2.4 Definitions
 1.2.5 Discussions and explanations with a mathematical slant for Formulaphiles
 1.2.6 Discussions and explanations without a mathematical slant for Formulaphobes
 1.2.7 Caveat augur
 1.2.8 Worked examples
 1.2.9 Useful Microsoft Excel functions and facilities
 1.2.10 References to authoritative sources
 1.2.11 Chapter reviews
 1.3 Overview of chapters in this volume

1.4 Elsewhere in the ‘Working Guide to Estimating & Forecasting’ series
 1.4.1 Volume I: Principles, Process and Practice of Professional Number Juggling
 1.4.2 Volume II: Probability, Statistics and Other Frightening Stuff
 1.4.3 Volume III: Best Fit Lines and Curves, and Some MatheMagical Transformations
 1.4.4 Volume IV: Learning, Unlearning and Relearning curves
 1.4.5 Volume V: Risk, Opportunity, Uncertainty and Other Random Models
 1.5 Final thoughts and musings on this volume and series
 References
 2 Linear and nonlinear properties (!) of straight lines

3 Trendsetting with some Simple Moving Measures
 3.1 Going all trendy: The could and the should

3.2 Moving Averages
 3.2.1 Use of Moving Averages
 3.2.2 When not to use Moving Averages
 3.2.3 Simple Moving Average
 3.2.4 Weighted Moving Average
 3.2.5 Choice of Moving Average Interval: Is there a better way than guessing?
 3.2.6 Can we take the Moving Average of a Moving Average?
 3.2.7 A creative use for Moving Averages – A case of forward thinking
 3.2.8 Dealing with missing data
 3.2.9 Uncertainty Range around the Moving Average
 3.3 Moving Medians
 3.4 Other Moving Measures of Central Tendency
 3.5 Exponential Smoothing
 3.6 Cumulative Average and Cumulative Smoothing
 3.7 Chapter review
 References

4 Simple and Multiple Linear Regression
 4.1 What is Regression Analysis?
 4.2 Simple Linear Regression
 4.3 Multiple Linear Regression
 4.4 Dealing with Outliers in Regression Analysis?

4.5 How good is our Regression? Six key measures
 4.5.1 Coefficient of Determination (RSquare): A measure of linearity?!
 4.5.2 FStatistic: A measure of chance occurrence
 4.5.3 tStatistics: Measures of Relevance or Significant Contribution
 4.5.4 Regression through the origin
 4.5.5 Role of common sense as a measure of goodness of fit
 4.5.6 Coefficient of Variation as a measure of tightness of fit
 4.5.7 White's Test for heteroscedasticity ... and, by default, homoscedasticity
 4.6 Prediction and Confidence Intervals – Measures of uncertainty
 4.7 Stepwise Regression
 4.8 Chapter review
 References

5 Linear transformation: Making bent lines straight
 5.1 Logarithms
 5.2 Basic linear transformation: Four Standard Function types
 5.3 Advanced linear transformation: Generalised Function types
 5.4 Finding the Best Fit Offset Constant
 5.5 Straightening out Earned Value Analysis ... or EVM Disintegration
 5.6 Linear transformation based on Cumulative Value Disaggregation
 5.7 Chapter review
 References

6 Transforming Nonlinear Regression
 6.1 Simple Linear Regression of a linear transformation
 6.2 Multiple Linear Regression of a multilinear transformation
 6.3 Stepwise Regression and multilinear transformations
 6.4 Is the Best Fit really the better fit?
 6.5 Regression of Transformed Generalised Nonlinear Functions
 6.6 Pseudo Multilinear Regression of Polynomial Functions
 6.7 Chapter review
 References
 7 Least Squares Nonlinear Curve Fitting without the logs

8 The ups and downs of Time Series Analysis
 8.1 The bits and bats ... and buts of a Time Series
 8.2 Alternative Time Series Models
 8.3 Classical Decomposition: Determining the underlying trend
 8.4 Determining the seasonal variations by Classical Decomposition
 8.5 MultiLinear Regression: A holistic approach to Time Series?
 8.6 Excel Solver technique for Time Series Analysis
 8.7 Chapter review
 Reference
 Glossary of estimating and forecasting terms
 Legend for Microsoft Excel Worked Example Tables in Greyscale
 Index
Product information
 Title: Best Fit Lines & Curves
 Author(s):
 Release date: October 2018
 Publisher(s): Routledge
 ISBN: 9781351661447
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