## Book description

Best Fit Lines and Curves, and Some Mathe-Magical 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 3-Point Estimates.

With a deft sleight of hand, certain commonly occurring families of non-linear relationships can be transformed mathe-magically 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.

1. Cover
2. Title
4. Dedication
5. Contents
6. List of Figures
7. List of Tables
8. Foreword
9. 1 Introduction and objectives
1. 1.1 Why write this book? Who might find it useful? Why five volumes?
2. 1.2 Features you'll find in this book and others in this series
3. 1.3 Overview of chapters in this volume
4. 1.4 Elsewhere in the ‘Working Guide to Estimating & Forecasting’ series
5. 1.5 Final thoughts and musings on this volume and series
6. References
10. 2 Linear and nonlinear properties (!) of straight lines
1. 2.1 Basic linear properties
2. 2.2 The Cumulative Value (nonlinear) property of a linear sequence
3. 2.3 Chapter review
4. Reference
11. 3 Trendsetting with some Simple Moving Measures
1. 3.1 Going all trendy: The could and the should
2. 3.2 Moving Averages
3. 3.3 Moving Medians
4. 3.4 Other Moving Measures of Central Tendency
5. 3.5 Exponential Smoothing
6. 3.6 Cumulative Average and Cumulative Smoothing
7. 3.7 Chapter review
8. References
12. 4 Simple and Multiple Linear Regression
1. 4.1 What is Regression Analysis?
2. 4.2 Simple Linear Regression
3. 4.3 Multiple Linear Regression
4. 4.4 Dealing with Outliers in Regression Analysis?
5. 4.5 How good is our Regression? Six key measures
6. 4.6 Prediction and Confidence Intervals – Measures of uncertainty
7. 4.7 Stepwise Regression
8. 4.8 Chapter review
9. References
13. 5 Linear transformation: Making bent lines straight
1. 5.1 Logarithms
2. 5.2 Basic linear transformation: Four Standard Function types
3. 5.3 Advanced linear transformation: Generalised Function types
4. 5.4 Finding the Best Fit Offset Constant
5. 5.5 Straightening out Earned Value Analysis ... or EVM Disintegration
6. 5.6 Linear transformation based on Cumulative Value Disaggregation
7. 5.7 Chapter review
8. References
14. 6 Transforming Nonlinear Regression
1. 6.1 Simple Linear Regression of a linear transformation
2. 6.2 Multiple Linear Regression of a multi-linear transformation
3. 6.3 Stepwise Regression and multi-linear transformations
4. 6.4 Is the Best Fit really the better fit?
5. 6.5 Regression of Transformed Generalised Nonlinear Functions
6. 6.6 Pseudo Multi-linear Regression of Polynomial Functions
7. 6.7 Chapter review
8. References
15. 7 Least Squares Nonlinear Curve Fitting without the logs
1. 7.1 Curve Fitting by Least Squares ... without the logarithms
2. 7.2 Chapter review
3. Reference
16. 8 The ups and downs of Time Series Analysis
1. 8.1 The bits and bats ... and buts of a Time Series
2. 8.2 Alternative Time Series Models
3. 8.3 Classical Decomposition: Determining the underlying trend
4. 8.4 Determining the seasonal variations by Classical Decomposition
5. 8.5 Multi-Linear Regression: A holistic approach to Time Series?
6. 8.6 Excel Solver technique for Time Series Analysis
7. 8.7 Chapter review
8. Reference
17. Glossary of estimating and forecasting terms
18. Legend for Microsoft Excel Worked Example Tables in Greyscale
19. Index

## Product information

• Title: Best Fit Lines & Curves
• Author(s): Alan R. Jones
• Release date: October 2018
• Publisher(s): Routledge
• ISBN: 9781351661447