Legend for Microsoft Excel Worked Example Tables in Greyscale
by Alan R. Jones
Best Fit Lines & Curves
- 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 Formula-philes
- 1.2.6 Discussions and explanations without a mathematical slant for Formula-phobes
- 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 Mathe-Magical Transformations
- 1.4.4 Volume IV: Learning, Unlearning and Re-learning 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 (R-Square): A measure of linearity?!
- 4.5.2 F-Statistic: A measure of chance occurrence
- 4.5.3 t-Statistics: 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 multi-linear transformation
- 6.3 Stepwise Regression and multi-linear transformations
- 6.4 Is the Best Fit really the better fit?
- 6.5 Regression of Transformed Generalised Nonlinear Functions
- 6.6 Pseudo Multi-linear 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 Multi-Linear 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
Legend for Microsoft Excel Worked Example Tables in Greyscale
| Cell type | Potential Good Practice Spreadsheet Modelling Colour | Greyscale used in Book | Example of Greyscale Used in Book |
|---|---|---|---|
| Header or Label | Liqht Grey | Text on grey | Text |
| Constant | Deep blue | Bold white numeric on black | 1 |
| Input | Pale Yellow | Normal black numeric on pale grey | 23 |
| Calculation | Pale Green | Normal black numeric on mid grey | 45 |
| Solver variable | Lavender | Bold white numeric on mid grey | 67 |
| Array formula | Bright Green | Bold white numeric on dark grey | 89 |
| Random Number | Pink | Bold black numeric on dark grey | 0.0902 |
| Comment | White | Text on white | Text |
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