Book description
The text considers Simple Linear Regression and ask what is it doing? how good is it? how accurate is it? and how can we use it to create estimates? The book considers all this and looks at how you can exploit this transformability and use the capability of Simple Linear Regression.
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 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
Product information
- Title: Best Fit Lines & Curves
- Author(s):
- Release date: October 2018
- Publisher(s): Routledge
- ISBN: 9781351661430
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