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It clarifies\u003ci\u003e \u003c\/i\u003ethe difference between an Estimating Process, Procedure, Approach, Method and\u003ci\u003e \u003c\/i\u003eTechnique. It expands on these definitions of Approach (Top-down, Bottom-up and\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e‘Ethereal’) and Method (Analogy, Parametric and ‘Trusted Source’) and discusses how these form the basis of all other means of establishing an estimate.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis volume also underlines the importance of ‘data normalisation’ in any estimating procedure, and demonstrates that the Estimating by Analogy Method, in essence, is a simple extension of Data Normalisation. The author looks at simple measures of assessing the maturity or health of an estimate, and offers a means of assessing a spreadsheet for any inherent risks or errors that may be introduced by failing to follow good practice in spreadsheet design and build.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis book provides a taster of the more numerical techniques covered in the remainder of the series by considering how an estimator can potentially exploit Benford’s Law (traditionally used in Fraud Detection) to identify systematic bias from third party contributors. 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With the help of a ‘Correlation Chicken’, the concept of partial correlation is explained, including how the estimator or forecaster can exploit this in reflecting varying levels of independence and imperfect dependence between an output or predicted value (such as cost) and an input or predictor variable such as size.\u003c\/p\u003e\u003cp\u003eUnder the guise of ‘Tails of the unexpected’ the book concludes with two chapters devoted to Hypothesis Testing (or knowing when to accept or reject the validity of an assumed estimating relationship), and a number of statistically-based tests to help the estimator to decide whether to include or exclude a data point as an ‘outlier’, one that appears not to be representative of that which the estimator is tasked to produce. 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