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Probability, Statistics and Other Frightening Stuff

By: (Author) Alan Jones

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Ksh 13,850.00

Format: Hardback or Cased Book

ISBN-10: 113806503X

ISBN-13: 9781138065031

Series: Working Guides to Estimating & Forecasting

Publisher: Taylor & Francis Ltd

Imprint: Routledge

Country of Manufacture: GB

Country of Publication: GB

Publication Date: Sep 10th, 2018

Print length: 500 Pages

Weight: 862 grams

Dimensions (height x width x thickness): 16.30 x 24.10 x 3.20 cms

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This practical guide to probability and statistics explains these concepts that underpin all professional estimating. Alan Jones considers what are called Measures of Central Tendency; Means, Modes and Medians, describing the differences, relevance and uses for each in the context of Estimating and the like.

Probability, Statistics and Other Frightening Stuff (Volume II of the Working Guides to Estimating & Forecasting series) considers many of the commonly used Descriptive Statistics in the world of estimating and forecasting. It considers values that are representative of the ‘middle ground’ (Measures of Central Tendency), and the degree of data scatter (Measures of Dispersion and Shape) around the ‘middle ground’ values.

A number of Probability Distributions and where they might be used are discussed, along with some fascinating and useful ‘rules of thumb’ or short-cut properties that estimators and forecasters can exploit in plying their trade. 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.

Under 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. This is a valuable resource for estimators, engineers, accountants, project risk specialists as well as students of cost engineering.


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