Calibrating Data

Mario Vanhoucke
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Mario Vanhoucke: Ghent University

Chapter Chapter 14 in The Illusion of Control, 2023, pp 257-282 from Springer

Abstract: Abstract This chapter presents a new way to calibrate real project data to prepare them for academic use. Three different calibration methods are discussed to translate the data from real completed projects into probability distributions with accurate parameter estimates. With these distributions, accurate predictions can be made for new projects where, thanks to the use of real data during calibration, fewer (incorrect) assumptions have to be made. The calibration methods consist of a balanced mix of statistical hypothesis testing, methods for reducing human bias, and partitioning methods to split the projects into clusters. Computational experiments show that the accuracy of these calibration procedures reaches an impressive 97%.

Keywords: Data calibration; Human bias; Estimates; Project partitioning; Statistical analysis (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:mgmchp:978-3-031-31785-9_14

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DOI: 10.1007/978-3-031-31785-9_14

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