Every year the Air Force spends millions of dollars to send personnel through Undergraduate Pilot Training (UPT). To be accepted into UPT, a candidate must go through a selection process to ensure the Air Force is choosing personnel best suited to succeed in the rigorous training environment. One of the key factors provided to the selection board is the Pilot Candidate Selection Method (PCSM) score. The PCSM weights a candidate's Air Force Officer Qualification Test (AFOQT) score, Federal Aviation Administration (FAA) flying hours, and a Basic Attributes Test (BAT) score to provide a single score between 1 and 99. The goal of this study was to apply a full range of multivariate data analysis techniques as well as current pattern recognition practices to the provided data to determine an appropriate model to predict pilot success. The new model was validated to establish its predictive accuracy, capabilities, and limits. In order to avoid bias, the model development process did not make any comparisons to the current PCSM model. However, a post-development comparison was made to determine relative model performance. The performance of the new model was superior to the existing model. The difference in performance between the two models is shown to be statistically and practically significant.
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Paperback. Condición: new. Paperback. Every year the Air Force spends millions of dollars to send personnel through Undergraduate Pilot Training (UPT). To be accepted into UPT, a candidate must go through a selection process to ensure the Air Force is choosing personnel best suited to succeed in the rigorous training environment. One of the key factors provided to the selection board is the Pilot Candidate Selection Method (PCSM) score. The PCSM weights a candidate's Air Force Officer Qualification Test (AFOQT) score, Federal Aviation Administration (FAA) flying hours, and a Basic Attributes Test (BAT) score to provide a single score between 1 and 99. The goal of this study was to apply a full range of multivariate data analysis techniques as well as current pattern recognition practices to the provided data to determine an appropriate model to predict pilot success. The new model was validated to establish its predictive accuracy, capabilities, and limits. In order to avoid bias, the model development process did not make any comparisons to the current PCSM model. However, a post-development comparison was made to determine relative model performance. The performance of the new model was superior to the existing model. The difference in performance between the two models is shown to be statistically and practically significant.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Nº de ref. del artículo: 9781025080840
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