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dc.contributor.authorCheng, Yuanzhi.
dc.creatorCheng, Yuanzhi.en_US
dc.date.accessioned2011-10-31T18:14:48Z
dc.date.available2011-10-31T18:14:48Z
dc.date.issued1994en_US
dc.identifier.urihttp://hdl.handle.net/10150/186631
dc.description.abstractThis study generalizes the applications of learning curve theory. It extends the simple power learning model in two ways: (1) by extending the model to include other sift variables, the extensive learning model; (2) by generalizing the functional relationship to give greater flexibility in modelling the learning curve, the translog learning model. Through empirical analyses of gross domestic product, total energy consumption, petroleum consumption, and petroleum products consumption, different learning curve models are explored and compared.
dc.language.isoenen_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.subjectDissertations, Academic.en_US
dc.subjectPetroleum.en_US
dc.subjectEnergy consumption.en_US
dc.titleForecasting by learning methods: The gross domestic product, total energy consumption and petroleum consumption of the United States.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.contributor.chairRieber, Michaelen_US
dc.identifier.oclc722013226en_US
thesis.degree.grantorUniversity of Arizonaen_US
thesis.degree.leveldoctoralen_US
dc.contributor.committeememberNewcomb, Richard T.en_US
dc.contributor.committeememberHarris, DeVerleen_US
dc.identifier.proquest9424964en_US
thesis.degree.disciplineMining and Geological Engineeringen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.namePh.D.en_US
refterms.dateFOA2018-06-16T15:29:45Z
html.description.abstractThis study generalizes the applications of learning curve theory. It extends the simple power learning model in two ways: (1) by extending the model to include other sift variables, the extensive learning model; (2) by generalizing the functional relationship to give greater flexibility in modelling the learning curve, the translog learning model. Through empirical analyses of gross domestic product, total energy consumption, petroleum consumption, and petroleum products consumption, different learning curve models are explored and compared.


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