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Estimating the unemployment rate using least square and conjugate gradient methods

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dc.contributor.author Nur Syarafina Mohamed
dc.contributor.author Mustafa Mamat
dc.contributor.author Mohd Rivaie
dc.contributor.author Nur Hamizah Abdul Ghani
dc.contributor.author Norhaslinda Zull
dc.contributor.author Syazni Syoid
dc.contributor.author (UniKL MITEC)
dc.date.accessioned 2018-05-08T08:10:47Z
dc.date.available 2018-05-08T08:10:47Z
dc.date.issued 2018-05-08
dc.identifier.other International Journal of Engineering & Technology
dc.identifier.other www.sciencepubco.com/index.php/IJET
dc.identifier.uri http://ir.unikl.edu.my/jspui/handle/123456789/18896
dc.description.abstract Unemployment rate is one of the major issues among Malaysian citizens. The unemployment rate indicates the percentage of the total workforce who are actively seeking employment and currently unemployed. In this paper, a data of unemployment rate of a state in Malaysia from year 2000 until 2015 is collected. The statistics data is extracted by Labour Force Survey Malaysia (LFSM) which was conducted monthly by using household approach targeted to working ages between 15 to 64 years old. An estimation data for year 2016 can be forecasted by using discrete least square method of numerical analysis and conjugate gradient method in unconstrained optimization. These methods have been chosen based on its simplicity and accuracy. The calculations are based on linear and quadratic models for each the method together with their errors. Results showed that the conjugate gradient method is comparable with the least square method. en_US
dc.language.iso en en_US
dc.relation.ispartofseries 7 (2.15) (2018) 94-97;
dc.subject Conjugate gradient method en_US
dc.subject Least square method en_US
dc.subject Unconstrained optimization en_US
dc.subject Workforce en_US
dc.title Estimating the unemployment rate using least square and conjugate gradient methods en_US


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