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Demand estimating model to forecast the building material requirements for the construction and allied industries in Sri Lanka

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dc.contributor.advisor
dc.contributor.author Maduranga, WWDD
dc.contributor.author Nithushan, N
dc.contributor.author Jayasinghe, JKSN
dc.contributor.author Dissanayake, DMDOK
dc.contributor.editor Abeysinghe, AMKB
dc.contributor.editor Dassanayake, ABN
dc.contributor.editor Elakneswaran, Y
dc.date.accessioned 2017-10-27T13:59:02Z
dc.date.available 2017-10-27T13:59:02Z
dc.identifier.citation Maduranga, W.W.D.D., Nithushan, N., Jayasinghe, J.K.S.N., & Dissanayake, D.M.D.O.K (2017). Demand estimating model to forecast the building material requirements for the construction and allied industries in Sri Lanka. In A.M.K.B. Abeysinghe, A.B.N. Dassanayake & Y. Elakneswaran (Eds.), Proceedings of International Symposium on Earth Resources Management & Environment 2017 (pp. 203-210). Department of Earth Resources Engineering, University of Moratuwa.
dc.identifier.uri http://dl.lib.mrt.ac.lk/handle/123/12826
dc.description.abstract Over the past few years, there has been a high level of interest in modelling demand estimation for the construction and allied industries in Sri Lanka. Demand estimation is a process that involves coming up with an estimate of the level of demand for a product or service and, typically confined to a particular period of time, a month, quarter or year. Demand estimation methods can be categorized into two main categories according to the technique applied to analyse data. Different approaches are survey methods and statistical methods. For a good quantitative analysis, statistical methods are more preferable. Regression analysis method which comes under econometric statistical method is more preferable to develop demand estimation models since it has high accuracy level. In the regression analysis, there are two methods to develop the model. These are Single Regression Analysis and Multiple Regression Analysis. The few steps to develop the estimation models are statement of a theory or hypothesis, model specification, data collection, estimation of parameters, checking goodness of it, hypothesis testing and forecasting. In this project, there are two models for sand and ABC materials each and the first model is for dust and ¾ particle size. Developed model for chip particles was rejected due to inaccuracy of the unavailability of required data. In validation, sand and ¾ particle size have shown very high accuracy when as dust and ABC has shown quite lower accuracy. en_US
dc.language.iso en en_US
dc.subject Demand estimation en_US
dc.subject Regression analysis
dc.subject Time series analysis
dc.title Demand estimating model to forecast the building material requirements for the construction and allied industries in Sri Lanka en_US
dc.type Conference Full-text en_US
dc.identifier.faculty Engineering en_US
dc.identifier.department Department of Earth Resources Engineering en_US
dc.identifier.year 2017 en_US
dc.identifier.conference International Symposium on Earth Resources Management & Environment 2017 en_US
dc.identifier.place Wadduwa en_US
dc.identifier.pgnos pp. 203-210 en_US
dc.identifier.proceeding
dc.identifier.proceeding Proceedings of International Symposium on Earth Resources Management & Environment 2017
dc.identifier.proceeding
dc.identifier.email [email protected] en_US


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