@article{(Open Science Index):https://publications.waset.org/pdf/10010964,
	  title     = {A Multiple Linear Regression Model to Predict the Price of Cement in Nigeria},
	  author    = {Kenneth M. Oba},
	  country	= {},
	  institution	= {},
	  abstract     = {This study investigated factors affecting the price of cement in Nigeria, and developed a mathematical model that can predict future cement prices. Cement is key in the Nigerian construction industry. The changes in price caused by certain factors could affect economic and infrastructural development; hence there is need for proper proactive planning. Secondary data were collected from published information on cement between 2014 and 2019. In addition, questionnaires were sent to some domestic cement retailers in Port Harcourt in Nigeria, to obtain the actual prices of cement between the same periods. The study revealed that the most critical factors affecting the price of cement in Nigeria are inflation rate, population growth rate, and Gross Domestic Product (GDP) growth rate. With the use of data from United Nations, International Monetary Fund, and Central Bank of Nigeria databases, amongst others, a Multiple Linear Regression model was formulated. The model was used to predict the price of cement for 2020-2025. The model was then tested with 95% confidence level, using a two-tailed t-test and an F-test, resulting in an R2 of 0.8428 and R2 (adj.) of 0.6069. The results of the tests and the correlation factors confirm the model to be fit and adequate. This study will equip researchers and stakeholders in the construction industry with information for planning, monitoring, and management of present and future construction projects that involve the use of cement.
},
	    journal   = {International Journal of Economics and Management Engineering},
	  volume    = {13},
	  number    = {12},
	  year      = {2019},
	  pages     = {1482 - 1487},
	  ee        = {https://publications.waset.org/pdf/10010964},
	  url   	= {https://publications.waset.org/vol/156},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 156, 2019},
	}