Canonical Correlation and Loglikelihood Ratio Analysis Applied to the Development of Crude Oil Products Evaluation
[1]
Shamsuddeen Idris Musa, General Studies, Emirates College of Health, Kano, Nigeria.
[2]
Usman Muhammad Tukur, General Studies, Emirates College of Health, Kano, Nigeria.
Investigation of whether the development of the Crude Oil Products depends over time or not and obtain the correlation matrix for the Crude Oil Products in Kaduna Refining and Petrochemical Company LTD (KRPC). Planning activities is very necessary in all organizations especially in oil producing companies because it helps to overcome the challenges in the future, looking at what had happened in the past, now and then make suggestions ahead. Statistical methodologies are concerned with making inference from observed data. Null hypothesis (Ho): The development of Crude Oil Products in KRPC is independence of the years. Alternate hypothesis (H1): The development of Crude Oil Products in KRPC is depending on the years. Canonical Correlation and Log-likelihood ratio test, Wilks lamda Criterion, and Correlation Matrix were used to analyzed the results using SPSS latest version. It reveals that a strong relationship exists between Xs variables Liquefied Petroleum Gas (LPG), Premium Motor Spit (PMS), Dual Purpose Kerosene (DPK) and Ys variables Automated Gas Oil (AGO), Low Pour Fuel Oil (LPFO), and Asphalt (A). The highest Co-efficient (CV1) is significant enough to conclude that there is a relationship between the Xs variables and Ys variables in terms of Development, and a Strong relationship exists between Crude Oil Products in Kaduna Refining and Petrochemical Company over time frame which is Statistically Significant (p<0.05). Although, the association between the number of years and the development of crude oil product in KRPC is (98.4%). Kaduna Refining and Petrochemical Company (KRPC) should notice the changes in the development of Crude Oil Products in the Company over the year so that it will enable them to plan ahead appropriately.
Canonical Correlation, Loglikelihood Ratio, Crude Oil products, Correlation Matrix, Refinery
[1]
Aka U., U. (1980). “Management for dynamic Economy” Centre for management Lagos, Nigeria.
[2]
Anderson D., R. (1941). “Statistics for Business and Economics” 6th edition library of Congress Cataloging – in – Publication data.
[3]
Augh and Singh, (2005). ‘Methods for Statistical Data Analysis of Multivariate Observations’. Wiley, New York.
[4]
Bower man B., L. (1997). “Applied Statistics” Improving Business Process. The Mc Graw Hill Companies Inc.
[5]
Cooley, W., W. and Lohnes, P., R. (1971). Multivariate Data Analysis, New York: John Wiley and Sons, Inc.
[6]
Fisher, R., A. (1938). Statistical Methods for Research Workers, Tenth Edition, Edinburgh: Oliver and Boyd.
[7]
Hanson, R., J. and Norris, M., J. (1981). “Analysis of Measurements Based on the Singular Value Decomposition,” SIAM Journal of Scientific and Statistical Computing, V (2), 363–373.
[8]
Helland, I., S. (1987). “On the Interpretation and Use of R2 in Regression Analysis,” Biometrics, V (43), 61–69.
[9]
Hotelling, H. (1935). “The Most Predictable Criterion,” Journal of Educational Psychology, V (26), 139–142.
[10]
Hotelling, H. (1936). “Relations between Two Sets of Variables,” Biometrika, V (28), 321–377.
[11]
Saul, P., and Zvi, R. (1999). The Chemistry of Organic Derivatives of Gold and Silver. Copyright 1999 John Wiley and Sons, Ltd. 10ISBN 0 - 471-98164-8.
[12]
Sigel, A., Sigel, H., Sigel, R., K., O. (2010). “Organometallics in Environment and Toxicology”. Metal ions in life sciences 7. Cambridge: RSC Publishing. ISBN 978-1-84755-177-1.
[13]
Omari, H. E., Boutaleb, N., Bahlaouan, B., Oualich, S., and Jrifi, A. (2017). Drinking water and Oil pipeline: New PVC formulation anti-biofilm for the Moroccan industry, 8 (12), 4444–4450.
[14]
NACE., (2003). Petroleum and Natural Gas Industries–Materials for use in H2S Containing Environments in Oil and Gas Production–Part 3: Cracking Resistant CRAs (Corrosion Resistant Alloys) and other Alloys. NACE, Houston. Shiwei, W. G., Gritis N., Jackson Augh, A., and Singh, P. (2005). Advanced Onshore and Offshore Pipeline Coating Technologies. 2005 China International Oil and Gas Technology Conference and Expo, Shangai, China.
[15]
Westood, J., (2011). Macro Factors Driving the Global Oil and Gas Industry and the Subsea Pipelines Sector. Toronto.
[16]
Diaconis, P., and Efron, B., (1983). Computer intensive methods in statistics. Scientific American, May 1983, 96–108.
[17]
Dunn, W. J., and Wold, S., (1980). Structure - activity analyzed by pattern recognition: The asymmetric case. J. Med. Chem. 23, 595.
[18]
Draper, N. R., and Smith, H., (1 981). Applied regression analysis, 2. nd edition. Wiley, New York.
[19]
Dunn, W. J., and Wold, S., (1980). Relationships between chemical structure and biological activity modelled by SIMCA pattern recognition. Bioorg. Chem. 9, 505–23.