A comparison Between Principal Component Regression and Partial Least Squares Regression Methods with application in The Kirkuk Cement
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Abstract
Appear in Many Application Areas for Regression Analysis and Presence the case of More Than One Variable Dependent Affected by A variety of Explanatory Variable and at The Same Time The Number of Observation is Relatively Small Compared to The Number of Variables, and Show Here The Problem of not Provide Offers Several Hypotheses Multiple Regression Analysis, More Over Prominence Problem of Multicollinearity between the Explanatory Variables Beside The Correlation between The Explanatory Variables and The Dependent Variables and Reflexive that on the Regression Estimates. and in This Research was Dealing with Problems from this Type Related to Variables Kirkuk Cement Factory, used the Methods, Principal Component PC and Partial Least Squares PLS to Solve the Problems Above, The First Method is Considered as one of the Commonest Methods used in Solving the Problem of Multicollinearity between the Explanatory Variables and the Second Method is Considered as one of the Methods Which Dally Methodically Different in Deduction the Components Dependent on Curing The Correlation the Presence between The Explanatory Variables and The Dependent Variables. Through the Statistical Analysis, Orphan Conduction to The PLS Method it has Succeeded in Establishing the Optimal Regression Model for all Depended Variables, Besides Superiority This Method Whence Ability on the Prediction for Futuristic Values Apiece Dependent Variables and Also Whence Dimension Reduction. The (Minitab, Version, 16.1) is used in the Statistical Analysis for the Data of this Research .
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References
[1] Bluman , Allan G., "Elementary Statistics (A step by step Approach) ", Seventh Edition , Published by MC Graw – Hill , Obusiness Unit of The MC Graw – Hill Companies , New York , (2009) .
[2] Maitra, Saikat and Yan, Jun, "Principle Component Analysis and Partial Least Squares: Two Dimension Reduction Techniques for Regression" , Casualty Actuarial Society , (2008), pp (79 – 90) .
[4] Poole, Michael A., Farrell, Patrick N.O., "The Assumptions of the Linear Regression Model ", Received , 10 July (1970), pp (145-157) .
[6] Henry , Gary T., "Practical Sampling", Chapter Title , Sample Size, Publishing Company, SAGE Publications, Inc., City, Thousand oaks , 14 October (2013), http://dx.doi.org//0.4135/9781412985451.n7,pp(1-15)
[9] Disatnik, David and Sivan, Liron, "The Multicollinearity illusion in Moderated Regression Analysis",Tel Aviv University, Carnegie Mellon University, USA., (2014), PP(1-12).
[10] Jeeshim and Kucc, "Multicollinearity in Regression Models", No. 625, (2002), pp (1-8), http:// php. Indiana. edu /~ kucc 625 .
[13] Rawlings, John O., Pantula, Sastry G. and Dickey, David A., "Applied Regression Analysis", A research Tool, Second Edition, Berlin, New york, (1998) .
[15] Akdeniz, Fikri, "III-Conditionaling and Multicollinearity", Department of Mathematics, Cukurova University, Adana, Turkey, Linear Algebra and its Applications, 321, (2000), pp(295-305), E-mail: akdeniz@mail.cu.edu.tr (F.akdeniz) .
[19] Chatterjee, Samprit and Hadi, Ali S., " Regression Analysis by Example", Fourth Edition, Copyright © John wiley & Sons, Inc., Printed in the United States of America, (2006) .
[20] Travaglini, Guido, " Principal Components and Factor Analysis–Acomparative Study" , University of La Sapienza , Roma, Italy , Second Draft , MPRA Paper, No. 35486, Posted. 20, 13 October (2011), http://Mpra.Ub.Uni-Muenchen. de /35486/ .
[21] Nguyen, Danh V. and Rocke, David M., "on Partial Least Squares Dimension Reduction for Microarray–Based Classification: Asimulation Study", University of California, Davis. USA, Computational Statistics and Data Analysis , (2004), pp (407 – 425), E-mail: Ucdnguyen@ucdavis. edu. .
[22] Garthwaite, Paul H., "An Interpretation of Partial Least Squares", Journal of The American Statistical Association, Theory and Method, Vol. 89, No. 425, (1994), pp (122–127) .
[23] Geladi, Paul and Kowalski, Brucer, "Partial Least Squares Regression: A tutorial", University of Washington, Seattle, WA 98195 (U. S. A.), Printed in The Netherlands , (1986), pp (1–17) .
[24] Cavanaugh, Joseph E., "The Akaike Information Criterion", Department of Statistics and Actuarial Science , The University of Iowa, (2012).
[25] Makridakis, Spyros, Wheelwright, Steven C. and Hyndman, Rob J., "Forecasting ", Methods and Applications, 3rd Edition, New york, (1998)