A comparison Between Principal Component Regression and Partial Least Squares Regression Methods with application in The Kirkuk Cement
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...
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| Language: | English |
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Tikrit University
2023-02-01
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| Series: | Tikrit Journal of Pure Science |
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| Online Access: | https://tjpsj.org/index.php/tjps/article/view/1126 |
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| author | Thafer Ramathan Muttar AL-Badrany Takwa Abdulsalam Taha AL-Mola |
| author_facet | Thafer Ramathan Muttar AL-Badrany Takwa Abdulsalam Taha AL-Mola |
| author_sort | Thafer Ramathan Muttar AL-Badrany |
| collection | DOAJ |
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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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| format | Article |
| id | doaj-art-f3fbf209d6da4d11aafa350202ad6dfd |
| institution | OA Journals |
| issn | 1813-1662 2415-1726 |
| language | English |
| publishDate | 2023-02-01 |
| publisher | Tikrit University |
| record_format | Article |
| series | Tikrit Journal of Pure Science |
| spelling | doaj-art-f3fbf209d6da4d11aafa350202ad6dfd2025-08-20T02:36:34ZengTikrit UniversityTikrit Journal of Pure Science1813-16622415-17262023-02-0121710.25130/tjps.v21i7.1126A comparison Between Principal Component Regression and Partial Least Squares Regression Methods with application in The Kirkuk Cement Thafer Ramathan Muttar AL-BadranyTakwa Abdulsalam Taha AL-Mola 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 . https://tjpsj.org/index.php/tjps/article/view/1126comparisonPrincipal ComponentPartial Least SquaresapplicationCement Factory |
| spellingShingle | Thafer Ramathan Muttar AL-Badrany Takwa Abdulsalam Taha AL-Mola A comparison Between Principal Component Regression and Partial Least Squares Regression Methods with application in The Kirkuk Cement Tikrit Journal of Pure Science comparison Principal Component Partial Least Squares application Cement Factory |
| title | A comparison Between Principal Component Regression and Partial Least Squares Regression Methods with application in The Kirkuk Cement |
| title_full | A comparison Between Principal Component Regression and Partial Least Squares Regression Methods with application in The Kirkuk Cement |
| title_fullStr | A comparison Between Principal Component Regression and Partial Least Squares Regression Methods with application in The Kirkuk Cement |
| title_full_unstemmed | A comparison Between Principal Component Regression and Partial Least Squares Regression Methods with application in The Kirkuk Cement |
| title_short | A comparison Between Principal Component Regression and Partial Least Squares Regression Methods with application in The Kirkuk Cement |
| title_sort | comparison between principal component regression and partial least squares regression methods with application in the kirkuk cement |
| topic | comparison Principal Component Partial Least Squares application Cement Factory |
| url | https://tjpsj.org/index.php/tjps/article/view/1126 |
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