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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 郭佳瑋(Chia-Wei Kuo) | |
| dc.contributor.author | Robin Bunker | en |
| dc.contributor.author | 柏洛賓 | zh_TW |
| dc.date.accessioned | 2021-06-15T06:20:02Z | - |
| dc.date.available | 2010-08-13 | |
| dc.date.copyright | 2010-08-13 | |
| dc.date.issued | 2010 | |
| dc.date.submitted | 2010-08-10 | |
| dc.identifier.citation | 1. Basic Business Statistics- Mark L. Berenson, David M. Levine, Timothy C. Krehbiel
2. Khamis, A., I. Zuhaimy, H. Khalid and M. Ahmad Tarmizi, (2006). Modeling oil palm yield using multiple linear regression and robust m-regression. J. Agron., 5: 32-36 3. Andrew, D.F., 1974. A robust method for multiple linear regression. Technometrics, 16: 523-551. 4. Barnett, V. and T. Lewis, 1995. Outliers in Statistical Data. 3rd Edn., John Wiley and Sons, England, pp: 584. 5. Fairhurst, T.H. and E. Mutert, 1999. Interpretation and management of oil palm leaf analysis data. Better Crops Int., 13: 48-51. 6. Ismail, Z. and F. Jamaluddin, 2008. Time series regression models for forecasting Malaysian electricity load demand. Asian J. Math. Stat., 1: 139-149 7. Taylor, J.W. and R. Buizza, 2003. Using weather ensemble predictions in electricity demand forecasting. Int. J. Forecasting, 19: 57-70. 8. Da Silva, C.G., 2008. Time series forecasting with a non-linear model and the scatter search meta-heuristic. Inform. Sci., 178: 3288-3299. 9. Dr. Khaled A. Abbas, Conceptual and Regression Models for Passenger Demand Prediction: A case study of Cairo Airport and Egyptair, 10. Egyptian Civil Aviation Authority (ECAA) (2001) ECAA Statistical Year Book. Cairo, Egypt. 11. Profillidis V. A. (2000) Econometric and Fuzzy Models for the Forecast of Demand in the Airport of Rhodes. Journal of Air Transport Management, Vol. 6, pp. 95-100. 12. Mariana Kaznovsky (University of Economics, Bucharest, Romania Monetary and Financial Statistics Division, National Bank of Romania, Bucharest, Romania) Money Demand In Romanian Economy, Using Multiple Regression Method And Unrestricted VAR Model 13. Friedman, M. The optimum quantity of money and other essays, Aldine, Chicago, 1969 14. Sriram, S. S. A survey of recent empirical money demand studies, International Monetary Fund, 2001 15. In Miaou, S.-P. (1990), A Stepwise Time Series Regression Procedure for Water Demand Model Identification, Water Resour. Res., 26(9), 1887–1897, 16. Abraham, B. and Ledolter, J., 1983. Statistical Methods for Forecasting. , Wiley, New York. 17. Provisional Patent Application Ser. No. 61/142,025, entitled 'Method For Updating Regression Coefficients In a Causal Product Demand Forecasting System' by Arash Bateni, Edward Kim, Philippe Dupuis Hamel, and Stephen Szu Chang; filed on Dec. 31, 2008. 18. Application Ser. No. 11/613,404, entitled 'Improved Methods and Systems for Forecasting Product Demand Using a Causal Methodology,' filed on Dec. 20, 2006, by Arash Bateni, Edward Kim, Philip Liew, and J. P. Vorsanger; 19. Application Ser. No. 11/967,645, entitled 'Techniques for Causal Demand Forecasting,' filed on Dec. 31, 2007, by Arash Bateni, Edward Kim, J. P. Vorsanger, and Rong Zong. 20. Bruce Schaller. Schaller Consulting A Regression Model of the Number of Taxicabs in U.S. Cities,(2005) 21. Taylor, Brian D. and Camille Fink. 2003. The Factors Influencing Transit Ridership: An Analysis of the Literature, Working Paper, UCLA Institute of Transportation Studies, UCLA. 22. K.E. Kioulafas, An application of multiple regression analysis to the Greek beer market, J. Oper. Res. Soc. 36(8) (1985) 689–696 23. M. SAMUELS (1971) The effect of advertising on sales and brand shares. Br. J. Advert.4, 187-207. 24. J. SIMON (1969) The effect of advertising on liquor brand sales. J. Mktg Res.6, 301-313. 25. Morphet, C. S. (1991) Applying multiple regression analysis to the forecasting of grocery store sales: an application and critical appraisal. International Review of Retail, Distribution and Consumer Research 1:3 , pp. 329-380, 26. Davies, R. L. (1977b) 'Store Location and Store Assessment Techniques: the integration of some new and traditional techniques', Transactions of the Institute of British Geographers, 2: 141-57. 27. Nelson, R. L. (1958) The selection of Retail Locations, New York: Dodge. 28. Selvanathan, E.A., 1991. A note on the accuracy of business economists gold price’s forecast. Aust. J. Manage., 16: 91-95. http://www.agsm.edu.au/eajm /9106/pdf/selvanathan.pdf 29. Mark Bujarski, Seth Kussmaul, Sharief Luqman, Andy Mcnary, Sundar Muthuvelu, Northern Illinois University, Invensys Optimization of Copper Pricing (2008) 30. New York State Energy Plan June 2002, All Fuels demand and Price Forecast Methodology 31. Rene Lalonde, Zhenhua Zhu. Frederick Demers. Oct 18 2002. Forecasting and Analyzing World Commodity Prices 32. United Nations Educational, Scientific and Cultural Organization. 33. http://www.Graphpad.com | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/47812 | - |
| dc.description.abstract | In order to develop a regression model to forecast iron-ore price, CVRD & Baosteel annual contract iron ore prices were used for IOF price i.e., the dependent variable. Twelve factors were identified to have influence on the IOF price as independent variables in the regression model.
In the process of developing Multi Regression Model, assumptions of – Linearity, Independence, Normality and Equal Variance were tested. It was found that multicollinearity among independent variables was the main problem. Stepwise regression was proposed to resolve this. The stepwise procedures successfully solved the problem of multicollinearity by reducing the total number of independent variables to four. The variables selected by stepwise regression were Oil Price, Production of Steel in China, World Steel Exports and China Iron Ore Production. The adjusted coefficient of correlation remained almost same. The results obtained with Stepwise Regression Model were very encouraging for years 2006-2007 and further research was suggested to overcome certain limitations. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T06:20:02Z (GMT). No. of bitstreams: 1 ntu-99-R97749059-1.pdf: 1919202 bytes, checksum: 13f2091742e5fbeb297df45dafb7b12a (MD5) Previous issue date: 2010 | en |
| dc.description.tableofcontents | TABLE OF CONTENTS
List of Figures ii List of Tables iii Chapter 1: Introduction 3 1.1 Iron Ore 3 1.1 Brief History of Iron Ore Trade 5 1.2 Motivation 9 1.3 Summary of Chapters 10 Chapter 2: Literature Review 11 Chapter 3: Multi Regression Model 18 3.1 Introduction 18 3.2 Data 19 3.3 Assumptions 19 3.4 Developing Multi Regression Model 27 3.4.1 Fit of the regression model 28 3.4.2 Statistical inferences for the model 30 3.4.3 ANOVA Table for Multiple Regression 31 3.5 Interim analysis of results 32 3.6 Stepwise Regression Model 33 Chapter 4: Analysis of Results 38 4.1 Results from Step-wise Regression Model 38 4.2 Validation of Step-wise Regression Model 38 Chapter 5: Conclusion 43 Bibliography 44 Appendix 1 47 | |
| dc.language.iso | en | |
| dc.subject | 鐵礦石 | zh_TW |
| dc.subject | 鐵礦 | zh_TW |
| dc.subject | 回歸模型 | zh_TW |
| dc.subject | 預測 | zh_TW |
| dc.subject | Iron-ore | en |
| dc.subject | Forecasting | en |
| dc.subject | Multi-Regression Model | en |
| dc.title | 利用多元回歸模型預測鐵礦石價格 | zh_TW |
| dc.title | Forecasting Iron-ore Prices Using Multi-Regression Model | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 98-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 余峻瑜,黃奎隆 | |
| dc.subject.keyword | 鐵礦,回歸模型,預測,鐵礦石, | zh_TW |
| dc.subject.keyword | Iron-ore,Multi-Regression Model,Forecasting, | en |
| dc.relation.page | 48 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2010-08-10 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 企業管理碩士專班 | zh_TW |
| 顯示於系所單位： | 管理學院企業管理專班(Global MBA) | |
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| ntu-99-1.pdf 未授權公開取用 | 1.87 MB | Adobe PDF |
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