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Please use this identifier to cite or link to this item: https://ah.lib.nccu.edu.tw/handle/140.119/51316
DC FieldValueLanguage
dc.contributor.advisor吳柏林zh_TW
dc.contributor.author林立夫zh_TW
dc.creator林立夫zh_TW
dc.date2010en_US
dc.date.accessioned2011-10-05T06:39:45Z-
dc.date.available2011-10-05T06:39:45Z-
dc.date.issued2011-10-05T06:39:45Z-
dc.identifierG0097972014en_US
dc.identifier.urihttp://nccur.lib.nccu.edu.tw/handle/140.119/51316-
dc.description碩士zh_TW
dc.description國立政治大學zh_TW
dc.description應用數學系數學教學碩士在職專班zh_TW
dc.description97972014zh_TW
dc.description99zh_TW
dc.description.abstract　　兩變數之間是否相關，以及相關的程度與方向是統計研究學者所關注的一項課題。傳統上使用皮爾森相關係數(Pearson’s Correlation Coefficient)來表達兩實數變數間線性關係的強度與方向。然而，對於反映人類思維不確定性的模糊資料而言，傳統的相關分析方法卻有不足與不適用之缺失。\n　　本論文的主要目的在於尋求一個合理、適用的區間模糊資料相關係數，提供研究者簡單且容易計算的模糊相關係數求法，用以了解區間模糊資料間的相關程度。接著利用轉換離散型模糊數成為區間模糊數的方式，處理離散型模糊資料間的相關係數。最後，以國中數學教學現場所調查的資料做實例應用。zh_TW
dc.description.abstract　　In statistical studies, the correlation between two variables and its strength and direction are always concerned. Traditionally, the Pearson’s Correlation Coefficient is used to convey the linear relationship between two variables. However, the traditional correlation analysis is not applicable to the fuzzy data which are able to reflect more appropriately the uncertainty of human thinking.\n　　The main purpose of the study is to find a reasonable and usable correlation coefficient of interval fuzzy data which provides researchers a simple and easy way to calculate and find the fuzzy correlation coefficient. Meanwhile, it can help us understand the correlation of interval fuzzy data. Moreover, we use the process of transforming discrete fuzzy number into the interval fuzzy number to deal with the correlation coefficient of discrete fuzzy data. Finally, we utilize the data from mathematics teaching in junior high school for application.en_US
dc.description.tableofcontents摘要.....................................................i\nAbstract................................................ii\n目次.....................................................iii\n圖目次....................................................iv\n表目次....................................................v\n1.前言....................................................1\n2.研究方法.................................................3\n 2.1傳統實數樣本之線性相關係數.................................3\n 2.2模糊統計量..............................................5\n 2.3區間模糊數與軟計算.......................................15\n 2.4模糊線性相關係數.........................................19\n 2.5模糊線性相關係數的性質....................................27\n3.實例應用.................................................29\n 3.1有序性離散模糊數與區間模糊數之模糊相關係數....................29\n 3.2三十位國中三年級學生影響數學成績表現可能因數之相關係數..........37\n4.結論與建議...............................................43\n5.參考文獻.................................................44zh_TW
dc.language.isoen_US-
dc.source.urihttp://thesis.lib.nccu.edu.tw/record/#G0097972014en_US
dc.subject模糊統計zh_TW
dc.subject區間模糊數zh_TW
dc.subject模糊相關係數zh_TW
dc.subjectFuzzy statisticsen_US
dc.subjectInterval fuzzy numberen_US
dc.subjectFuzzy correlation coefficienten_US
dc.title模糊資料相關係數及在數學教育之應用zh_TW
dc.titleCorrelation of fuzzy data and its applications in mathematical educationen_US
dc.typethesisen
dc.relation.reference[1]江彥聖 (2008)。模糊相關係數及其應用。碩士論文，國立政治大學，台北市。zh_TW
dc.relation.reference[2]吳柏林 (2005)。模糊統計導論：方法與應用。台北：五南。zh_TW
dc.relation.reference[3]Buckley, J. J. (2003). Fuzzy Probabilities: New Approach and Applications, Physics-Verlag, Heidelberg, Germany.zh_TW
dc.relation.reference[4]Buckley, J. J. (2004). Fuzzy Statistics, Springer-Verlag, Heidelberg, Germany.zh_TW
dc.relation.reference[5]Carlsson, C., Fuller, R. (2001). On possibilistic mean value and variance of fuzzy numbers, Fuzzy Sets and Systems 122, 315-326.zh_TW
dc.relation.reference[6]Dubois, D. and Prade, H (1987). The mean value of a fuzzy number, Fuzzy Sets and Systems 24, 279-300.zh_TW
dc.relation.reference[7]Galvo, T., and Mesiar, R. (2001). Generalized Medians, Fuzzy Sets and Systems 124, 59-64.zh_TW
dc.relation.reference[8]Gebhardt, J., Gil, M. A., and Kruse, R. (1998). Fuzzy set-theoretic methods in statistics, in: R. Slowinski(Ed.), Handbook on Fuzzy Sets, Fuzzy Sets in Decision Anaysis, Operations Research, and Statistics, vol. 5, Kluwer Academic Publishers, New York, 311-347.zh_TW
dc.relation.reference[9]H. T. Nguyen and B. Wu (2006) Fundamentals of Statistics with Fuzzy Data. New York：Springer.zh_TW
dc.relation.reference[10]Heilpern, S. (1992). The expected value of a fuzzy number, Fuzzy Sets and Systems 47, 81-86.zh_TW
dc.relation.reference[11]Hung, W. L. and Wu, J. W., (2001). A note on the correlation of fuzzy numbers by Expected interval, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 9, 517-523.zh_TW
dc.relation.reference[12]Hung, W. L. and Wu, J. W., (2002). Correlation of fuzzy numbers by α-cut method, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 10, 725-735.zh_TW
dc.relation.reference[13]Korner, R. (1997). On the Variance of Fuzzy Random Variables, Fuzzy Sets and Systems Vol. 92, 83-93.zh_TW
dc.relation.reference[14]Liu, S. T. and Kao, C. (2002). Fuzzy measures for correlation coefficient of fuzzy numbers. Fuzzy Sets and Systems 128, 267-275zh_TW
dc.relation.reference[15]Wu, H. C. (1999). Probability density functions of Fuzzy Random Variables. Fuzzy Sets and Systems Vol. 105, 139-158.zh_TW
dc.relation.reference[16]Zadeh, L.A., (1965). Fuzzy sets. Information and Control 8, 338-353.zh_TW
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