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    政大機構典藏 > 理學院 > 應用數學系 > 學位論文 >  Item 140.119/49452
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/49452


    Title: 模糊卡方適合度檢定
    Fuzzy Chi-square Test Statistic for goodness-of-fit
    Authors: 林佩君
    Lin,Pei Chun
    Contributors: 吳柏林
    wu,Berlin
    林佩君
    Lin,Pei Chun
    Keywords: 模糊思維
    模糊邏輯
    模糊集合理論
    隸屬度函數
    樣本調查
    卡方適合度檢定
    fuzzy thinking
    fuzzy logic
    fuzzy set theory
    membership functions
    sampling survey
    chi-square test statistic for goodness-of-fit
    Date: 2006
    Issue Date: 2010-12-08 11:45:09 (UTC+8)
    Abstract: 在資料分析上,調查者通常需要決定,不同的樣本是否可被視為來自相同的母體。一般最常使用的統計量為Pearson’s 統計量。然而,傳統的統計方法皆是利用二元邏輯觀念來呈現。如果我們想要用模糊邏輯的概念來做樣本調查,此時,使用傳統 檢定來分析這些模糊樣本資料是否仍然適當?透過這樣的觀念,我們使用傳統統計方法,找出一個能處理這些模糊樣本資料的公式,稱之為模糊 。結果顯示,此公式可用來檢定,模糊樣本資料在不同母體下機率的一致性。
    In the analysis of research data, the investigator often needs to decide whether several independent samples may be regarded as having come from the same population. The most commonly used statistic is Pearson’s statistic. However, traditional statistics reflect the result from a two-valued logic concept. If we want to survey sampling with fuzzy logic concept, is it still appropriate to use the traditional -test for analysing those fuzzy sample data? Through this concept, we try to use a traditional statistic method to find out a formula, called fuzzy , that enables us to deal with those fuzzy sample data. The result shows that we can use the formula to test hypotheses about probabilities of various outcomes in fuzzy sample data.
    Reference: [1] Arnold, Steven F. (1990). Mathematical statistics. Prentice-Hall, Englewood Cliffs, NJ.
    [2] Hilton, James G. (1971). Probability and statistical analysis. Intext Educational Publishers, London.
    [3] Hogg, Robert V. and Elliot A. Tanis, (1977). Probability and Statistical inference. Prentice-Hall, Upper Saddle River, NJ.
    [4] H. Kwakernaak, Fuzzy random variables - I. Definitions and Theorems, Information Sciences, 15, (1978), 1-29, Fuzzy random variables – II. Algorithms and Examples for the Discrete Case, Information Sciences, 17, (1979), 253-278.
    [5] Johnson, Richard A. and Gourik.Bhattacharyya, (1992). Statistics: Principles and Methods. (2nd ed.). Wiley, New York.
    [6] Liu Yubin, Qiao Zhong and Wang Guangyuan, Fuzzy random reliability of structures based on fuzzy random variables, Fuzzy Sets and Systems, 86, (1997), 345-355.
    [7] M.L. Puri and D. Ralescu, Fuzzy random variables, Journal of Mathematical Analysis and Applications,114, (1986), 409-422.
    [8] Nguyen, H and Wu, B. (2006). Fundamentals of Statistics with Fuzzy Data. Springer, Netherlands.
    [9] Pearson, K., (1900). “On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling.” Philosophy Magazine Series 5, 50, 157-172.
    [10] Wu, B. and Chang, S. K. (2007), “On testing hypothesis of fuzzy mean”, Japan Journal of Industrial and Applied Mathematics. (will appear)
    [11] Zadeh, L.A. (1965). Fuzzy Sets. Information and Control, 8,338-353.
    [12] Zimmermann, H. J. (1996). Fuzzy set theorem and its applications. Kluwer Academic, Boston.
    Description: 碩士
    國立政治大學
    應用數學研究所
    94751015
    95
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0094751015
    Data Type: thesis
    Appears in Collections:[應用數學系] 學位論文

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