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Fuzzy Chi-square Test Statistic for goodness-of-fit
fuzzy set theory
chi-square test statistic for goodness-of-fit
|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.
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|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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