在知識經濟之社會，多元思維逐漸取代傳統二元邏輯的思考與分析方法。過去使用單一數值的樣本來計算中位數的方法，已漸不符現今複雜多變的智慧科技時代之需求。尤其是在具有多變性、不確定性、與訊息不完整性的財金與經濟環境下，過分強調對於數值之運算及數學假設的前提，反而更容易造成與現實環境及條件的背離、甚至是脫節。故在進行財金與經濟方面問題的研究時，利用隸屬度函數與模糊統計的分析將會是一種較為進步的測度方法。本文在此提出模糊中位數的分析理論，並將其應用於財務金融的分析測度上，期望能對複雜的財金經濟現狀提供一套更有效且精確合理的分析方法。 / In the society of economic knowledge, Multi-valued logic goes to replace binary logic gradually. In the traditional way, we usually ask the task-taker to response the answer according to the thinking of binary logic. But such kind of response is improper since the human thinking is fuzzy and uncertain. So it should be an improved measurement using membership functions and fuzzy statistics. 　　In this paper, we will propose the definition of fuzzy median, and present some of its application. According to the above theoretical contents, we give some examples, which is used frequently in financial and economic assessment. From the explanation and discussion of fuzzy median in these examples, we can recognize that fuzzy statistics is more meaningful and proper for research of finance and economics. At last, based upon the findings of this study, certain recommendations for further research are suggested.

從西元2010至2014年以來，台灣車險市場的賠款率逐年攀升，而汽車保險業務占產險公司營收來源約五成，顯見汽車保險業務對於產險公司的重要性，然而近來高單價超跑肇事頻繁，屢登上新聞版面，單一案件的賠償金額遠超過客戶所繳保費收入，對公司經營績效產生莫大壓力。 本研究透過二元邏輯迴歸來探討可能影響車體險出險理賠的因素，依據發現的影響因子建立客戶評估模式，提高公司經營績效，藉由個案公司所提供西元2012至2014年共1,071,935筆的資料，以SPSS Statistics作為分析工具，得出年齡、性別、國產或進口車種類、車輛出廠年份以及車輛用途分類中的重型機車、自用小客車、客貨兩用車、長租小客車和個人計程車與出險理賠存在相關性。 分析後發現五大結論。第一，年齡增長與出險理賠呈現負相關；第二，女性出險的機率高於男性；第三，進口車出險理賠的機率遠高於國產車；第四，車齡愈小發生出險理賠的機率愈高；最後則是各項車種用途的車輛發生出險理賠機率皆偏高，尤以長租小客車和個人計程車為最。

逆物流回收的複雜度遠比正向物流高，企業為專注核心價值多半將逆物流活動委外專業物流服務供應商。對第三方逆物流業者而言，選擇適當的回收處理中心位址為其重要核心能力之一，而現今研究對於選址模式中之回收不確定性，大多以歷史資料作為參數，無根據區域特性不同而有所分別。故本研究希望探討在零售商提供資訊的情境下，結合消費者問卷建構廢棄產品的使用年限機率、並以二元迴歸邏輯分析建構回收機率以此預測區域回收數量，透過資訊分享以建立更好的回收處理中心選址設置模式，使第三方逆物流業者可按照此模式選擇最適當的回收點位置與回收處理量安排用以求得利潤最大化。 / Since reverse logistics is much more complex than forward logistics, third-party logistics providers are often the prior choice for firms to obtain their core value when a reverse logistic activity is needed. For third-party logistics providers, the location is one of their crucial core values; while most of them can only rely on historical data to assume the best location, due to the uncertainty of recycling in present studies.Therefore, this paper tries to construct the probability of products’ used-years by combining the retailers’ information with consumer-oriented questionnaires. Binary logistic regression is the methodology used to analyze and predict recycling probability. By information-sharing the third-party logistics providers will be able to construct a better selecting model for the best facility location, which will reach the most suitable recycling quantity to maximize their profits.