臺灣博碩士論文加值系統

隨機效果模型中的組內相關係數ICC (intraclass correlation coefficients)在實際應用上無所不在，ICC(2)的最適樣本數如何決定，為本文章主要貢獻。一般較常見的指數有皮爾森相關係數(Pearson correlation coefficient)，雖然藉此可獲知變數之間的相關性，但皮爾森相關係數需滿足的前提假設較為困難，必須假設兩變數為相互獨立，所以在實際應用上較為困難，實際研究中資料多為有相似性且不獨立數據，於是使用組內相關係數來測量其變數之間的關係較為恰當。本文將專注研究於單因子隨機效果，使用統計軟體SAS/IML程式進行模擬，透過假設檢定法在檢定力及顯著水準α固定下，運用F分配作檢定找到最適樣本數。

In the practical applications, Intraclass correlation coefficients is the model in random effects model. The main contribution in this research is how to determine the optimum number of samples based on ICC(2). For the begging we learn to use the tools of measurement is the Pearson correlation coefficient. Although, we can know the correlation between variables, but satisfy the assumption is difficult in the reality. The reason is that the two variables of Pearson correlation coefficient t must be assumed independent. Therefore, in practical application is not easy. Most of the research data have similarities and dependent. Due to the reason above, Using the correlation coefficient ICC(2) to measure the relationship between variables is more appropriate. This article will focus on research on One-way random effects model, using statistical software SAS / IML program do the simulation. Through the hypothesis testing method to set the power, and employ the F ratio to look for the optimistic sample size.

中文摘要 i
Abstract ii
目錄 iv
圖表目錄 v
第一章研究動機與文獻探討 1
1.1研究動機與目的 1
1.2文獻探討 2
第二章模型與方法 4
2.1隨機校用模型假設 4
2.2 F檢定 5
2.2.1右尾檢定 5
2.2.2左尾檢定 6
2.2.3雙尾檢定 6
2.3樣本數計算 6
2.3.1第一部分 7
2.3.2第二部份 8
第三章數據分析 10
3.1第一部分分析 10
3.1.1右尾檢定 10
3.1.2左尾檢定 14
3.2第二部分分析 18
3.2.1右尾檢定 18
3.2.2左尾檢定 27
第四章結論 36
參考文獻 37

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