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認識二元變數 (binary response) 定義以及適用場景。
了解 Prospective 與 Retrospective 研究設計的差異與應用。
廣義線性模型 (Generalized Linear Model, GLM) 的架構與邏輯斯迴歸應用。
似然函數與最大概似估計 (Maximum Likelihood Estimate, MLE) 原理。
二項資料的 Logistic Regression 模型與應用案例。
對數線性迴歸 (Log-Linear Regression) 之應用。
過度離散 (Overdispersion) 的檢測與修正模型建構。
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3 assignments
本單元介紹：1. 二元變數 (binary response) 的概念與應用情境、2. 二元資料分析中比較兩組族群的母體比例、機率與勝算、3. 前瞻性 (Prospective) 與回顧性 (Retrospective) 研究設計的差異與應用、4. 列聯表 (Contingency Table) 的建立與解讀、5. 獨立性 (Independence) 與同質性 (Homogeneity) 檢定的基本概念、6. 抽樣方式與適用的檢定方法、以及 7. 卡方獨立性檢定 (Chi-Squared Test for Independence) 的步驟與應用
11 videos1 reading
本單元介紹：1. 廣義線性模型 (GLM) 的基本概念、組成與特性、2. 二元資料的邏輯斯迴歸 (GLM：Logistic Regression) 模型、3. 似然函數與最大概似估計 (MLE) 的原理、以及 4. 迴歸的模型評估與初步概念
20 videos1 reading1 assignment
本單元介紹：1. 二項資料的 Logistic Regression 模型與應用案例、2. 模型評估方法與推論程序、3. 實例分析：Survey Mac 案例、4. 過度離散 (Overdispersion) 的概念與檢測、以及 5. 含過度離散的 Logistic Regression 模型建構
14 videos1 reading
本單元介紹：1. 對數線性迴歸 (Log-Linear Regression) 在 Poisson 反應資料中的應用、2. 模型評估方法與診斷、3. 對數線性迴歸係數的推論與解釋、4. 過度離散 (Overdispersion) 的概念與檢測、以及 5. 含額外變異的 Log-Linear Regression 模型建構
6 videos1 reading1 assignment
1 assignment
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