依序介紹簡單線性迴歸模型、多元線性迴歸模型及迴歸決測樹模型之理論及應用，藉由推論、診斷和模式選擇，加強資料分析之建模與預測能力。理論部份提供學生在學習大一統計學、微積分和線性代數之後，如何進一步與專業的統計課程結合，並應用於實際的問題解決之中;同時在龐大的資料流中，透過實際問題的思索和探究，藉由基礎的摘要統計和圖表進行初步探勘，有效率的進行有用資料之探勘和採礦，並將數據轉換為有用的資訊，探討日常生活中無所不在的統計問題，以及如何正確使用迴歸分析建立量化之預測模型。透過專業之統計採礦軟體進行實務操作，建立模型配適之正確觀念統計，除了專業知識與技術學習之外，並在課程學習過程中逐漸培養國際觀、團隊合作、創新思考和口語與書寫溝通能力等職涯競爭力，將統計最重要的量化模型理論融合於實務操作，進行決策判斷之參考依據，培養學生職涯發展所需之跨領域專長。The theory and application of simple linear replication models, multivariate linear replication models and replication tree models are introduced in sequence, and the modeling and prediction capabilities of data analysis are enhanced through recommendation, diagnosis and model selection. The theories section provides students with how to further combine it with professional statistic courses after learning the first year of freshman schema, microscore and linear generation, and apply it to actual problem solving; at the same time, through the thinking and exploration of actual problems in large data streams, through the thinking and exploration of actual problems, The summary statistics and graphs are initially explored, efficiently explored and mined useful data, and converted data into useful information, explored the unoccupied statistical problems in daily life, and how to correctly use replication analysis to establish quantitative prediction models. Through professional statistics, we conduct practical operations and establish correct conceptual statistics that match models. In addition to professional knowledge and technology learning, we will gradually cultivate international tourism, team cooperation and creation in the course learning process. New thinking and career competition such as oral and writing skills integrate the most important quantitative model theories into practical operations, conduct references for decision-making and judgment, and cultivate cross-regional experts required for students' career development.
本課程主要目標在介紹迴歸分析之相關方法以及其理論。除此之外，如何利用所學迴歸方法來做實際資料分析亦是本課程之重點，課程主要涵蓋如下：
1.簡單以及多重迴歸之方法及理論
2.迴歸模式適合度檢定以及診斷
3.反應變數之轉換
4.迴歸與變異數分析
5.模式選取
6.利用迴歸相關方法之實例分析
The main purpose of this course is to introduce the relevant methods and theories of reproductive analysis. In addition, how to use the learned method to do practical data analysis is also the focus of this course. The course mainly covers the following:
1. Simple and multiple recitation methods and theories
2. Verification mode suitability confirmation and diagnosis
3. The transformation of reaction variables
4. Analysis of regression and variations
5. Mode selection
6. Example analysis of using rehabilitation-related methods
1. Kutner, M.H., Nachtsheim, C., and Neter, J. （2004）, Applied Linear Regression Models, 4th edition, McGraw-Hill Book Co.（華泰文化事業股份有限公司代理）(Textbook)
2. Mendenhall, W. and Sincich, T. （2012）, A second Course in Statistics--Regression Analysis, 7th edition, Pearson Education, Inc, Boston, USA. （華泰文化事業股份有限公司代理）
3. Montgomery D.C., Peck, E. A. and Vining, G. G. (2006）, Introduction to Linear Regression Analysis, 4th edition, John Wiley & Sons, Inc. （歐亞書局有限公司代理）
1. Kutner, M.H., Nachtsheim, C., and Neter, J. (2004), Applied Linear Regression Models, 4th edition, McGraw-Hill Book Co. (Agent of Huatai Cultural Affairs Co., Ltd.) (Textbook)
2. Mendenhall, W. and Sincich, T. (2012), A second Course in Statistics--Regression Analysis, 7th edition, Pearson Education, Inc, Boston, USA. (Agent from Huatai Cultural Affairs Co., Ltd.)
3. Montgomery D.C., Peck, E. A. and Vining, G. G. (2006), Introduction to Linear Regression Analysis, 4th edition, John Wiley & Sons, Inc. (Agent from European Books Agency, Ltd.)
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| 平時成績平時成績 Regular achievements |
40 | 學習態度(包括出缺席)、作業成績、平時考成績、課堂討論與互動 |
| 期中考期中考 Midterm exam |
30 | 筆試進行 |
| 期末考期末考 Final exam |
30 | 筆試進行 |
