本課程之目的在於訓練學生正確使用統計方法，應用於業界或其它研究領域之相關資料分析。對於分析結果之闡釋以及報告之撰寫，亦為課程訓練之重點。課程所涉及之統計方法包括實驗設計、多變量分析、類別資料分析、存活分析、迴歸分析等。1. The topics include statistical methodology for regression analysis, the
analysis of experimental data, categorical data, multivariate data, survival data.
2. SAS coding for data analysis.The purpose of this course is to train students to use statistical methods correctly and apply them to relevant data analysis in the industry or other research fields. The highlights of the analysis results and the writing of reports are also the focus of course training. The statistical methods involved in the course include experiment design, multi-variable analysis, category data analysis, survival analysis, reproductive analysis, etc. 1. The topics include statistical methodology for regression analysis, the
analysis of experimental data, category data, multivariate data, survive data.
2. SAS coding for data analysis.
為求學生明瞭現代統計學在科學研究中所扮演之重要角色，進而藉由統計學方法及實際論文之研讀與習作，培養學生獨力與團隊合作進行科學研究之能力及經驗。課程主要工作包括：
(1)科學研究工作之理念及研究方法講授。
(2)分組完成題目自訂之完整研究報告。包括組織、協調、排程、執行及撰寫報告等工作。
(3)培養訓練蒐集資料能力。包括詢問、上網、查閱書籍與期刊等。
(4)培養訓練當眾口頭報告能力。包括儀態、台風等。
In order to help students understand the important role played by modern statistical studies, they can cultivate students' ability and experience in scientific research through the study and practice of statistical methods and practical essays. The main course work includes:
(1) Teaching of concepts and research methods of scientific research work.
(2) Complete research report for the sub-assembly. Including organization, coordination, scheduling, execution and writing reports.
(3) Cultivate training and training to collect data. Including inquiries, online access, searching books and journals, etc.
(4) Cultivate the ability of trainees to report publicly. Including state, Taiwan style, etc.
1、沈葆聖(2002)，SAS統計軟體與資料分析
2. Applied linear regression models (by Kutner, Nachtsheim and Neter).
3. Design of Experiments: Statistical Principles of Research Design
and Analysis (by Robert Kuehl)
4. Applied Multivariate Statistical Analysis (by Richard Johnson and Dean Wichern)
5. Categorical Data Analysis (by Alan Agresti)
6. Applied Survival Analysis (by Chap T. Le)
1. Shen Baosheng (2002), SAS statistical software and data analysis
2. Applied linear regression models (by Kutner, Nachtsheim and Neter).
3. Design of Experiments: Statistical Principles of Research Design
and Analysis (by Robert Kuehl)
4. Applied Multivariate Statistical Analysis (by Richard Johnson and Dean Wichern)
5. Categorical Data Analysis (by Alan Agresti)
6. Applied Survival Analysis (by Chap T. Le)
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| midtermmidterm midterm |
50 | |
| finalfinal Final |
50 |
