| Course Name |
(中) 進階量化與資料分析(7259) |
| (Eng.) Advanced Quantitative and Data Analysis |
| Offering Dept |
Graduate Institute of Bio-Industry Management |
| Course Type |
Elective |
Credits |
3 |
Teacher |
YANG SHANG-HO |
| Department |
Graduate Institute of Bio-Industry Management/Graduate |
Language |
English |
Semester |
2025-FALL |
| Course Description |
This course focuses on social study with data management and advanced quantitative analysis, including dichotomous & multiple choice regression model, sample selection bias adjustment model, categorical choice regression model, conjoint analysis, survival data analysis, panel data analysis, time series, etc.
Statistical Software used in this course: Stata
本課程著重於結合社會研究與資料管理，並進行進階量化分析，內容涵蓋：二元及多元選擇迴歸模型、樣本選擇偏誤調整模型、類別選擇迴歸模型、聯合分析、生存資料分析、Panel Data分析、時間序列分析等。
本課程使用的統計軟體為 Stata |
| Prerequisites |
|
self-directed learning in the course |
Y |
| Relevance of Course Objectives and Core Learning Outcomes(%) |
Teaching and Assessment Methods for Course Objectives |
| Course Objectives |
Competency Indicators |
Ratio(%) |
Teaching Methods |
Assessment Methods |
1. Cultivate students’ skills in data processing, analysis, and interpretation of statistical variable results.
2. Develop students’ ability to independently utilize statistical software analysis tools, as well as to engage in self-directed learning and application of these skills.
1.培養學生具備資料處理與分析及解釋統計變數結果之技能
2.培養學生能自主使用統計軟體分析工具，並有辦法自主學習與應用技能 |
|
|
| Exercises |
| Practicum |
| Lecturing |
|
| Written Presentation |
| Attendance |
| Assignment |
|
| Course Content and Homework/Schedule/Tests Schedule |
| Week |
Course Content |
| Week 1 |
Course Introduction & The Probability and Distribution / Data type / How to define variables
|
| Week 2 |
Data Management and Practice & Visualizing Data in Graphics |
| Week 3 |
OLS Introduction |
| Week 4 |
Dichotomous Choice Regression Model: binary outcome model in logit and probit |
| Week 5 |
Multiple Choice Regression Model: multinomial & ordered model |
| Week 6 |
Multiple Choice Regression Model: limited dependent variable model |
| Week 7 |
Count Data Model: poisson and negative binomial model |
| Week 8 |
Seminar for mid-term Oral Presentation |
| Week 9 |
Survival Data Analysis |
| Week 10 |
Treatment Evaluation: matching methods |
| Week 11 |
Treatment Evaluation: difference-in-differences (DID) |
| Week 12 |
Fixed Effect Regression and Panel Data Analysis (I) |
| Week 13 |
Fixed Effect Regression and Panel Data Analysis (II) |
| Week 14 |
Time Series Model |
| Week 15 |
Conjoint Analysis |
| Week 16 |
Final Report |
self-directed
learning |
01.Participation in professional forums, lectures, and corporate sharing sessions related to industry-government-academia-research exchange activities.
02.Viewing multimedia materials related to industry and academia.
03.Preparing presentations or reports related to industry and academia.
|
|
| Evaluation |
| Attendance (20%), Assignments (20%), Written Report I (30%), Written Report II (30%) |
| Textbook & other References |
1. Alexander C. Lembcke. (2009), Advanced Stata Topics. London School of Economics.
2. Alexander C. Lembcke. (2010), Introduction to Stata. London School of Economics. |
| Teaching Aids & Teacher's Website |
| Stata User's Guide: https://www.stata.com/manuals/u.pdf |
| Office Hours |
| Appointment needed. |
| Sustainable Development Goals, SDGs(Link URL) |
| 01.No Poverty 02.Zero Hunger 03.Good Health and Well-Being 04.Quality Education 05.Gender Equality 08.Decent Work and Economic Growth | include experience courses：N |
|