Type of Credit: Elective
Credit(s)
Number of Students
Regression analysis is an important subject for statistical analysis. In this course, we first revisit linear models in Regression Analysis (I) and introduce several theoretical properties. After that, we discuss other regression models, such as exponential family models, and other complex data structures, including nonparametric regression, variable selection or high-dimensional data analysis. This course will accompany with R software for computation implementation.
能力項目說明
After this course, students are expected to have basic knowledge of regression analsysis and are able to handle complex regression models.
| 教學週次Course Week | 彈性補充教學週次Flexible Supplemental Instruction Week | 彈性補充教學類別Flexible Supplemental Instruction Type |
|---|---|---|
| Week | Topic |
| 1 | Revisit of Linear Models |
| 2 | Revisit of Linear Models |
| 3 | Revisit of Linear Models |
| 4 | Introduction of Generalized Linear Models |
| 5 | Introduction of Generalized Linear Models |
| 6 | Introduction of Generalized Linear Models |
| 7 | Introduction of Nonparametric Regression |
| 8 | Introduction of Nonparametric Regression |
| 9 | Midterm |
| 10 | Introduction of Nonparametric Regression |
| 11 | High-Dimensional Data Analysis |
| 12 | High-Dimensional Data Analysis |
| 13 | High-Dimensional Data Analysis |
| 14 | Analysis of Noisy Data |
| 15 | Analysis of Noisy Data |
| 16 | Final |
| 17 | Capstone self-learning |
| 18 | Capstone self-learning |
Note: Students are expected to workload 3 hours in-class as well as outside-of-class.
Midterm 50%
Final 50%
The materials are based on the instructor's lecutre note.