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Under construction
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| Semester | Spring 2026 |
|---|---|
| Time & Days | TuTh 10:30AM - 11:45AM |
| Location | TBD |
| Instructor | Kihyuk Hong (E2-2 #3104) |
| Office Hours | TBD |
| [email protected] | |
| TAs | TBD |
| Lecture Note | To be provided |
| Midterm | TBD |
| Final | TBD |
This course reviews general theories of linear regression models with applications to industrial engineering problems. Topics include: Principles of least squares method; multivariate normal distribution and quadratic forms; estimation and hypothesis testing; residual analysis; polynomial regression and ridge regression; regression model building; response surface methodology, etc. Computational aspects of regression analysis are also emphasized.
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Subject to change.
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| No. | Date | Lecture Notes | 📹 | Readings | Exercise | Homework |
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| 01 | Lecture 01: Introduction 1 - Regression model examples. Notations. | |||||
| 02 | Lecture 02: Introduction 2 - Prerequisites. Linear Algebra. Normal Distributions. | |||||
| 03 | Lecture 03: Linear Model 1 - Least squares. Gauss-Markov theorem. | |||||
| 04 | Lecture 04: Linear Model 2 - | |||||
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