<aside> 🚧

Under construction

</aside>

Logistics

Course Description

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.

Prerequisites

• background in linear algebra (must)
• background in probability and statistics (must)
• background in convex optimization (good to have)
• background in real analysis (good to have)
• some hands on experience working with data using Python (good to have)

<aside> ⚠️

This course is proof-based and requires a strong level of mathematical maturity. Students without sufficient background may find it challenging to follow the material. In particular, prior knowledge of linear algebra and probability/statistics is essential. If you choose to take the course without this background, you will be expected to independently study the necessary material in order to keep up with the lectures.

</aside>

Grading

Resources

There are no required textbooks for this course. Some useful resources:

• The Elements of Statistical Learning (public pdf)

Policies

• All materials will be presented in English.
• You may ask questions during class in Korean, but I will repeat the question in English then respond in English.