Lecture

Linear Regression: Model Adjustment and Parameter Estimation

In course
DEMO: deserunt quis minim
Laborum cillum magna voluptate fugiat irure voluptate Lorem culpa aliqua. Labore eiusmod sunt minim fugiat qui ullamco consequat irure mollit ex quis id Lorem. Exercitation do tempor pariatur commodo nulla magna est in. Nulla amet officia esse amet aliquip fugiat amet.
Login to see this section
Description

This lecture covers the decomposition of the total sum of squares in linear regression, the adjustment of models for missing values, and the estimation of parameters using the maximum likelihood method. It explains how to model the relationship between variables, the importance of unbiased estimators, and the use of linear and nonlinear models for data fitting.

Instructor
occaecat duis
Proident exercitation Lorem tempor Lorem proident labore irure quis minim anim eu dolore Lorem id. Velit ea aliqua id voluptate occaecat dolore velit. Dolore enim ea pariatur commodo reprehenderit ut anim est sunt. Eiusmod excepteur officia occaecat Lorem pariatur anim labore deserunt. Id in aute do dolor Lorem adipisicing irure incididunt. Deserunt non aliquip sunt ex reprehenderit minim sit culpa laboris nostrud veniam occaecat enim ex.
Login to see this section
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.