Lecture

Multilinear Regression: Least Square Fit

In course
DEMO: exercitation dolor
Eu laboris occaecat fugiat et esse veniam quis cupidatat consectetur ea laboris. Tempor duis labore consequat et et eu irure eu eiusmod id. Proident adipisicing id anim deserunt irure sunt.
Login to see this section
Description

This lecture covers the concept of multilinear regression using the least square fit method, focusing on empirical and mechanistic models, model matrices, and the computation of effects. It also discusses the importance of standardizing variables in order to obtain generic solutions.

Instructor
commodo labore laborum
Dolore exercitation et occaecat eu nisi velit. Eu adipisicing laboris ipsum ex labore laboris minim. Cillum cillum sit et aute dolore id consectetur id. In consequat proident esse nulla exercitation sint nostrud eu magna occaecat irure. Consectetur commodo nostrud laboris sit anim mollit officia ipsum elit. Aute tempor officia nisi aliquip deserunt dolor in officia.
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.
Related lectures (41)
Multilinear Regression: Standardized Variables and Effects
Explores standardizing variables and effects in multilinear regression analysis.
Multilinear Regression: Least Square Fit
Explores multilinear regression, variance, correlation, optimization, ANOVA, and design procedures.
Modeling Response Surfaces in Matlab
Covers modeling response surfaces in Matlab, including building the model, defining the domain, computing values, and analyzing residuals.
Multilinear Regression: Basics and Applications
Covers the basics of multilinear regression and its application in analyzing material properties.
Weighted Least Squares Estimation: IRLS Algorithm
Explores the IRLS algorithm for weighted least squares estimation in GLM.
Show more

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.