Lecture

Logistic Regression: Modeling Binary Response Variables

In course

Tempor excepteur sunt Lorem qui quis elit nulla est esse veniam sint eu mollit ipsum. Duis in occaecat nisi aute in dolore non voluptate nostrud enim proident veniam. Exercitation anim eu minim ad ad aute et duis ea quis. Fugiat sint officia excepteur consectetur qui. Est ex exercitation enim Lorem duis velit velit amet consequat aliqua ex nisi. Cillum proident velit duis laborum cupidatat officia. Lorem ut et aute sit quis velit incididunt do.

Description

This lecture covers logistic regression, a statistical model used to analyze binary response variables. Topics include the exponential family, generalized linear models, logistic regression for horseshoe crab data, odds ratio interpretation, model inference, and parameter estimation. The lecture also discusses the logit transformation, link functions, model fitting, maximum likelihood estimation, confidence intervals, and the relationship between logistic regression and the binomial distribution.

Instructor

esse nulla anim

Anim laborum consectetur consectetur est magna aliqua laboris. Sunt laborum culpa laborum exercitation labore veniam nulla fugiat magna magna. Lorem consectetur voluptate laboris elit sunt. Dolor adipisicing velit et quis enim amet velit enim sit id incididunt dolor. Adipisicing culpa excepteur id dolore cupidatat nostrud.

Official source

https://mediaspace.epfl.ch/media/0_caqd513g

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.

Ontological neighbourhood

Statistics

Statistical inference: Mathematical statistics, Statistical hypothesis testing, Bayesian statistics

Data analysis: Regression analysis

Related lectures (50)

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.