3 Ways Linear Models Can Lead to Erroneous Conclusions
Last Updated on July 20, 2023 by Editorial Team
Author(s): ___
Originally published on Towards AI.
In this article, I will share 3 ways in which linear models can lead to erroneous conclusions. The focus will be on fitting linear models to simulated data and checking whether the resultant estimates are consistent with the simulation.
This article is based on the content of [1].
The code to reproduce the results described in this article can be found in this notebook.
Let Y be the thing we would like to model. Suppose we know that Y is defined by a vector of variables X as Y = β X, where β is a vector of parameters, one for each variable… Read the full blog for free on Medium.
Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.
Published via Towards AI