Introduction to Lecture51 Data2decision Addressing Multicollinearity
Let's dive into the details surrounding Lecture51 Data2decision Addressing Multicollinearity. Methods for
Lecture51 Data2decision Addressing Multicollinearity Comprehensive Overview
Multicollinearity Using R to detect mutlicollinearity (eigenvalues, variance inflation factors), and using ridge regression to deal with Intro to multiple regression, interactions,
Multicollinearity
Summary & Highlights for Lecture51 Data2decision Addressing Multicollinearity
- Correlation matrix, variance inflation factor, and eigensystem analysis to detect
- Indicator variables; non-linear regression. Course Website: http://www.lithoguru.com/scientist/statistics/course.html.
- Notebook(s) can be found on https://github.com/MrGeislinger/flatiron-school-data-science-curriculum-resources.
- Using the correlation matrix to detect
- Multicollinearity
That wraps up our extensive overview of Lecture51 Data2decision Addressing Multicollinearity.
