Multiple regression analysis - effect modifiers and interactions
This is a video about linear regression and how when undertaking multiple regression analysis, we need to take into account the possibility of variables that interact and effect modifiers. Interaction and effect modifiers are important to understand when you are dealing with multiple variables in a complex dataset. This video walks you through the R programming skills you need to generate meaningful models.

▶︎
Multiple Regression from beginning to end in 30 minutes.

▶︎
Learn Statistical Regression in 40 mins! My best video ever. Legit.

▶︎
13.8 Multiple Linear Regression: Interaction Terms

▶︎
Adding variables to your multiple regression model

▶︎
Poisson regression in R

▶︎
Working with factors and categorical variables. Use forcats in R programming to change factor levels

▶︎
Gaussian Processes

▶︎
Principal Component Analysis (PCA)

▶︎
Multiple Linear Regression: An Easy and Clear Beginner’s Guide

▶︎
Multiple regression - making sure that your assumptions are met

▶︎
Explore your data using R programming

▶︎
Regularization Part 1: Ridge (L2) Regression

▶︎
Turing Award Winner: Disagreeing with Google, Postgres, Future Problems | Mike Stonebraker

▶︎
Multicollinearity

▶︎
ML Tutorial: Gaussian Processes (Richard Turner)

▶︎
Moderated Multiple Linear Regression

▶︎
What is the K-Nearest Neighbor (KNN) Algorithm?

▶︎
Billionaire's WARNING: I'm SELLING. The Crash Is Already Here!

▶︎
Principal Component Analysis (The Math) : Data Science Concepts

▶︎
