OLS Regression

Simple Linear Regression

StatQuest: Linear Regression in R (5:00)

R Linear Regression Tutorial - Door to master its working! - DataFlairR linear regression tutorial covers single and multiple linear regression, curvilinear regression and working with linear regression in R along with regression assumptions.

Lesson 9 Simple Linear Regression | Basic Analytics in RThis is a brief overview of the R language as it related to my Business Analytics courses at SFU. It is meant to be a complement to doing tasks with other tools, such as Excel and SAS Enterprise Guide

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Simple Linear Regression

Linear Regression Confidence Intervals

Regression Diagnostics

Marin: Fitting a Model and Discussing Assumptions (8:56)

Marin: Addressing Violations of Assumptions in R (11:26)

A Language, not a Letter: Learning Statistics in R

Regression Assumptions

Regression: Basics, Assumptions, & Diagnostics

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Regression Diagnostics

An excellent review of regression diagnostics is provided in John Fox's aptly named Overview of Regression Diagnostics. Dr. Fox's car package provides advanced utilities for regression modeling.

Multiple Linear Regression

Marin: Multiple Linear Regression (13:10)

StatQuest: Multiple Regression in R (7:42)

Statistics of DOOM: R - Regression Lecture (58:45)

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Multiple (Linear) Regression

R provides comprehensive support for multiple linear regression. The topics below are provided in order of increasing complexity.

Lesson 10 Multiple Linear Regression | Basic Analytics in RThis is a brief overview of the R language as it related to my Business Analytics courses at SFU. It is meant to be a complement to doing tasks with other tools, such as Excel and SAS Enterprise Guide

Variable Selection Methods

Marin: Why Including ALL Variables Into A Regression Model Is A Bad Idea (8:04)

R Simple, Multiple Linear and Stepwise Regression [with Examples]

Variable Selection Methods (OLSRR)

Using the OLSRR package to do forward, backward, and stepwise regression

Model Selection Approaches (Prabhakaran)

It is possible to build multiple models from a given set of X variables. But building a good quality model can make all the difference. Here, we explore various approaches to build and evaluate regression models.