Multiple Linear Regression in R

4 hours of instruction

Multiple linear regression is a supervised learning technique that allows analysts to model the relationship between a certain number of labeled features and a single continuous numerical target variable. In this course, learners will encounter the mathematical underpinnings of regression models in general before building, optimizing, and evaluating a multiple linear regression model in R. Learners will also discuss concepts such as statistical significance to clearly present their findings.

OBJECTIVES

1. Identify opportunities and use cases for regression models
2. Build and evaluate multiple regression models
3. Assess statistical significance and validate models for explanatory power and bias

PREREQUISITES

Moderate familiarity wrangling and analyzing data using simple linear regression in R, is required.

SYLLABUS & TOPICS COVERED

1. Multiple Linear Regression
   • Multiple linear regression in a nutshell
   • Implement multiple linear regression on a dataset

SOFTWARE REQUIREMENTS

You will have access to an R-based Posit Cloud environment for this course. No additional download or installation is required.