Data Science Linear Regression

1 Learning Objectives

• How linear regression was originally developed by Galton
• What confounding is and how to detect it
• How to examine the relationships between variables by implementing linear regression in R

1.1 Course Overview

There are three major sections in this course: introduction to linear regression, linear models, and confounding.

1.1.1 Introduction to Linear Regression

In this section, you’ll learn the basics of linear regression through this course’s motivating example, the data-driven approach used to construct baseball teams. You’ll also learn about correlation, the correlation coefficient, stratification, and the variance explained.

1.1.2 Linear Models

In this section, you’ll learn about linear models. You’ll learn about least squares estimates, multivariate regression, and several useful features of R, such as tibbles, lm, do, and broom. You’ll learn how to apply regression to baseball to build a better offensive metric.

1.1.3 Confounding

In the final section of the course, you’ll learn about confounding and several reasons that correlation is not the same as causation, such as spurious correlation, outliers, reversing cause and effect, and confounders. You’ll also learn about Simpson’s Paradox.