Subject category:
Knowledge, Information and Communication Systems Management
Published by:
Harvard Business Publishing
Version: 20 July 2022
Revision date: 15-Mar-2024
Share a link:
https://casecent.re/p/190822
Write a review
|
No reviews for this item
This product has not been used yet
Abstract
This note provides an overview of linear regression for an introductory data science course. It begins with a discussion of correlation, and explains why correlation does not necessarily imply causation. The note then describes the method of least squares , and how to interpret the r-squared and model coefficient values of a simple linear regression model. Next, the note describes how the interpretation of a model coefficient changes when there are multiple independent variables in the model. Finally, the note explains how to interpret the coefficients on dummy variables in a regression model. The appendix includes R code for implementing all of these topics.
About
Abstract
This note provides an overview of linear regression for an introductory data science course. It begins with a discussion of correlation, and explains why correlation does not necessarily imply causation. The note then describes the method of least squares , and how to interpret the r-squared and model coefficient values of a simple linear regression model. Next, the note describes how the interpretation of a model coefficient changes when there are multiple independent variables in the model. Finally, the note explains how to interpret the coefficients on dummy variables in a regression model. The appendix includes R code for implementing all of these topics.