Skip to Main Content
HBS Home
  • About
  • Academic Programs
  • Alumni
  • Faculty & Research
  • Baker Library
  • Giving
  • Harvard Business Review
  • Initiatives
  • News
  • Recruit
  • Map / Directions
Faculty & Research
  • Faculty
  • Research
  • Featured Topics
  • Academic Units
  • …→
  • Harvard Business School→
  • Faculty & Research→
Publications
Publications
  • June 2021
  • Technical Note
  • HBS Case Collection

Introduction to Linear Regression

By: Michael Parzen and Paul Hamilton
  • Format:Print
  • | Language:English
  • | Pages:15
ShareBar

Abstract

This technical note introduces (from an applied point of view) the theory and application of simple and multiple linear regression. The motivation for the model is introduced, as well as how to interpret the summary output with regard to prediction and statistical inference. Using salary data from Glassdoor, the note provides a broad overview of correlation, simple linear regression, and multiple regression. Students will learn how to interpret regression coefficients and their corresponding p-values. The note also describes evaluation metrics such as r-squared and residual squared error. Finally, the note introduces students to diagnostic plots and reinforces the important concept that correlation is not causation. Throughout, the note demonstrates how these concepts can be implemented using the R statistical programming language.

Keywords

Linear Regression; Regression; Analysis; Forecasting and Prediction; Risk and Uncertainty; Theory; Compensation and Benefits; Mathematical Methods; Analytics and Data Science

Citation

Parzen, Michael, and Paul Hamilton. "Introduction to Linear Regression." Harvard Business School Technical Note 621-086, June 2021.
  • Educators

About The Author

Michael I. Parzen

Technology and Operations Management
→More Publications

More from the Authors

    • 2022
    • Faculty Research

    Nailing Prediction: Experimental Evidence on the Value of Tools in Predictive Model Development

    By: Daniel Yue, Paul Hamilton and Iavor Bojinov
    • October 2022
    • Faculty Research

    On Ramp to Crypto

    By: Iavor Bojinov, Michael Parzen and Paul Hamilton
    • June 2022 (Revised July 2022)
    • Faculty Research

    Causal Inference

    By: Iavor Bojinov, Michael Parzen and Paul Hamilton
More from the Authors
  • Nailing Prediction: Experimental Evidence on the Value of Tools in Predictive Model Development By: Daniel Yue, Paul Hamilton and Iavor Bojinov
  • On Ramp to Crypto By: Iavor Bojinov, Michael Parzen and Paul Hamilton
  • Causal Inference By: Iavor Bojinov, Michael Parzen and Paul Hamilton
ǁ
Campus Map
Harvard Business School
Soldiers Field
Boston, MA 02163
→Map & Directions
→More Contact Information
  • Make a Gift
  • Site Map
  • Jobs
  • Harvard University
  • Trademarks
  • Policies
  • Accessibility
  • Digital Accessibility
Copyright © President & Fellows of Harvard College