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  • March 2022 (Revised July 2022)
  • Module Note

Linear Regression

By: Iavor I. Bojinov, Michael Parzen and Paul Hamilton
  • Format:Print
  • | Language:English
  • | Pages:18
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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.

Keywords

Data Science; Linear Regression; Mathematical Modeling; Mathematical Methods; Analytics and Data Science

Citation

Bojinov, Iavor I., Michael Parzen, and Paul Hamilton. "Linear Regression." Harvard Business School Module Note 622-100, March 2022. (Revised July 2022.)

About The Authors

Iavor I. Bojinov

Technology and Operations Management
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Michael I. Parzen

Technology and Operations Management
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Related Work

    • March 2022 (Revised July 2022)
    • Faculty Research

    Linear Regression

    By: Iavor I. Bojinov, Michael Parzen and Paul Hamilton
Related Work
  • Linear Regression By: Iavor I. Bojinov, Michael Parzen and Paul Hamilton
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