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Problems and Testing the Assumptions of Linear Regression: a Machine Learning Perspective

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  • Ayoosh Kathuria
  • Baij Nath Kaushik

Abstract

Linear Regression is perhaps one of most well-known algorithms in statistics and Machine Learning. Despite its widespread use in machine learning applications, the importance of testing the assumptions of linear regression is often trivialised in machine learning literature. However, the predictions of linear regressions cannot be trusted unless its assumptions are met. An attempt has been made to attract the attention of the community towards this understated aspect of putting linear regression into practice. This paper serves as an endeavour to shed some light on ways to test the assumptions of linear regressions and how to remedy the violations if there are any.

Suggested Citation

  • Ayoosh Kathuria & Baij Nath Kaushik, 2017. "Problems and Testing the Assumptions of Linear Regression: a Machine Learning Perspective," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 2(7), pages 16-25, September.
    • Handle: RePEc:jbh:ijsrcs:v2:y2017:i7:id:hcseit174403
    Note: Article URL: https://ijsrcseit.com/CSEIT174403
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