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MIT OpenCourseWare: Free Courses from the institute · October 17 · 1 hr 24 min

Lecture 8: Regression Analysis (cont.)

MIT 18.642 Topics in Mathematics with Applications in Finance, Fall 2024 Instructor: Peter Kempthorne View the complete course: https://ocw.mit.edu/courses/18-642-topics-in-mathematics-with-applications-in-finance-fall-2024 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP601Q2jo-J_3raNCMMs6Jves Uploaded 03-12-25 This lecture provides a comprehensive overview of linear regression modeling, focusing on ordinary least squares (OLS) estimation, its mathematical formulation, and statistical properties. It discusses the derivation of the least squares estimator, the interpretation of the Hat matrix as a projection, the distributional assumptions under the normal linear model, inference using t- and F-tests, and model diagnostics including residual analysis and influence measures, concluding with extensions to generalized least squares for correlated errors. License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu Support OCW at http://ow.ly/a1If50zVRlQ We encourage constructive comments and discussion on OCW’s YouTube and other social media channels. Personal attacks, hate speech, trolling, and inappropriate comments are not allowed and may be removed. More details at https://ocw.mit.edu/comments. Learn more about your ad choices. Visit megaphone.fm/adchoices Learn more about your ad choices. Visit megaphone.fm/adchoices

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MIT 18.642 Topics in Mathematics with Applications in Finance, Fall 2024
Instructor: Peter Kempthorne
View the complete course: https://ocw.mit.edu/courses/18-642-topics-in-mathematics-with-applications-in-finance-fall-2024
YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP601Q2jo-J_3raNCMMs6Jves

Uploaded 03-12-25

This lecture provides a comprehensive overview of linear regression modeling, focusing on ordinary least squares (OLS) estimation, its mathematical formulation, and statistical properties. It discusses the derivation of the least squares estimator, the interpretation of the Hat matrix as a projection, the distributional assumptions under the normal linear model, inference using t- and F-tests, and model diagnostics including residual analysis and influence measures, concluding with extensions to generalized least squares for correlated errors.

License: Creative Commons BY-NC-SA
More information at https://ocw.mit.edu/terms
More courses at https://ocw.mit.edu
Support OCW at http://ow.ly/a1If50zVRlQ

We encourage constructive comments and discussion on OCW’s YouTube and other social media channels. Personal attacks, hate speech, trolling, and inappropriate comments are not allowed and may be removed. More details at https://ocw.mit.edu/comments.



Learn more about your ad choices. Visit megaphone.fm/adchoices

Learn more about your ad choices. Visit megaphone.fm/adchoices

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