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## L15: Linear Regression (Part II) ([pdf](../slides/15-regression-2.pdf), [video](https://www.youtube.com/watch?v=5p8wtSmwkEE)) | ||
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[![Lecture15-Regression-Diagnostics](../assets/images/15-regression-2.jpeg)](../slides/15-regression-2.pdf) | ||
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This is the second lecture in a series dedicated to regression modeling. We talked about some of the things that can go wrong when estimating linear models and how to diagnose those. | ||
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The importance of having a good understanding of linear regression before studying more complex statistical models cannot be overstated. | ||
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### Lecture Readings | ||
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> James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). [An introduction to statistical learning](https://www.academia.edu/download/60707896/An_Introduction_to_Statistical_Learning_with_Applications_in_R-Springer_201320190925-63943-2cqzhk.pdf) (Vol. 112, p. 18). Springer. | ||
Chapter 3 reviews some of the key ideas underlying the linear regression model, as well as the least squares approach that is most commonly used to fit this model. | ||
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--- | ||
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> Grolemund, G., & Wickham, H. (2018). [R for data science](https://r4ds.had.co.nz/index.html). | ||
This book will teach you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it, visualise it, and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you’ll learn how to clean data and draw plots—and many other things besides. | ||
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Chapters 22-24 (Modeling) are the most relevant for this lecture. | ||
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> Bruce, P., Bruce, A., & Gedeck, P. (2020). [Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python](https://github.com/gedeck/practical-statistics-for-data-scientists). O'Reilly Media. | ||
Chapter 4 covers regression modeling. Note the emphasis on prediction, instead of the more common goal of explanation in empirical research. | ||
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> Goodman, S. (2008). [A dirty dozen: Twelve p-value misconceptions](https://www.ohri.ca/newsroom/seminars/SeminarUploads/1829%5CSuggested%20Reading%20-%20Nov%203,%202014.pdf). In Seminars in Hematology (Vol. 45, No. 3, pp. 135-140). WB Saunders. | ||
Among others, the paper addresses a common false belief that the probability of a conclusion being in error can be calculated from the data in a single experiment without reference to external evidence or the plausibility of the underlying mechanism. | ||
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### Additional Readings | ||
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> Woolridge, J. M. (2003). [Introductory econometrics: A modern approach](https://repository.fue.edu.eg/xmlui/bitstream/handle/123456789/2774/7831.pdf). Thomson, Mason. | ||
Probably the most in-depth coverage of regression modeling possible. More emphasis on theory than other sources. | ||
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> F.E. Harrell, Jr., [Regression Modeling Strategies](https://hbiostat.org/doc/rms.pdf), Springer Series in Statistics. | ||
- Chapter 1: Introduction | ||
- Chapter 2: General aspects of fitting regression models (especially 2.1–2.3, 2.7) | ||
- Chapter 4: Multivariable modeling strategies | ||
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> Freedman, D., Pisani, R., & Purves, R. (2007). [Statistics](https://wwnorton.com/books/9780393929720). W. W. Norton & Company. | ||
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