regression part 2

README.md

@@ -79,8 +79,8 @@ Thu, Oct 13 | [Experimental design (part II)](pages/oct13-counterbalancing.md) |
 ~~Tue, Oct 18~~ | ~~Fall break, no class~~ |
 ~~Thu, Oct 20~~ | ~~Fall break, no class~~ |
 Tue, Oct 25 | [Experimental design (part III)](pages/oct25-experiment-stats.md) | [slides](slides/13-experiments-3.pdf) • [video](https://www.youtube.com/watch?v=Qk8vNy3m3vU)
-Thu, Oct 27 | [Intro to regression modeling + diagnostics](pages/oct27-regression-pt1.md) | [slides](slides/14-regression-1.pdf)
-Tue, Nov 1 | Standardized coefficients + Mixed-effects |
+Thu, Oct 27 | [Intro to regression modeling](pages/oct27-regression-pt1.md) | [slides](slides/14-regression-1.pdf) • [video](https://youtu.be/HTC7dPY-F34)
+Tue, Nov 1 | [Diagnostics, factors, std coefficients](pages/nov01-regression-pt2.md) | [slides](slides/15-regression-2.pdf) • [video](https://www.youtube.com/watch?v=5p8wtSmwkEE)
 Thu, Nov 3 | Exemplar regression papers |
 Tue, Nov 8 | Simpson’s paradox + Mixed-effects |
 Thu, Nov 10 | Interrupted time series design |

pages/nov01-regression-pt2.md

@@ -0,0 +1,59 @@
+## L15: Linear Regression (Part II) ([pdf](../slides/15-regression-2.pdf), [video](https://www.youtube.com/watch?v=5p8wtSmwkEE))
+
+[![Lecture15-Regression-Diagnostics](../assets/images/15-regression-2.jpeg)](../slides/15-regression-2.pdf)
+
+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.
+
+The importance of having a good understanding of linear regression before studying more complex statistical models cannot be overstated.
+
+
+### Lecture Readings
+
+
+> 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.
+
+---
+
+> 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. 
+
+Chapters 22-24 (Modeling) are the most relevant for this lecture.
+
+---
+
+> 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.
+
+---
+
+> 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.
+
+
+### Additional Readings
+
+
+> 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.
+
+---
+
+> 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
+
+---
+
+> Freedman, D., Pisani, R., & Purves, R. (2007). [Statistics](https://wwnorton.com/books/9780393929720). W. W. Norton & Company.
+
+
+
+
+

pages/oct27-regression-pt1.md

@@ -1,4 +1,4 @@
-## L14: Intro to Linear Regression ([pdf](../slides/14-regression-1.pdf), video)
+## L14: Intro to Linear Regression ([pdf](../slides/14-regression-1.pdf), [video](https://youtu.be/HTC7dPY-F34))
 
 [![Lecture14-Regression-Intro](../assets/images/14-regression-1.jpeg)](../slides/14-regression-1.pdf)