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EPsy 8264: Advanced Multiple Regression Analysis

This GitHub repository includes materials for the course EPsy 8264: Advanced Multiple Regression Analysis. You can access the course website at: https://github.com/zief0002/epsy-8264



Downloading the Course Materials

To download all of the materials simultaneously from this site, click on the Clone or Download button and select Download ZIP. This will download a ZIP file of the entire site on your local computer.

To download individual PDF files, open the file link and then click on the Download button. CSV files can be individually download by opening their links, clicking on the Raw button. This should display the text of the CSV in your browser window. If you right-click on this text, you should be able to Save as or Save page as (or something along those lines).



Unit 13: Piecewise Regression Models

In this unit we will learn about piecewise models as a method for fitting local models. The notes for this unit are available as an HTML file at:

In addition to the class notes and what we cover in class, there many other resources for learning about piecewise models. Here are some resources that may be helpful in that endeavor:

  • Berk, R. (2016). Splines, smoothers, and kernels. Statistical learning from a regression perspective (2nd ed., pp. 55–127). New York: Springer.
  • Fox, J. (2016). Nonlinear regression. Applied regression analysis and generalized linear models (3rd ed., pp.502–527). Thosand Oaks, CA: Sage.
  • James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). Moving beyond linearity. An introduction to statistical learning: with applications in R (pp. 265–301). New York: Springer.
  • Statistical Learning MOOC taught by Hastie and Tibshirani


Unit 14: Regression Splines

In this unit we will learn about regression splines as a method for fitting local models. The notes for this unit are available as an HTML file at:

In addition to the class notes and what we cover in class, there many other resources for learning about piecewise models. Here are some resources that may be helpful in that endeavor:

  • Berk, R. (2016). Splines, smoothers, and kernels. Statistical learning from a regression perspective (2nd ed., pp. 55–127). New York: Springer.
  • Fox, J. (2016). Nonlinear regression. Applied regression analysis and generalized linear models (3rd ed., pp.502–527). Thosand Oaks, CA: Sage.
  • James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). Moving beyond linearity. An introduction to statistical learning: with applications in R (pp. 265–301). New York: Springer.
  • Statistical Learning MOOC taught by Hastie and Tibshirani


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