Statistical regression models are a staple of predictive forecasts in a wide range of applications. In this episode Matthew Rudd explains the various types of regression models, when to use them, and his work on the book "Regression: A Friendly Guide" to help programmers add regression techniques to their toolbox.
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- Your host as usual is Tobias Macey and today I’m interviewing Matthew Rudd about the applications of statistical modeling and regression, and how to start using it for your work
- How did you get introduced to Python?
- Can you start by describing some use cases for statistical regression?
- What was your motivation for writing a book to explain this family of algorithms to programmers?
- What are your goals for the book?
- Who is the target audience?
- What are some of the different categories of regression algorithms?
- What are some heuristics for identifying which regression to use?
- How have you approached the balance of using software principles for explaining the work of building the models with the mathematical underpinnings that make them work?
- What are some of the concepts that are most challenging for people who are first working with regression models?
- What are the most interesting, innovative, or unexpected ways that you have seen statistical regression models used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on your book?
- What are some of the resources that you recommend for folks who want to learn more about the inner workings and applications of regression models after they finish your book?
Keep In Touch
- Regression: A Friendly Guide
- Sewanee University of the South
- Sewanee Data Lab
- Mark Lutz Python books
- Elements of Statistical Learning
- Linear Regression
- Logistic Regression
- Modeling Binary Data
- Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
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