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?
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