A Friendly Approach To Regression Models For Programmers

Summary 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. Announcements Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science. When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! 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 Interview Introductions 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 LinkedIn @MatthewBRudd on Twitter Picks Tobias The Argument podcast from the NY Times Matthew Primus Claypool Lennon Delirium South of Reality Links 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 Closing Announcements Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA