#20 Regression and Other Stories, with Andrew Gelman, Jennifer Hill & Aki Vehtari

21 snips

Jul 30, 2020

Guest

Andrew Gelman

Join Andrew Gelman, a statistics and political science professor at Columbia, Jennifer Hill from NYU specializing in causal questions, and Aki Vehtari, an expert in computational modeling from Aalto University, as they dive into the enchanting world of regression analysis. They share insights on their writing journey, offer ten tips to enhance regression modeling, tackle the challenges of statistical significance, and reveal the power of storytelling in data education. Plus, there's a whimsical discussion about exploring Mars!

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Intro

00:00 • 5min

Collaboration and Bayesian Insights

04:49 • 16min

Navigating Uncertainty in Statistical Analysis

20:34 • 2min

The Art of Data Visualization and Modeling

22:39 • 18min

Understanding Linear Regression Assumptions

40:20 • 11min

The Power of Storytelling in Data Education

50:53 • 2min

Navigating Statistical Challenges in Political Science

52:57 • 8min

Reflections and Recommendations on Regression Techniques

01:01:17 • 2min

Once upon a time, there was an enchanted book filled with hundreds of little plots, applied examples and linear regressions — the prettiest creature that was ever seen. Its authors were excessively fond of it, and its readers loved it even more. This magical book had a nice blue cover made for it, and everybody aptly called it « Regression and other Stories »!

As every good fairy tale, this one had its share of villains — the traps where statistical methods fall and fail you; the terrible confounders, lurking in the dark; the ill-measured data that haunt your inferences! But once you defeat these monsters, you’ll be able to think about, build and interpret regression models.

This episode will be filled with stories — stories about linear regressions! Here to narrate these marvelous statistical adventures are Andrew Gelman, Jennifer Hill and Aki Vehtari — the authors of the brand new Regression and other Stories.

Andrew is a professor of statistics and political science at Columbia University. Jennifer is a professor of applied statistics at NYU. She develops methods to answer causal questions related to policy research and scientific development. Aki is an associate professor in computational probabilistic modeling at Aalto University, Finland.

In this episode, they tell us why they wrote this book, who it is for and they also give us their 10 tips to improve your regression modeling! We also talked about the limits of regression and about going to Mars…

Other good news: until October 31st 2020, you can go to http://www.cambridge.org/wm-ecommerce-web/academic/landingPage/GoodBayesian2020 and buy the book with a 20% discount by entering the promo code “GoodBayesian2020” upon checkout!

That way, you’ll make up your own stories before going to sleep and dream of a world where we can easily generalize from sample to population, and where multilevel regression with poststratification is a bliss…

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Links from the show:

Regression and Other Stories on Cambridge Press website: http://www.cambridge.org/wm-ecommerce-web/academic/landingPage/GoodBayesian2020
Amazon page (because of VAT laws, in some regions ordering from Amazon can be cheaper than from the editor directly, even with the discount): https://www.amazon.com/Regression-Stories-Analytical-Methods-Research/dp/110702398X
Code, data and examples for the book: https://avehtari.github.io/ROS-Examples/
Port of the book in Python and Bambi: https://github.com/bambinos/Bambi_resources/tree/master/ROS
Andrew's home page: http://www.stat.columbia.edu/~gelman/
Andrew's blog: https://statmodeling.stat.columbia.edu/
Andrew on Twitter: https://twitter.com/statmodeling
Jennifer's home page: