March 2020

S1E21: Integrative Data Analysis — The Quest for a Cooperative, Cumulative Science

Patrick and Greg are inspired by the community support we are witnessing during this time of crisis, and they expand this discussion into the community of science. They drill down into the collaborative approach of integrative data analysis in which models are fit to data that have been aggregated across multiple independent samples. They also […]

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S1E20: Finite Mixture Modeling & Sir Mixture-A-Lot

Greg and Patrick waste an hour of your time drunkenly wandering along the dangerous cliffs overlooking mixture modeling. In addition to exploring the potential promises and unadulterated dangers of mixture models as a mode of scientific inquiry, they also mention drama nerds, hunchbacks, date night for the Lincolns, name dropping, UConn women’s basketball, inductive-deductive wishin’-and-a-hopin’,

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S1E19: Live from The Rusty — Irish Storytelling & Quantitative Limericks

Patrick and Greg celebrate St. Patrick’s Day with Irish storytelling of some of their own epic academic fails, interspersed with a selection of quantitative limericks submitted by Quantitude listeners (as well as a few of their own). The final selections are read by a special guest (who, be warned in advance, is a bit grumpy).

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S1E18: A Conversation with Katherine Masyn — The Power of Three

Patrick and Greg have a blast hanging out with quant super-star Katherine Masyn, arguably one of the coolest people on earth. In addition to talking about professional development, the futility of making plans, and how to make meaningful contributions to science, the trio also discuss the power of three, mad cow disease, evaporated metal films,

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S1E17: Planned Missing Designs (or, How to Beat a Metaphor to Death)

Greg and Patrick start with what seems like a reasonable metaphor linking musical compositions to planned missing data designs, and then they proceed to beat it to death. Then, just when you think they’ve buried it, they dig it back up and beat the carcass some more before eventually just running out of time. In

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