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Early projections for 2024 based on previous Presidential and House returns slighly favor Republicans. These projections are completely unrelated to Biden’s recent polling numbers. Here’s the story behind this approach: In early 2020, I ran battleground state election forecasts for Acronym.

TLDR: [UPDATED SEPT 30] Yesterday, Science published a letter I wrote arguing that there is little evidence of algorithmic bias in Facebook’s feed ranking system that would serve to increase ideological segregation, also known as the “Filter Bubble” hypothesis.

TL/DR Summary BlueSky has a chance to dethrone twitter right now, but that path is narrow. Its exclusive invite only model means its user base is now small, elite, and homogenous with few bad actors.

Last Friday (2023-03-31) Twitter released what it calls “the algorithm,” which appears to be a highly redacted, incomplete part of code that governs the “for you” home timeline ranking system. And I saw nothing to suggest the parts of the code they put in the GitHub repository wasn’t authentic.

It’s becoming clear that the 2020 polls underestimated Trump’s support by anywhere from a 4-8 point margin depending on your accounting–a significantly worse miss than in 2016, when state polls were off but the national polls did relatively well.

According to the latest polling research, Trump’s chances of hanging on to power beyond 2020 look pretty dismal. Nate Cohn published an impressive battleground poll from New York Times/Sienna showing Biden ahead of Trump by at least six points in pivotal states.

Do you remember the night of Nov 8, 2016? I was glued to election coverage and obsessively checking probabilistic forecasts, wondering whether Clinton might do so well that she’d win in places like my home state of Arizona.

Second post in my data visualization series on scatterplots and regression analysis

Learn how to blog in Academic using Jupyter notebooks

Regression models are a cornerstone of modern social science. They’re at the heart of efforts to estimate causal relationships between variables in a multivariate environment and are the basic building blocks of many machine learning models.