Regression Analysis

Alex Ramiller

Senior Data Science Fellow 2024-2025, Data Science Fellow 2023-2024

City and Regional Planning

I am a PhD Candidate in City and Regional Planning. My research focuses on the use of large administrative datasets to study residential mobility, neighborhood change, and housing access. I received a Master in Geography from the University of Washington and a Bachelor's in Economics and Geography from Macalester College. I have also consulted on analytical projects for several organizations including the San Francisco Federal Reserve Bank, PolicyLink, and the City of Seattle.

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Farnam Mohebi

Data Science Fellow 2023-2024, Data Science for Social Justice Senior Fellow 2024

Haas School of Business

I am a PhD student at the Haas School of Business, University of California, Berkeley, and a researcher in the Department of Radiation Oncology at the University of California, San Francisco, having previously earned my MD and MPH degrees. My research focuses on the intersection of professionals and emerging technologies, drawing from the fields of medical sociology, organizational theory, and science and technology studies. I am particularly fascinated by the evolving relationship between physicians and artificial intelligence, the phenomenon of physician influencers, and the social...

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Valeria Ramírez Castañeda

Data Science for Social Justice Fellow (2024-2025)

Integrative Biology

Valeria Ramírez Castañeda is a Colombian biologist currently pursuing a PhD in the Department of Integrative Biology at the University of California, Berkeley. I completed my undergraduate degree in Biology at the National University of Colombia and earned a master's degree in Ecology and Evolution, as well as another in Science Communication. During her PhD, she is studying the interactions between snakes and frogs and how this influences the evolution of toxin resistance in snakes. She is also collaborating and leading projects regarding the consequences of English in science and the...

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Causal Thinking in Thermal Comfort

September 17, 2024

Ruiji Sun

by Ruiji Sun. We demonstrate the importance of causal thinking by comparing two linear regression approaches used in thermal comfort research: Approach (a), which regresses thermal sensation votes (y-axis) on indoor temperature (x-axis); Approach (b), which does the reverse, regressing indoor temperature (y-axis) on thermal sensation votes (x-axis). From a correlational perspective, they may appear interchangeable, but causal thinking reveals substantial and practical differences between them. Using the same data, we found Approach (b) leads to a 10 °C narrower than the conventionally derived comfort zone using Approach (a). This finding has important implications for occupant comfort and building energy efficiency. We highlight the importance of integrating causal thinking into correlation-based statistical methods, especially given the increasing volume of data in the built environment.

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