In the United States, support for gun control has been declining among all age groups since 1990; among young adults, support is substantially lower than among previous generations. Using data from the General Social Survey (GSS), I perform age-period-cohort analysis to measure and predict these generational effects.
In this talk, I demonstrate a computational approach to statistics that replaces mathematical analysis with random simulation. Using Python and libraries like NumPy and StatsModels, we can define basic operations — like resampling, filling missing values, modeling, and prediction — and assemble them into a data analysis pipeline.
This approach is more flexible than math-based statistics, and fits better into an incremental software development process. The result is a set of versatile, reusable components and novel analytic methods.