Forecasting business cycles and macroeconomic trends is inherently challenging due to their complex and non-linear relationships with volatile and noisy economic factors. However, the growing availability of large-scale economic data, coupled with advances in computational power, creates new opportunities to extract meaningful information and develop robust predictive models. De...

Hierarchical linear mixed models are commonly used in many scientific fields. However, without a strong statistical background, it can be hard to understand the relationships between the random effect variables and the inferences that can be made when a model has nested random effects. Visualizing relationships makes it easier for the practitioner to understand what relationship...

This study investigates how user ability to manipulate plot features affects graphical perception, by extending a previous graphical study (Vanderplas and Hofmann, 2017) with an interactive framework. Similar to the original study, statistical lineups included two target patterns (a linear trend and a clustering pattern), as well as eighteen null plots generated from three diffe...

Satellite precipitation products have the potential to be employed for the purpose of better understanding extreme precipitation events in remote mountainous terrain, where weather stations and radar data tend to be sparse. For this reason, it is crucial to assess how closely satellite estimates agree with ground observations during extreme events, and how that agreement varies ...

In data analysis, unexpected results often prompt researchers to revisit their procedures to identify potential issues. While some researchers may struggle to identify the root causes, experienced researchers can often quickly diagnose problems by checking a few key assumptions. These checked assumptions, or expectations, are typically informal, difficult to trace, and rarely di...