Despite the widespread adoption of Gradient Boosting Decision Trees (GBDTs), practitioners lack systematic criteria for determining when linear models are more effective. This knowledge gap impacts model selection in applications where
computational efficiency, interpretability, and extrapolation capabilities are required. This study addresses this issue through five systematic experiments that isolate data characteristics: linearity dominance, feature interactions, extrapolation requirements, small-sample scenarios, and interpretability needs. Our multi-dimensional evaluation
framework integrates predictive performance with computational and interpretability costs, providing a comprehensive empirical comparison of linear regression and GBDTs. Linear models significantly outperformed GBDTs under four critical conditions.

This study investigated the status of learning statistical inference based on the new high school curriculum guidelines, focusing on problems related to the standard normal distribution to assess understanding.
Furthermore, based on past incorrect answer data for the standard normal distribution, effective teaching methods were proposed, implemented in educational practice, and their effectiveness was verified.
Regarding problems on the standard normal distribution, having students draw graphs to understand the relationship with the formula and then review their work led to an increase in the correct answer rate. However, a significant number of errors persisted, including those based solely on rote understanding and careless mistakes. Measures to address careless mistakes still have room for improvement.

くじら産業の街である下関市と北九州市は、くじらや捕鯨の歴史や文化を持ちながら、従前よりそれらが観光ツアー商品の対象となることは無かった。2019（令和元）年に商業捕鯨が再開され、下関市が国内唯一の母船式捕鯨基地となったことを契機に、関門両市に存在するくじらの歴史、文化等を辿るツアーが新たな商品となりうるのか、行政や旅行会社へのヒアリング等を行い、その実現可能性について検証した。