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Explores statistical hypothesis testing, including constructing confidence intervals, interpreting p-values, and making decisions based on significance levels.
Delves into mechanosensory interactions driving collective behavior in Drosophila, exploring odor responses, group movement, and touch-triggered reactions.
Explains the significance analysis of spatial autocorrelation using Moran's I and random permutations, emphasizing the importance of spatial weighting.
Explores the Decision Theory Framework in Statistical Theory, viewing statistics as a random game with key concepts like admissibility, minimax rules, and Bayes rules.
Explores the concept of explainable neural networks and their significance in improving model interpretability, particularly in finance and house price valuation.
