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Statistical solutions for interdisciplinary problem-solving
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AI
DOI：10.1080/08982112.2024.2430610.png)
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Abstract
En 中文
Interdisciplinary problem-solving draws upon expertise from multiple fields and often requires teams of individuals from different disciplines working together to address a complex challenge. Statisticians can play an important role in addressing interdisciplinary challenges by providing a statistical framework for modeling and analysis. In this paper, statistical distributions and metrology concepts will be used to provide a foundation for modeling errors, constructing different types of statistical intervals, and characterizing error transmission to support further modeling and analysis. Examples of statistical solutions involving methods for Design and Analysis of Experiments, Functional Data Analysis, Predictive Analytics, and Data Science will be discussed that were developed as part of the collaborative process of interdisciplinary problem-solving.
Keywords:
data science
design of experiments
error transmission
functional data analysis
interdisciplinary teams
metrology
predictive analytics
statistical problem-solving
Journal
Q
IF:
2.2
Papers:
40
Citations:
1.4K