A big part of his work is figuring out when a statistical method is truly the best choice. Some problems have straightforward ...

Statistical modeling continues to deliver distinct value to businesses both independent of, and in concert with, machine learning. “Artificial intelligence” (AI) and “machine learning” are among the ...

Abstract: Assumptions play a pivotal role in the selection and efficacy of statistical models, as unmet assumptions can lead to flawed conclusions and impact decision ...

Livestock agriculture (e.g., dairy, beef, pork, poultry) in the United States is tending rapidly toward operations where a large number of animals are concentrated in a relatively small area. The ...

A model is proposed for the study of plant breeding where the self-fertilization rate is of importance. The model can be used to provide convenient maximum likelihood estimation of the ...

Researchers have created a statistical method that may allow public health and infectious disease forecasters to better predict disease reemergence, especially for preventable childhood infections ...

Statistical models predict stock trends using historical data and mathematical equations. Common statistical models include regression, time series, and risk assessment tools. Effective use depends on ...

Researchers have created and preliminarily tested what they believe may be one of the first models for predicting who has the highest probability of being resistant to COVID-19 in spite of exposure to ...

The Statistical & Data Sciences (SDS) Program links faculty and students from across the college interested in learning things from data. At Smith, students learn statistics by doing—class time ...