This is a core course that provides essential grounding in statistical inference and modelling relating to science and food technology. Students will learn how to design, conduct, and analyse the ...

Introduces methods, theory and applications of statistical models, from linear models (simple and multiple linear regression), to hierarchical linear models. Topics such as estimation, residual ...

The purpose of statistical model selection is to identify a parsimonious model, which is a model that is as simple as possible while maintaining good predictive ability over the outcome of interest.

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 ...

A semester-long study of topics in Statistics. Topics and emphases will vary according to the instructor. This course may be repeated for credit with different topics. See the New and Topics Courses ...

Demand is at an all-time high for data analysts who can help organizations, technology companies, governments, and nonprofit agencies grasp their organizational, societal, and scientific needs. The ...

Cellular dynamics are intrinsically noisy, so mechanistic models must incorporate stochasticity if they are to adequately model experimental observations. As well as intrinsic stochasticity in gene ...