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Statistical Modeling of Spatial and Time Series Data

Introduction to Bayesian modeling for data with spatial and/or time dependence. Exploratory analysis of spatial (point referenced and areal) and time series data. Gaussian processes and generalizations. Extending hierarchical Bayesian linear models and generalized linear models. Spatial models: CAR, SAR, kriging and time series models: ARM, ARMA, dynamic linear models. Computational methods for model fitting and diagnostics. Prerequisite: STA360, STA601 or equivalent. One course.

Research Independent Study

Individual research in a field of special interest, under the supervision of a faculty member, resulting in a substantive paper or written report containing significant analysis and interpretation of a previously approved topic. Consent of instructor and Director of Undergraduate Studies required. Prerequisite: STA360. One course.

Introduction to Statistical Consulting

Immerses students into real world consulting, exposing them to all aspects of research including data collection, modeling, and evaluating results. Through campus-wide consulting program, students work with researchers from various disciplines providing recommendations for statistical methodologies appropriate for their research. Projects examined through lens of research ethics underlying data collection, model assumptions, analysis, reproducibility, and reporting of results.

Statistical Modeling of Spatial and Time Series Data

Introduction to Bayesian modeling for data with spatial and/or time dependence. Exploratory analysis of spatial (point referenced and areal) and time series data. Gaussian processes and generalizations. Extending hierarchical Bayesian linear models and generalized linear models. Spatial models: CAR, SAR, kriging and time series models: ARM, ARMA, dynamic linear models. Computational methods for model fitting and diagnostics. Prerequisite: STA360, STA601 or equivalent. One course.

Case Studies in the Practice of Statistics

Students apply statistical analysis skills to in-depth data analysis projects ranging across diverse application areas including but not limited to energy, environmental sustainability, global health, information and culture, brain sciences, and social networks. Students practice cutting-edge statistical methods and communicate their results both technically and non-technically via presentations and written reports. Prerequisite: STA360 or instructor permission. One course.

Research Independent Study

Individual research in a field of special interest, under the supervision of a faculty member, resulting in a substantive paper or written report containing significant analysis and interpretation of a previously approved topic. Consent of instructor and Director of Undergraduate Studies required. One course.

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