Christian Stratton
Asst. Prof. of Mathematics & Statistics
- Office
- Warner Hall 203
- cstratton@middlebury.edu
- Office Hours
- Tuesday 3:30-4:30 PM; Wednesday 2:00-4:00 PM; Thursday 3:30-4:30 PM; Friday 10:30-11:30 AM; and by appointment
I am an Assistant Professor of Statistics, and have been at Middlebury College since the Fall of 2024. I received my Ph.D. in Statistics from Montana State University in May of 2022, after which I worked as a research statistician affiliated with the United States Geological Survey. My research interests include Bayesian hierarchical models for ecological and environmental data, and typically focus on problems with complex temporal or spatial dependence.
When not working, my hobbies include fly fishing, backpacking, and playing video games. Students interested in research with me can send me an email or visit my office at any time.
Courses Taught
STAT 0116
Upcoming
Intro to Statistical Science
Course Description
Introduction to Statistical Science (formerly MATH 0116)
A practical introduction to statistical methods and the examination of data sets. Computer software will play a central role in analyzing a variety of real data sets from the natural and social sciences. Topics include descriptive statistics, elementary distributions for data, hypothesis tests, confidence intervals, correlation, regression, contingency tables, and analysis of variance. The course has no formal mathematics prerequisite, and is especially suited to students in the physical, social, environmental, and life sciences who seek an applied orientation to data analysis. (Credit is not given for MATH 0116 if the student has taken ECON 0111 (formerly ECON 0210) or PSYC 0201 previously or concurrently.) 3 hrs. lect./1 hr. computer lab.
Terms Taught
Requirements
STAT 0218
Upcoming
Statistical Learning
Course Description
Statistical Learning (formerly MATH 0218)
This course is an introduction to modern statistical, machine learning, and computational methods to analyze large and complex data sets that arise in a variety of fields, from biology to economics to astrophysics. The theoretical underpinnings of the most important modeling and predictive methods will be covered, including regression, classification, clustering, resampling, and tree-based methods. Student work will involve implementation of these concepts using open-source computational tools. (MATH 0118 or STAT 0118 or STAT 0201 or MATH 0216 or BIOL 1230 or ECON 1230 or ENVS 1230, or FMMC 1230 or HARC 1230 or JAPN 1230 or LNGT 1230 or NSCI 1230 or MATH 1230 or SOCI 1230or WRPR 1230 or GEOG 1230) 3 hrs. lect./disc.
Terms Taught
Requirements
STAT 0219
Time Series Analysis
Course Description
Time Series Analysis
An introduction to statistical methods for time series analysis for students with a background in statistics. Topics include time series regression, auto-regressive models, moving average models, and ARIMA models, with an emphasis on estimation and forecasting with real data applications. Students will develop skills visualizing and summarizing serially correlated data structures and fitting time series models in various statistical software packages, including R and Julia. (STAT 116 or STAT 201 and MATH 0200 concurrently, or by waiver.)
Terms Taught
Requirements
STAT 0311
Statistical Inference
Course Description
Statistical Inference
An introduction to the mathematical methods and applications of statistical inference using both classical methods and modern resampling techniques. Topics will include: permutation tests, parametric and nonparametric problems, estimation, efficiency and the Neyman-Pearsons lemma. Classical tests within the normal theory such as F-test, t-test, and chi-square test will also be considered. Methods of linear least squares are used for the study of analysis of variance and regression. There will be some emphasis on applications to other disciplines. This course is taught using R. (MATH 0310) 3 hrs. lect./disc.
Terms Taught
Requirements
STAT 0500
Upcoming
Advanced Study
Course Description
Independent Study
Individual study for qualified students in more advanced topics in statistics. Particularly suited for those who enter with advanced standing. (Approval required) 3 hrs. lect./disc.
Terms Taught