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. (Not open to students who have taken BIOL 0211 or ECON 0111 (formerly ECON 0210) or PSYC 0201)

Spatial Statistics In this course students will be introduced to statistical methodology for data that are collected over space. Following a brief introduction to Bayesian inference and probabilistic programming languages (Stan, NIMBLE, and JAGS), we will investigate how spatial dependence challenges classical modeling assumptions. We will learn about spatial correlation and covariance functions, Poisson point processes, methods for areal data, and methods for geostatistical data including Gaussian process regression and ordinary Kriging. Students will also develop skills in visualizing and summarizing spatially correlated data structures, learn to fit spatial models in R, and develop the theory of spatial statistics. (STAT 0310, STAT 0311 concurrently).

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.

Statistical Consulting In this course we will work with community partners to solve real-world problems using modern statistical and data science techniques. Students will work in small groups to translate research questions into actionable analysis and visualizations. Students will select a project of interest from a subset of community partners, maintain contact and collaboration with the community partner, and present their findings in a final symposium.

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.

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.

Christian Stratton

Reviews and ratings for Christian Stratton at Middlebury College.Christian Stratton teaches Intro to Statistical Science, Spatial Statistics, Time Series Analysis and more.  Explore top rated instructors and find the best for you.Is Christian Stratton good ? Find out on MiddCourses.

I really liked this class- office hours are super accessible, Prof Stratton will break down concepts if you don't understand concepts. He is very passionate. You will have to do data fest. Nearly half the class is group project work. 

One of my favorite classes at Middlebury. I would definitely take more classes with Stratton if I had the math prereqs. He was very fair with grading, didn't assign too much homework, and overall was an excellent instructor

This is a fun class, Professor Stratton goes deep into topics, and cares deeply about his students. The class is mostly a few time series reports and a couple of in-class/take-home exams. Overall, this class is very manageable and covers a cool topic.