This course is a boot camp in statistical modeling and data visualization using the R computer language. Topics include basic R programming, data exploration, statistical modeling, formal model comparison, parameter estimation and interpretation, and the visual display of quantitative information. Students will learn how to use the R statistical environment to process, analyze, and visualize data. We will provide R code to execute all example analyses used in class; assignments will entail modifying and extending this code to solve similar problems. Statistical topics will focus primarily on various types of general linear models, generalized linear models (GLMs), and generalized linear mixed models (GLMMs) and formal model comparison using information criteria. We will also discuss data imputation, resampling, and basic simulations. Classes on data visualization will help students to learn principled, effective ways to visually depict data using R. This is not an introductory statistics course. Participants are expected to begin the course with a solid understanding of basic statistical methods (e.g., linear regression). No formal modeling experience, programming ability, or knowledge of advanced mathematics are required. Some prior experience with R is advisable, but not required.
Course Requirements:
Most of the course assessment (85%) will be based on weekly problem sets, which will be available asynchronously and can be completed and submitted via Canvas at any time prior to relevant deadlines. The balance of assessment (15%) will be based on attendance, preparation, and participation for the weekly two-hour lab meetings. Lectures will be given synchronously via videoconferencing software (Zoom or BlueJeans) and recorded for asynchronous viewing. Lab meetings will also be conducted synchronously via videoconferencing software (Zoom or BlueJeans). All problem set and lecture recordings will be posted on Canvas. Optional synchronous meetings and office hours with the instructor and GSI will be conducted through Zoom. Students should have access to a camera and microphone.
Class Format:
The lectures for this course will be recorded and made available asynchronously. We will also have weekly discussion sessions that require synchronous participation. We will strive to make accommodations for individual student circumstances as necessary. Please contact us if you have questions or concerns.