Statistical Modeling and Machine Learning I
This is the first course in a two-term sequence in applied statistical methods and machine learning that are the basis in handling complex datasets. The topics covered include: overview on the quantitative research, linear regression, analysis of variance, inference, prediction, model diagnostics and selection and resampling methods. The emphasis will be to understand and apply the methods.
Topics
- overview on the quantitative research
- linear regression
- analysis of variance
- inference
- prediction
- model diagnostics and selection and resampling methods
Topics explicitly listed in official catalogue description
Prerequisites & course information
Restriction: Must be in Survey Methodology (Master's) program; or permission of instructor. Additional Information: Must have basic R Programming skills; and must have completed a two course sequence in probability and statistics; or students who have comparable content may contact the department for permission.
Sources
- Course catalogue ↗2026-2027