Problem Sets

Weight 18% of final grade

Components 2 problem sets

Evaluation Completion-based

Submission Canvas / Gradescope

Two problem sets provide practice applying the statistical concepts and methods developed in the course. They draw primarily on exercises from the assigned textbooks and related course applications.

Problem sets are graded for completion rather than correctness. A complete submission demonstrates a good-faith attempt at every requested part, including code, output, calculations, and brief interpretations when required. Simply uploading a file without substantive attempts does not count as complete.

The required submission format and the collaboration and AI-use expectations will be stated in the instructions for each problem set. Submit all materials through the platform identified in those instructions.

Grace period: Late submissions will be accepted for up to 24 hours after the due date with no penalty and without an extension request. After the 24-hour grace period, late assignments will receive a 25% deduction for each additional day.

Problem Set 1

Due: September 30

Logistic regression and machine learning

Apply methods from the first part of the course through a set of guided quantitative exercises. The problem set will emphasize implementing and interpreting logistic regression and working through regression and classification problems using statistical and machine-learning approaches. Specific questions, datasets, and submission requirements will be provided with the assignment.

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Problem Set 2

Due: October 21

Ordinal models and text analysis

Apply advanced modeling techniques through exercises in ordinal logistic regression and text analysis. The problem set will focus on selecting, implementing, interpreting, and communicating appropriate methods for ordered outcomes and unstructured text. Specific questions, datasets, and submission requirements will be provided with the assignment.

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