Exam 02 review

Prof. Maria Tackett

Apr 15, 2025

Announcements

  • Statistics experience due TODAY at 11:59pm

  • Lab 06 due TODAY at 11:59pm

  • Exam 02 on Thursday during lecture

    • Ed Discussion will be read-only once exam starts

    • No office hours Thursday after 3pm and Friday

Course evaluations

Please share your feedback about the course!

Course evaluations are open now until April 26 at 11:59pm.

  • If the response rate is at least 80%, everyone in the class will receive 0.5 points (out of 50) on their Exam 02 grade.

  • Should receive emails with links to course evaluations.

Project

  • See peer feedback in the Issues of GitHub repo

  • Optional project meetings April 21 and 22

  • Written report due April 30

  • Project highlights & final repo due May 2

  • Project survey & team feedback due May 3

Note

Reminders and updates sent through Canvas announcements.

Exam 02 format

  • 50 points total

    • in-class: 35 points

    • take-home: 15 points

  • In-class: 75 minutes during April 17 lecture

    • See email for classroom assignment

  • Take-home: due Sunday, April 20 at 11:59pm

  • Official university documentation (not incapacitation form) or note from your academic dean required to excuse any part of the exam

Exam 02 content

Concepts from the first half of the semester continue to apply, but the exam will focus on new content since Exam 01.

  • Model diagnostics

  • Multicollinearity

  • Variable transformations

  • Probabilities, odds, odds ratios

  • Fitting and interpreting logistic model

  • Predicted probabilities and classes

  • ROC curve and AUC

  • Inference for logistic regression

  • Assumptions for logistic regression

  • Model comparison

  • Multinomial logistic regression

  • Cross validation

Tips for studying

  • Rework derivations from assignments and lecture notes

  • Review exercises in AEs and assignments, asking “why” as you review your process and reasoning

  • Understand similarities and differences between linear and logistic regression

    • How are interpretations for logistic regression similar to interpretations for linear regression with response \(\log(y)\)? How are they different?

  • Focus on understanding not memorization

  • Explain concepts / process to others

  • Ask questions in office hours

  • Review lecture recordings as needed (available until start of in-class exam)

Resources

  • Lecture notes, AEs, labs, homework

  • Lecture recordings available until start of the exam (link in course website menu)

  • HW and lab assignments

    • Keys Lab 06 and HW 04 on Gradescope

  • Exam 02 practice problems (link in course website menu)

Application exercise

📋 sta210-sp25.netlify.app/ae/ae-12-exam-02-review