Exam 02 review

Author

Prof. Maria Tackett

Published

Apr 15, 2025

Announcements

  • Statistics experience due April 22 at 11:59pm

  • Exam 02 on Thursday during lecture

    • Ed Discussion will be read-only once exam starts

    • No office hours Thursday 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 22 and 23

    • Click here to sign up (one time slot per team) by April 21 at 9pm

  • Written report due April 28

  • Project highlights & final repo due April 30

  • Project survey & team feedback due May 1

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 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

  • Likelihood functions and Maximum likelihood estimation

  • Properties of estimators

  • 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

  • Not on the exam: Newton-Raphson method

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

📋 sta221-sp25.netlify.app/ae/ae-07-exam-02-review