Exam 02 review
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
- How are interpretations for logistic regression similar to interpretations for linear regression with response
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
