Exam 02 review
Dec 03, 2024
Announcements
Project peer review due today at 11:59pm
Round 1 submission (optional) due December 8 at 11:59pm
Final report and GitHub repo due December 12 at 11:59pm.
Regular office hours end Thursday at 10am.
Project feedback
I would appreciate your feedback on the final project!
Earn 1 point on your final project grade by completing the feedback survey (individually scored) by December 12.
All feedback will be anonymous.
The survey is administered by Dr. Jess Dewey in Learning Innovation. Dr. Dewey give me the names of students who have completed the survey (without responses) for the purposes of awarding extra credit.
I will receive the anonymized feedback after final course grades are submitted to DukeHub.
Course and TA evaluations
I would appreciate your feedback about the course!
Course and TA evaluations are now available and are due December 9
If there is at least 80% on the course evaluations and TA evaluations, everyone in the class will receive 1 point on their final homework average
Should receive emails with access to the course and TA evaluations.
Exam 02 format
50 points total
in-class: 38 points
take-home: 12 points
In-class: 75 minutes during Thursday, December 5 lecture
Take-home: due Friday, December 6 at 11:59pm (grace period: can submit without late penalty until Saturday, December 7 at 11:59pm)
Need a note from your academic dean if you miss 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.
Multiple linear regression
Maximum likelihood estimation
Model diagnostics
Multicollinearity
Variable transformations
Model comparison
Logistic regression
Probabilities, odds, odds ratios
Maximum likelihood estimation
Predicted probabilities and classes
ROC curve and AUC
Inference
Assumptions
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
- e.g., Why do we include “holding all else constant” in interpretations?
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 December 5 at 11:45am)
Resources
Lecture notes and application exercises
Lecture recordings available until start of the exam
HW and lab assignments
- Lab 06 returned on Gradescope. HW 04 key on Canvas until start of the exam
