Week 10
On Monday, 6/3, we will be in RPV Room A (1217)
Resources
| Lesson | Topic | Slides | Annotated Slides | Recording |
|---|---|---|---|---|
| 16 | Poisson Regression | |||
| 17 | Other types of categorical regressions! |
On the Horizon
Quiz 3 open 6/3 at 2pm
- Closes on 6/5 at 1pm
Lab 4 due yesterday!
HW 5 due 6/6 at 11pm
Project report due 6/13 at 11pm
Announcements
Monday 6/3
In the last stretch of the project
Class time next week dedicated to project report help
I may have other time slots for project help next week. I just need to take a serious look at my calendar
- I’m attending a virtual stat ed conference, so I just need to balance meetings with conference attendance
No office hours this Wednesday 6/5
#3 “The Last Bounce: Bunnies, Burritos, and DIY Bath Salts”
Wednesday, June 5th Noon-2pm
Student Success Center, 6th floor Vanport
Wednesday 6/5
Last day of lecture!!
Quiz 2: added 3 points to everyone’s grade for that one confusing question (I forget the number, maybe question 6?)
Lab 4
Your starting variables should be the TEN from lab 2, not the 5 from Lab 3
g-value for Hosmer-Lemeshow test
Please look at Lesson 12!! There is a note on how to pick the g-value when we have many samples!!
Some of us are getting NA’s when we put the correct
g- Double check that your observed values are in numeric form
If doing LASSO, make sure you describe the process!
We used LASSO regression with a penalty of 0.001 to identify important predictors. We used a single test and training split of 80% and 20%, respectively. Only main effects were considered in our LASSO regression.
Always think: what are the key pieces of information that someone else might need to recreate this method?
The class will end on June 14, 2024. All coursework is expected to be completed by June 16, 2024 at 11pm.
- I need to have grades in on June 21st. In order to grade fairly and thoroughly, I need the whole week to grade the project report and any late assignments.
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In the final lab, I gave you the option to do LASSO regression and focus on prediction. I know this created some confusion since we mostly set up the project as a question of association in Lab 1-3. We can still interpret the odds ratios from LASSO regression. I will be fairly lenient if reports are confused between prediction and association aims. I will try to give feedback on it, but I will not penalize any minor confusions.
Another word: My process starts harsh. I want to give you as much feedback as possible, and this will also reflect in lower assigned scores. At this point, I put the report grades into Sakai. I check to see if anyone’s overall course letter grade goes down. If less than ~5 course grades go down, then I revisit their project reports. If their report fails to demonstrate the most important learning objectives from the course, then I will keep the lower grade. If they demonstrate an understanding of the most important learning objectives, then I will adjust their score to increase their course grade. If more than 5 grades go down, then I revisit everyone’s reports. I will make a class wide grade bump in all reports.
The most important learning objectives are: understanding when and what test is appropriate, and interpreting odds ratios (from main effects and interactions)
Project: LASSO
You can take the finalized formula for LASSO and use it in
glm()In this case, use the test data to come up with predictive values (like AUC)
You can run it on the full dataset to get the coefficient estimates and other diagnostic information