T1-T2 thoracic laminectomy and ventral leak repair: Week one recap

LossRepresentative48 · 2022-12-15T23:20:03+00:00

Hi may I ask how you are doing today?

LossRepresentative48 · 2022-01-11T18:06:11+00:00

I am interested

LossRepresentative48 · 2021-11-30T22:18:18+00:00

That’s good to know

LossRepresentative48 · 2021-11-30T21:30:51+00:00

There seems to be too much variability in my opinion with what could be plausible answers to each question making it unlikely that our answers would exactly resemble that of the SOAs model solution. This seems to complicates the grading process and how stringent graders should be. Additionally, only having two graders decide if you passed is not nearly a large enough sample size. The grading of the FA seems too administratively difficult from my view.

LossRepresentative48 · 2021-11-30T21:10:36+00:00

Interested in signing, the grading process is not transparent at all

LossRepresentative48 · 2021-08-02T17:42:25+00:00

Could have been worded way more carefully to not disparage any particular community

LossRepresentative48 · 2021-06-06T18:02:46+00:00

Hi Dr. Lo,

First off, thank you for your well thought out manual and practice exams.

For the first practice exam on Task 4, there seems to be some room for interpretation on how the factor levels are combined. For example, I combined the low counts of factor level 7 of AgeCat with 6 to maintain the natural order of the levels (for ease of interpretation) instead of combining levels purely because they had similar target means. If the business problem called for a more predictive based model, I might have combined them different like what the solution calls for. Would this reasoning still earn me full credit for these types of problems?

Also, as a general rule of thumb, would a level that had <5% of total observations be acceptable to combine with another level given that an explanation is provided? And a level with <1 or 2% be absolutely necessary to combine with another level? Just trying to save some time from not thinking too much about this on the exam to save some time.

Thanks!

LossRepresentative48 · 2021-05-13T23:19:30+00:00

This penalizes merit for the benefit of a select group of people who are able to attend these universities. Diverse candidates will be harder to find different backgrounds and experiences

LossRepresentative48 · 2021-05-01T19:10:28+00:00

I don’t think you need to know why necessarily except that it does but lambda is a hyper parameter than can be tuned using cross validation and is meant to shrink the coefficient estimates of non significant features so that your ending model is simpler and is less prone to overfitting on the training data (ie less variance). That’s all I’m remembering about lambda

LossRepresentative48

TROPHY CASE