FIRST QUARTER FINALS

Question

Be honest...

lepium · Answer

I figure about 4 - 5 hours/course x 5 courses. I voted 20 as a base case, but could escalate to 25 - 30 ish if I freak out.

Thanks. L.

pelihu · Answer

Well, we actually completed a quarter about 5 weeks ago, and we're about 2 weeks away from our next set of exams. They actually have 4 quarters here during the school year, as opposed to 3 quarters and a summer quarter that many schools have (that's what we had at UCLA when I was an undergrad).

I'll say I put in 10-20 hours of studying for exams. I don't really like to study for exams, so the system here works pretty well for me. We have an integrated system with regular prep, learning team and in-class discussion that forces everyone to prepare and keep up. I don't think you can effectively cram for exams; which is fine with me because it's not my thing.

I'll say I spent 10-20 hours preparing and things went pretty well. I'm not going to spend more than that this time around.

aaudetat · Answer

I've already taken two sets of files - one after the summer term at the end of August and another toward the end of October. Peli and I hare on similar schedules.

I barely studied at all in August because I was on top of the work and felt comfortable with the classes. But then kinda quit doing homework (and got hit by a car) for term 1, so had to make up for it in the end. It all worked out, though! I would guess I was at about 20 hours.

OasisNYK · Answer

Yeah, what pelihu said pretty much sums it up here at Darden.

GMATT73 · Answer

I voted for 40~60 hours, but something about this morning's announcement (all finals are cumulative) sent a shiver up my spine. I thought economical forecasting and integrals were a thing of the past...NOT!

GMATT73 · Answer

Anybody know how to interpret "adjusted R squared" on a regression/ANOVA summary report?

aaudetat · Answer

Well, let's see what I remember from last term's stats class! Without checking my notes, here's what I recall:

In a simple linear regression, R-squared tells you how much of your data is explained by your model. A high r-squared is important if you're looking backward, trying to explain what has been happening in a certain relationship.

However, due to the way R-squared is derived, it gets synthetically higher for every additional independent variable you add to your model. The adjusted R-squared has a slightly different derivation to ensure it's not affected by those additional variables. 

Again, a higher adjusted r-squared tells you to what extent your model explains your data set.

OasisNYK · Answer

We also learned it is not best to always rely on R^2....

You definitely need to take a look at the Tstats, R^2 should just be a confirmation of the correlation after checking those but it is not the be all and end all.

FIRST QUARTER FINALS

How many total hours do you expect to invest in preparing for final exams over the next 2~3 weeks?

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