[another thread about choosing a statistics program]

just a little PSA

this thread is a year old, OP has already made her choice

[Who's sitting on no acceptances? Come commiserate]

When did you hear from Purdue?

[Reason to worry?]

Oh Berkeley, looks like I'll be headed east

[Stats program by tiers?]

How would places that have their statistics group housed in another department fit in? Like Princeton, Johns Hopkins, Georgia Tech?

[Evaluate my funding offer!]

UC Davis tuition and fees: http://budget.ucdavis.edu/studentfees/estimated/2013-14%20Grad%20Nonres%20Estimated.pdf

[Whose heart was broken on Valentine's day?]

Rejection by ... an actual person?

[Reason to worry?]

Hmm, it looks like in past years the acceptances have been sort of spread out. What did the guy you talked to say?

[How long are statistics waitlists usually?]

Judging from the results page, Michigan and Penn have sent out rejections.  I haven't heard from them yet, so I'm guessing I'm on a waitlist.  How many others are typically on such waitlists? How many typically are let in?

[Reason to worry?]

Also, what website did Wharton direct people to?

[Reason to worry?]

I guess it's a good sign if I wasn't rejected (by Penn) then?

[deferring for a year]

(not related to your question -- when did you get a response from Berkeley? Not biostats, right?)

[15 minute skype interview]

I was emailed to set up a 15 minute skype interview with a professor. What sort of questions are they likely to ask?

[Competitive profile for Statistics Grad School]

It's very difficult to give a general answer to this question, because every admissions committee is going to look for something slightly different. Having said that, in general, you are probably better off taking more upper-division math courses. The biggest concern of most admissions committees is that you won't be able to pass your qualifying exams if you can't do the math. Also, there is a mindset (incorrect in my opinion) that you can teach a person to do applied statistics or computational work but mathematical ability is something innate, and if you're not a strong "math person" than you'll never be able to do high-level theoretical work. Also, nearly every statistics graduate program will require you to do some sort of coursework in theoretical statistics and probability, but the applied curriculum varies greatly from school to school. Thus, if you take more applied statistics courses, you may have a bunch of applied courses on your transcript in areas that you will end up never using in grad school. (Well, pretty much every program will require a course in linear models, but otherwise there is a lot of variation.) So in general, I would definitely error on the side of more math (or theoretical statistics/probability). Analysis, measure theory, theoretical statistics, and measure-theoretic probability would all be fantastic, as would advanced linear algebra. You probably won't get much mileage from complex analysis or topology, though. Also, if you are interested in applied statistics, you should look into the possibility of taking some computer science courses. Demonstrating programming ability will probably help you more than applied statistics courses for the reasons I listed above.

As for research, I would say that it is far more important to find a project where you can make a major contribution and get a strong recommendation than it is to find a project in a specified area. You would be better off working in an area that is only tangentially related to statistics if it will result in a first-author paper and a superb recommendation than a project where you're basically just making photocopies for a superstar statistician, if that makes sense. The main thing is to demonstrate that you can do independent research, so anything you can do to provide evidence of that would be a good idea.

Let me know if you have any other questions. I don't check this board frequently, but I'll try to answer your questions when I come on here.

Wow, lots of advice here. Thanks for the help.

[Competitive profile for Statistics Grad School]

I'm not sure it's worth taking grad level measure theory/probability if you are already planning on going to grad school in Statistics. Most PhD programs assume you will take this material your first year, and even if you take it now you'll probably want to take it again to make sure the material is very fresh when you take quals, and because classes like that are pretty foundational for most programs. More math is never a bad thing, even if the areas aren't that relevant to Statistics. I'm assuming you've done real analysis and linear algebra. Functional analysis, fourier analysis, and complex analysis would all also be useful courses for Statistics. Also, definitely take some Statistics classes (probability doesn't count). I think it's important to show a genuine interest in Statistics rather that just one-dimensional mathematical aptitude.

For research, working with a statistician would be best, but either of the other options you mentioned would certainly still be valuable. Research experience is definitely an important part of the picture.

Lastly, like anything else GPA of course matters, but how much depends on the quality of your undergrad program, letters of recommendation, research, etc. Good grades are obviously desirable, but average grades can be made up for by great letters, experience, and enthusiasm.

Thanks for the response. I have taken Linear Algebra (1 quarter introduction focusing on solving problems, 1 quarter focusing on theory and proofs) and Real Analysis (2 quarters). I've also taken 2 quarters of calc-based probability and a quarter of calc-based Statistics. I'm hoping to have all or most of the core statistics classes done before I graduate too. The biggest question I had was depth in math vs. breadth in Statistics. It seems like either way is good, which is comforting to know when picking a path.

[Competitive profile for Statistics Grad School]

Math major with a stat minor is perfect.

Set theory, grad-level measure theory, and probability are worth taking. I wouldn't spend any time on Topology or Complex Analysis.

Any stat elective would be good, too I'm sure.

Applied math or stat research would be better than statistical consulting.

I don't know about typical GPA's, though, sorry. I wouldn't worry about that if I were you. Just take the right classes and research, and do your best. You're starting early, so you're in great shape.

Thanks for the reply. It's interesting that you say the Measure theory class would be useful but to not take Topology, since undergrad Topology is listed as a prereq for graduate measure theory (which is a prereq for Probability). Maybe I should look into whether that's enforced and how necessary it's really thought to be.

[Competitive profile for Statistics Grad School]

Hi, I'm a 2nd year math major (stats minor) interested in applying to Statistics grad schools down the line. The problem I'm having is in choosing how to balance math classes vs. statistics classes and schoolwork vs. research (and what types of research?)

In terms of classes, I'm probably going to take an introduction to Measure Theory next quarter. But how useful would classes like Topology, Complex Analysis, Set Theory, Grad level Measure Theory, and Grad level Probability be? The alternatives would probably be taking a couple extra statistics electives.

In terms of Research, how useful would it be to do work in Applied Math? Or doing some statistical analysis for researchers in a completely separate field? Or is it much more useful to do straight statistical research?

Lastly, what's a typical undergrad GPA for a top 10 or top 20 program? Is there much of a difference between the two levels?

(apologies if any of these questions are naive or asked too frequently. It seems like it's hard to find concrete information on what grad schools expect, and instead people always say just to "do what you're interested in.")