PompousPilots

I like Discrete Mathematics by Rosen as an introduction to basic concepts and to mathematical thinking in general. It's a giant textbook, but don't be intimidated. You might only want to read the first couple chapters. I'm sure your university library has it.

Yep, I second everything coffeeintotheorems said. You're in wonderful shape. Study your ass off for the general and the math GREs and nail both of them. And apply to all the top programs. And find something good to do during your year off (in addition to studying for the GREs and filling out all your applications). Maybe look for internships. Or you might be able to find a one-year Master's program or something. Good luck.

American, 3.9 undergrad GPA, 3.9 Master's GPA, 800Q, 800V, 5.5 W, research experience, etc... REJECTED!!! (It's okay, though; I'm really happy with my options.) That's a tough program to get into. I called the department today to check on my status. Yes, it's all true: they sent out acceptances a few weeks ago. (Apparently nobody who uses gradcafe got accepted.) I suppose you might be waitlisted, though, if you haven't heard from them yet. So you can keep hoping.

about three weeks ago via email. good luck to you.

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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.

You definitely want to have a very good basic understanding of LA. Beyond that, your time would be better spent learning probability than rigorously developing LA. Maybe watch the MIT open courseware video series on LA on Youtube to review everything. Just search for "MIT Linear Algebra" and watch the whole series. The lecturer is great. And, here's a pop quiz: Given some vector of data, y and some matrix of data, X Find the vector b such that the distance from y to X*b is minimized In other words find the linear combination of the columns of X that has the smallest Euclidean distance from y. You don't actually have to answer it right now. The answer is b = inv( transpose(X) * X) * transpose(X) * y So that's a basic question in Linear Regression. Did that basically make sense? You need to know how matrix arithmetic works, column spaces, etc. If that question made sense to you, then you probably understand LA well enough. But you should still review to stay sharp. Watch the MIT videos. Probability is important in quantum too. It definitely won't hurt to take an undergrad-level calc-based probability course now, then measure theory at some point, then a measure-based probability course. I think that would be better. You really want probability to become second nature - become intimately familiar with all the common distributions, etc. That just takes a lot of time and repetition.

I would recommend a probability course over a second proof-based linear algebra course for both grad stat and for the actuarial exam. You definitely use a lot of Linear Algebra in stat, but you really just have to be good at using the results of linear algebra rather than having a deep understanding of all the proofs. For example, would likely be much more useful for you to learn to use the R programming language to manipulate matrices, etc. than to take a theoretical linear algebra course. Probability, on the other hand, is absolutely essential for every aspect of Statistics, applied and pure, master's and phd. I would recommend as much probability as you can get.

No, you won't have to pay back any funding if you don't complete the phd. If you can get in as a PhD student, you should probably do it - even if you think you probably just want a master's. I know a few people who have done this. But you should probably make sure that you apply to plenty of PhD and Master's programs to have a good chance of getting accepted somewhere. Also, you've missed a lot of deadlines if you haven't already applied to some of the schools on your list.

I did a little research on some programs, so I'm going to be more specific. Most good programs seem to either require or "highly recommend" the Math GRE. So I don't think I'm going to apply to those. Of the really good schools, the ones that didn't mention the Math GRE were: Berkeley, Harvard, Duke, and UNC

White male from southeastern U.S. B.S. in Neuroscience (3 years) from a top-50 (barely) university Math minor (30 credits, GPA: 4.0) Overall GPA: 3.9 Dual-Master's in Secondary Education & Applied Statistics (3 years) from a large public university in the Midwest GPA: 3.9 (just starting my third and final year currently) GRE: 800 Q, 800 V, 5.5 W (taken once) Extracurriculars: Work: College Tutoring Centers Hobby: Web development Research: Just starting to research with a Stat professor this coming year. Weaknesses: No Math Subject GRE Just starting to research, no publications Neurosci major and Education Master's - might seem aimless Am I a competitive applicant to top Statistics PhD programs? Where might I get accepted? Where do I have no chance? Thanks!