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@kingduckthe higher the better is the general rule but in that range you're sitting fine. The GRE is more of a weed out tool. If you will improve just by 1 point, don't spend the money, it's an expensive test and a 165 vs 166 will be virtually meaningless in the eyes of the adcoms. If it were a 159 to 160, or a 159 to 161, I get it, but not in the mid 160s-- you're fine.

Plane answers is a good book, I've used that one. Primer to Linear Models (Monahan) is good, as is Linear Models in Statistics (Rencher). I also really like the discussion in KNNL, personally.

The most "typical" required coursework seems to be:
2 semesters of Casella & Berger mathematical statistics  2 semesters of applied statistics (based on the book "Applied Linear Statistics" by Kutner et al.) 1 semester of statistical computing 1 or 2 semesters of measure theoretic probability 1 semester of linear models theory 1 or 2 semesters of advanced statistical inference Some elite PhD programs like Stanford and UPenn Wharton skip the first two sequences above because the students they admit are fairly advanced already. 
Anyway: my opinion is that the typical first-year courses are fine for the most part, though they certainly should be updated to incorporate current research topics. If an entering student has not already had much exposure to statistics at the graduate level, then I think it's fine to teach the topics like linear regression, ANOVA, GLM/categorical data analysis, and theory of sufficient statistics, point estimation, hypothesis testing, etc. in detail... though I definitely agree that some of their curricula should be updated. For example, at my PhD program, an entire semester was devoted to different ANOVA/ANCOVA models, including things like split plot design, etc. That seemed a bit excessive to me -- usually, you only need to go over a couple of ANOVA models in detail to get the general gist. So if I were on the PhD curriculum committee, I would probably "modernize" the applied stats sequence (and the statistical computing class) to spend less time on design of experiments and include more modern topics.
Additionally, the advanced statistical inference courses (i.e. the theoretical statistics course(s) you take in the second or third year) at many programs do seem to focus on some topics that are dated. For example, at some schools, you learn to cross every "t" and dot every "i" for "classical" topics like UMP tests, UMVUE, equivariance, likelihood principle, etc., which isn't necessarily helpful for modern statistics research. 
I would probably repurpose the advanced statistical inference classes to cover more 'modern' statistical theory like multiple testing/knock-offs, RKHS and nonparametric regression, convex/nonconvex optimization for high-dimensional regression, graphical models, etc. 

I often find that the best way to learn a new field/subject is to watch video lectures, read review articles and read select chapters from textbooks. So when I wanted to learn about variational inference, the first thing I did was watch a few video tutorials by David Blei and Tamara Broderick. After establishing this "baseline," I kind of just pick up on things as I go -- i.e. I just read the papers and try to figure out what the authors are doing as I go. This gets easier to do as you gain more experience and as you read more papers (in the beginning, I might annotate the papers a lot more). 
Realistically, when you are doing research, you won't know (or need to know) *everything* there is to know about, say, convex or nonconvex optimization. But you can pick up what it is you need as you go, and if you encounter something you're not familiar with, you get better at knowing WHERE to look and fill in those gaps. 

I'm sorry, I just can't let this stand unchallenged. It is complete nonsense to say that GLMs have had little impact on data science. Talk to any practicing data scientist and they'll tell you that a lot of the models actually being used in practice are relatively simple regression models. And survey sampling? That's a special case of weighting, which is heavily used in machine learning in the case of rare events (and also to increase algorithmic fairness). 
If all you're interested in doing is creating algorithms that do something faster or more accurately, sure, maybe you don't need a ton of statistical training. But, if that's all you're interested in doing, you're not really interested in being a statistician! Statisticians seek to develop tools for better data analysis, which includes quantifying uncertainty, carrying out inference, and improving model interpretability. It's impossible to do that without a solid grounding in the kind of old-fashioned statistics you look down your nose at.
Lastly, your conclusion that it is better to attend EECS/ORFE programs like MIT/Princeton because graduates from these programs have obtained positions in top stat departments is flawed. Top departments are often looking to find the smartest people they can hire, on the logic that they'd rather have a rock star who does something a little bit outside the norm than an "excellent-but-not-exceptional" faculty member who fits easily within the field. Sometimes, those brilliant people are in non-stat programs, but they're being hired because of their brains not because of their training. Indeed, if they were equally brilliant but had been trained in a stat department, they might be even more attractive candidates! Most people in EECS/ORFE programs will end up in those disciplines; entering such a program with the goal of entering a different field upon graduating is taking a huge gamble that you'll be so exceptional that hiring committees will overlook the fact that your research and training is unorthodox.
OK, rant over.

I'll just add - if I were OP, I'd ignore most of what's being said in these past couple of posts. There's certainly some truth to it, but also some reaches... I'm not going to engage beyond that.

An important omission on the above suggestions is MIT. MIT does not have a statistics department but it is possible to study statistics there through EECS, math, OR, or CSE track. The matter fact is that when you talk about the "hot areas" such as statistical/machine learning, inference algorithms, high dimensional statistics, MIT is as strong as (or is probably stronger than) Stanford or UCBerkeley. A list of "emerging superstars" there: Elchanan Mossel, Sasha Rakhlin, Philippe Rigollet, Guy Bresler, David Gamarnik, Ankur Moitra, and many many more. 
I'm surprised that this forum doesn't even mention MIT when it has one of the most powerful stats communities there. Not to mention that when you get into MIT, you virtually get into Harvard because you can have supervisors/collaborators at both schools and take any courses, joins any reading groups you like at both places. 

Your profile looks very strong. If you attended a top 3 university, I don't think it matters whether there is "grade inflation" or not. You have also taken a lot of graduate-level courses in both math and stat, and grades in grad school tend to be inflated anyway. Your research experience in evolutionary genetics is also a plus.
I think you should apply to mainly top 15 stats programs (according to the USNWR rankings). I'm sure you will get into several of them.

First of all, you don't have to be anxious or stressed out. I read your post. You seem to be concerned about a lot of things. I am not able to answer many of your very technical questions. But trust me there is no need to be worried about any of these things. Try to relax and enjoy your senior year! Do not let the pressure get to you. Have a social life, have fun, and talk to your friends (especially when you feel low or are stressed out).
Here is another fact that may soothe you: most students applying to masters or phd programs in statistics don't have nearly as much experience as you do. You have done great work and should be proud of your achievements! Master programs are often less selective than PhD programs. Your profile has far exceeded requirements of many programs out there. I'm sure that you will get into some of the places on your list. 
Finally, good luck!

I agree with @StatsG0d. Frankly, as long as your computer is fast enough to run some code for research and big problem sets, you're fine. For some of the heavier research you'll be doing, you'll be sending it to the cluster, at least in my department. Get a laptop you'd like. I got a MacBook Pro with the student discounts and upgraded it. I love it, and have always been an Apple user so I prefer the macs for many reasons. 
Takeaway: Get something you want that's light, fast (enough) and won't cripple you financially. As long as you don't mistreat it or download some seedy parts of the internet, you'll be okay.

Usually I would say a score that is solidly within the 80's (as a %ile), or above, will always improve your chances. Unfortunately, at the top schools, most folks will have very solid scores, but I think that a score in the mid 80%ile or above will certainly boost your already solid profile!

Usually I would say a score that is solidly within the 80's (as a %ile), or above, will always improve your chances. Unfortunately, at the top schools, most folks will have very solid scores, but I think that a score in the mid 80%ile or above will certainly boost your already solid profile!

You have a very solid profile, I think your list is fine. If your MGRE score is good (worth taking with your profile imo), you could even aim for the tippy top departments.

Your profile is not extremely strong but it is good considering you are applying to only UK masters. The MASt is a good preparation for PhD and is generally well known in academia. A majority of the applicants will be admitted if I recall correctly. But I'm not sure it is a good option for industry biostatistics.  

Can't agree with this more. That being said, there are very good cost of living calculators. For instance, the south is incredibly cheap compared to the north east and west coast. A stipend of $1800 - 2500 can get you a very nice apartment (without roommates) and gives you money for things you enjoy down south, while in other places, that can barely cover housing. Just another thing to think about, while you're looking into the matter.

Yup, I agree. You're in some great programs, no need to even consider an unfunded offer, even from a school like UNC. Pick a program you like with research areas you like and you'll be happy.

I think also a school like South Carolina would be worth a look. if you have questions about it, feel free to reach out. I think it fits into your range, and fits your southern disposition. Virginia tech also may be worthwhile. Perhaps George Mason as well. Or, some of the UNC- schools are worth a shot, such as UNC-G, UNC-W, etc.

I think also a school like South Carolina would be worth a look. if you have questions about it, feel free to reach out. I think it fits into your range, and fits your southern disposition. Virginia tech also may be worthwhile. Perhaps George Mason as well. Or, some of the UNC- schools are worth a shot, such as UNC-G, UNC-W, etc.

I respectively disagree. I got into a handful of top masters programs (5/6) and only into 6/14 ph.d. programs. I'd be willing to argue that masters programs are far less competitive. All of my 6 M.S. applications were to top 15 schools, 3 of which were top top tier (Yale, NYU, Columbia, etc.)

I wouldn't exactly say this is the case. You get paid a living wage to work very hard and grapple with complex ideas. Reading wiki, while sometimes is a supplement, is certainly not the main activity of a Ph.D. candidate in Statistics. 

The rollercoaster is terrible, but it's not even March. Mid-March is when decisions really roll out. Initial waves have just been sent out or are being sent out now. Most schools don't send out other waves of acceptances until after they here from their first wave, that way they don't send out too many. 
You guys are fine. The waiting game sucks- but it happens year after year. Develop hobbies and find ways to keep yourself out of your email. That's what I did!
Best of luck.

That being said, it certainly doesn't hurt to visit campus if you want to do that. No need to arrange something with the department (if you haven't been accepted yet), and check out the building and part of campus that they're in. 

The rollercoaster is terrible, but it's not even March. Mid-March is when decisions really roll out. Initial waves have just been sent out or are being sent out now. Most schools don't send out other waves of acceptances until after they here from their first wave, that way they don't send out too many. 
You guys are fine. The waiting game sucks- but it happens year after year. Develop hobbies and find ways to keep yourself out of your email. That's what I did!
Best of luck.

The rollercoaster is terrible, but it's not even March. Mid-March is when decisions really roll out. Initial waves have just been sent out or are being sent out now. Most schools don't send out other waves of acceptances until after they here from their first wave, that way they don't send out too many. 
You guys are fine. The waiting game sucks- but it happens year after year. Develop hobbies and find ways to keep yourself out of your email. That's what I did!
Best of luck.

The rollercoaster is terrible, but it's not even March. Mid-March is when decisions really roll out. Initial waves have just been sent out or are being sent out now. Most schools don't send out other waves of acceptances until after they here from their first wave, that way they don't send out too many. 
You guys are fine. The waiting game sucks- but it happens year after year. Develop hobbies and find ways to keep yourself out of your email. That's what I did!
Best of luck.