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The GRE is more like a filter. A high GRE Q won't get you in but a low GRE Q will disqualify you. With that said, I strongly recommend you spend some time studying and retake the GRE. It should be pretty easy for you to get 168+ since they are mainly high school math. You can also consider retaking the GRE math subject test. I am saying this because with your math background and some preparation, a 90% is very attainable for you.

I am planning on applying for statistics phd and next term I have the option to take measure theory and functional analysis. I heard these courses are especially helpful for phd coursework. However, I don't think the grades will be available when I applied. I also have the option to work as a biostatistician at a Pharma next term and I am not sure if this experience is going to boost my application. Just for the purpose of admission, which way do you think will I be better off?

The OP definitely has a chance at OSU and Iowa. However, the admissions committee are much more interested in grades in individual courses than the overall GPA. Real analysis grades are really important, and a B- in graduate real analysis from an unknown school just doesn't look good. GRE is pretty useless because it is assumed you do well on the GRE Q. That being said, the GRE score can only hurt and anything less than a near perfect GRE Q could lead to an automatic rejection.

Since you come from an unknown school, anything less than a near perfect gpa is going to be a concern. A B- in real analysis obtained from a top university like U of T or Chicago may be forgivable. However, your B- in lebesgue integration may raise admissions committee's concern about your ability to do math. With that said, it is unlikely that you will get accepted into the schools on your list except for maybe UCLA. You should either look into schools within the range of 30-50 or do a master's at a well-known Canadian school to strengthen your profile (think of U of T, Waterloo, Mcgill and UBC).

With your current GRE score, your application will probably be filtered out in the first round. As a quantitative discipline, doing well in the GRE Q does not help your application but anything less than a near perfect GRE Q is a red flag. I would try my best to improve GRE Q to at least the 90th percentile. Even with a perfect GRE Q, you may still have trouble getting into top 50. You have the bare minimum math background to apply for statistics PhD and your B's in real analysis and probability will raise admissions committee's concern of your ability to succeed in PhD level courses. In order to improve your chances, you could first apply to master's programs in statistics, which allows you to take more advanced proof-based math and statistics courses and possibly write a thesis. UBC's master's program is definitely a good choice but since it is funded, it is harder to get into. If you do well in your master's, you could probably get into schools at the level of UIUC and UF.

Your math gpa may be too low for top schools. Given your B's in linear algebra, math stat and measure-theoretic stochastic processes, the admissions committee may doubt your ability to handle PhD level courses. Also, you have very few proof based courses compared to other applicants to top schools, which doesn't help your case. With that being said, you should look into schools in the range of 25-40, and maybe add a few more safeties. I would identify schools at the level of OSU and Davis be your reasonable targets.

I am a rising senior from a top school in the US and I am planning on applying to statistics PhD. I have been doing well in almost all math/stat courses and among about 20 math/stat courses I took, I have never obtained anything less than an A-. However, my overall gpa is not that great (around 3.55 ish) because of some bad grades from other disciplines and electives. For example, I got a D+ in mathematical finance and a C- in loss model. In addition, I also obtained quite a few B's in finance courses and electives. I am wondering how these bad grades might negatively impact statistics PhD admissions. Should I address them in my SOP or they don't care about these grades at all?

I am a junior year student in one of the top 10 universities in the US and I am planning on applying to Statistics PhD for 2021 entry. I am looking for your advice to further strengthen my profile. Currently I have taken linear algebra, the undergraduate real analysis sequences and the mathematical statistics sequences. Next year, I have the option to take more upper-division math courses such as abstract algebra, complex analysis and geometry. I also have the option to take the graduate mathematical statistics sequences and possibly some other stat courses like GLM and stochastic processes. For the purpose of admission, which option looks better?