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This may be a bit harsh but you really need to think about why you want to pursue a PhD degree. PhD is about original research, which could be much more challenging than taking courses, and it's a lot of time commitment. A strong background in mathematics is necessary but not sufficient to succeed in a statistics or biostatistics PhD program. That said, given your consistent low grades in math courses, even if you get admitted into some programs, you might have a hard time in your PhD coursework and research. For your list of schools, I think the first three are out of reach. I would mainly focus on schools ranked below 70. Since you have taken many math courses, I would suggest picking up some previous textbooks and see if you can reproduce those proofs. If you still find them very difficult, I would advise you against pursuing a PhD degree.

If you are interested in statistical genetics, generally it would be your best interests to apply for biostatistics PhD programs. Your math background is a bit thin for top schools, especially for statistics PhD programs. So it would be good if you can beef up your math background. This will make you much more competitive for top PhD programs, and also you will have an easier time when you take PhD level courses, which are very intense. If you can get strong grades in future courses you listed, I think schools in the range of 10-30 would be good targets.

You have a good math background and a strong overall profile. The schools you listed are reasonable and I think you have a good chance of getting into some of them. However, admissions to PhD are very competitive. That said, I would apply broadly and also add some safe options. I think 10-20 schools are a good target to go. You could apply to some of the top 20 schools, some midrange schools like UIUC/Davis and some safety schools in the top 50s.

FSU definitely has more research areas and it's a large department. It's just that McGill's biostatistics program is pretty small with only 8 faculty members, and it seems that it is not very well-known outside of Canada. The program itself has a very good reputation and is probably the best biostatistics program in Canada. Besides Dr. Moodie, Dr. Platt is also very well-known in the area of causal inference. McGill's program does require students to take graduate probability/statistical inference, and is more rigorous than other biostatistics programs in Canada, say UToronto, which does not require students to take advanced probability/statistical inference.

I also got into the Biostatistics PhD program at McGill last year. McGill's biostatistics department is highly reputable in Canada, especially in the area of causal inference. Dr. Robert Platt would also be a good choice if you are interested in causal inference. FSU has a decent statistics department with a much larger size, however it's not in the top tier/second tier in the US. I would think it's a tier below Columbia/Emory. If I were you I would definitely choose McGill. As for the funding, I'd say that most, if not all (bio)statistics PhD programs in Canada are fully funded. On the other hands, I heard that FSU only guarantees funding for some students.

You should directly go for PhD if your goal is a PhD. Your grades and mathematical background are very impressive. If you are from a school that is known for grade deflation, your grades in grad analysis won't hurt you, since they are hard courses and students in those classes are usually very smart. I would say that you have a very good shot at top 10s and even top 5s. Just to be on the safe side, I would also add some schools in the 20-30 range such as Penn State/Minnesota.

The major problem in your profile is your weak math background. At the minimum, you would need two semesters of real analysis and/or some other proof-based courses. However, I don't see any proof-based courses in your profile. The actuarial science program at Waterloo is intended for people who wanna become an actuary. Unfortunately, the mathematical training you obtained from the program is not enough for you to apply for statistics PhD. You would've been better off if you majored in pure math/applied math/CO. However, if you can make up the required mathematical background, you will not be at a disadvantage when it comes to admission. If you could take a few courses in real analysis/measure theory and get A's in them, then you might have a chance at some of the top 50's such as CSU/FSU/Iowa. You should also have a good shot at Waterloo. Given your math background, I wouldn't suggest you take the math GRE subject test. Only if you have had most of the pure math courses should you consider taking the test.

For master's programs in the US, real analysis won't matter that much and many people get in without having taken it. Most master's programs in the US are not very selective probably except for a few elite schools like Berkeley/Chicago/Stanford. However, statistics master's programs in Canada are much more selective because most of them are funded, and if you perform satisfactorily in the program then you are almost guaranteed to transfer into the PhD program. With that said, your lack of real analysis, low grades in a couple of statistics/math courses, along with your undergraduate institution might have made you less competitive at UBC/Toronto. Taking real analysis and obtaining good grades in them would definitely help your chances, and having a strong math background never hurts, especially if you consider a PhD in the future. However, even if you get strong grades in real analysis, UBC/Toronto are still gonna be reaches. It's just that admissions for top master's programs in Canada are very competitive. For example, UBC had 247 master's applicants in 2019 and admitted 15 of them. If you were to reapply, I would suggest also applying to schools at the level of Simon Fraser/Western/Alberta, which I think you have a good shot.

This is not the correct mindset for pursuing a PhD. If you wanna pursue a PhD, you should strive to stand out in the program and comparing yourself to others who don't do well won't help. PhD programs, especially the elite ones select the strongest candidates from all over the world, so the stake is high, and they don't care when you learnt quadratic forms. If you are motivated, you would've self-learnt it, or if you are smart, you could fill in the gap very quickly. You just have to prove you are capable of doing a PhD by proving your mathematical abilities, usually through high grades in proof-based courses such as real analysis/mathematical statistics/measure theory. Grade inflation or not, B/B+ in core math/stat courses look unimpressive to PhD admissions anyway. If you have occasional bad grades like B/B+, that might be ok, but the majority of your gr des should be A's. Those model checking stuff were known knowledge and they are quite routine checks, which can be done by master's or even undergraduates. What sets master's and PhD students apart is your ability to conduct original research. For example, at the PhD level you could propose new methods for improving the performance of GLM and prove that works through mathematical proofs.

Consistent B range grades in graduate level courses are even more concerning, given the grade inflations in master's. In this case I would say that you are probably not a good fit for PhD in quantitative disciplines like biostat/stat/ds/ml, where the math is lot deeper. Understanding asymptotic theory is crucial to conduct higher level ML research, either applied or theoretical. At the PhD's level, even applied research needs solid mathematical foundation. Asymptotic theory is one of the most fundamental elements in statistics, and many if not all research areas are based on asymptotic theory.

I think you probably have misconceptions on what PhD is about. At the PhD level, you dig deep into a particular area and conduct original research, where you would need a deep understanding of mathematical/statistical theory. If you don't have very strong mathematical skills, you are gonna have a hard time in your PhD coursework such as probability theory/inference (sorry but I don't mean to scare you), let alone making breakthrough in research. Given your B/B+ in undergraduate math/stat courses, a question you wanna answer is that if you are confident of doing well in real analysis and other proof-based courses, which are much more challenging than the math courses you have taken.

Reputation-wise, McMaster is much weaker than UBC. Waterloo has some good people but they are not super famous. For graduate mathematics programs in Canada, a general perception is that only Toronto and UBC are in tier-1.

If you are shooting for top schools, without a substantial math background it would be unlikely. For most biostat programs and lower ranked stat programs, they sometimes admit students without real analysis.

A 3.0 in writing is a bit low but it won't disqualify you. I would submit your first score with 3.5 in writing. I have never heard of any schools asking for over 80th percentile in each section. It would make sense if they require above 80th percentile in Q though. Even one of my Canadian friends scored 70s in the verbal section and he got accepted into multiple top places. In general if you score at least a 50th percentile in verbal and a 3.5 in writing, you should be fine.

I think Chicago or even schools in the top 30s are out of reach given your profile. The admissions for international students are very competitive and your consistent low grades in core math courses are a red flag. That being said, I would target much lower and it seems Northeastern/NC State are reasonable targets. I would also apply to some master's programs just to be on the safe side. If you didn't get into a good PhD program, doing a master's and then reapplying might yield better results.