parukia911

Also, depending on the expected pace of your program/what you feel comfortable with, it might be worth trying to get ahead on material you may be unfamiliar with. I'm definitely traveling and relaxing this summer, but I plan on working through a MS stat theory textbook (casella, Berger) and some measure theory since my analysis class didn't really cover it.

Just got my Berkeley rejection oop

Anyone feel like Berk is taking exceptionally long this year? I just want to find out I got rejected already

Update on situation: Got Math GRE test back, got 85 which was on the low range of what I was expecting Updated my list of schools to include UW stats and UCLA.

There are a few reasons why thinking like this is relatively impractical/your theory is slightly incorrect. First of all, it's not practical to think about where your overall rank is without a proper ordering on the sections (i.e., a way to weight each of the sections). Without this, it's impossible to say whether a 80 V, 90 Q is better or a 90 V, 80 Q. If you weigh all of the sections equally, then you get the Irwin-Hall distribution, that edward130603 mentioned (intuition behind this is that if your true expectation is 50th percentile, then getting 60-70 multiple times is more telling of your true rank than getting it once). Secondly, you've found an upper bound to what percentile your score is. The probability that you calculated is the probability that someone beats you on all 3 categories (which would be ranked higher than you regardless of how the sections are weighted). You can go the other way and find the lower bound on your rank (0.69*0.58*0.59 = 24th percentile). Thus, what we show is that your true overall percentile is somewhere between 24th and 95th - seemingly not very useful. Feels like the safest way to sort of "unbiased"-ly determine what your rank is is an average of your percentiles. Once again, thinking about this kind of stuff isn't really that important/not what the admissions committee would likely be thinking about, but taking an average is how I would do it.

For reference, my other 2 letters are from another statistics professor and a quantitative finance professor.

For one of my letters of recommendations, I was conflicted between two professors and I'm having trouble deciding who to get a letter from. One is from a quantitative marketing professor that I'm fairly close to/done some work outside of class with/have kept in contact over the past few years, pretty well known in the field (~9k citations), and his course is cross listed as a statistics course. However, although he is well known in quantitative marketing, he doesn't have as much pull in statistics. The other professor is a slightly younger professor (~4k citations) that I haven't really talked to since I took his class, but I took a high level graduate statistics course with him so a letter from himself may have more weight. In terms of applying to top programs (thinking Stanford, Berk, etc), which letter would be more maximizing for my chances?

Summa cum laude was a hard cut off, so I guess it's not as impressive. I'm pretty location bound imo, so if this doesn't work out I might postpone grad school and try to work at a research institute and get more experience in that avenue. We'll see if things change.

Sorry, I actually did not go to Stanford, didn't mean to imply that I did. Went to another top 10 US university (not in the bay area) but would like to be in the bay area.

Hi guys, was planning on applying for a Stats PhD this coming fall, but am a little worried because I am a little short on the research experience/personal projects part. I was had a very quantitatively focused undergraduate course load, and math is probably one of my fortes. Ideally I want to stay in the SF/bay area. What do you think I should do to increase my chances? Undergrad Institution: Top 10 US Majors: Mathematics, Economics Concentrations: Statistics, Finance GPA: 3.88/4.00 Type of Student: Domestic Male GRE General Test: Q: 170 V: 168 W: 4.5 GRE Subject Test in Mathematics: M: taking in October, but expect 85-95% range Programs Applying: Statistics Research Experience: None Awards/Honors/Recognitions: Putnam Competition school winner - top 200 nationally, graduated summa cum laude Pertinent Activities or Jobs: TAed for 5 different courses (1 compsci, 1 statistics, 3 finance), all quantitative. Worked at a quantitative trading firm with work on quant research macro projects. Interned as a data scientist at a few marketing research firms, applying models to transaction data. Letters of Recommendation: 1 stat/marketing professor who is very prominent in his field (helped me get my data science internships), 1 finance professor (quantitative finance focus, took 2 of his classes inc financial engineering and TAed 2 of his classes), 1 stat professor (senior stat professor, ex dean of admissions for the school, teaches 1st year PhD stat class which I took as a sophomore) Math/Statistics Grades: Calc IV (A+), Real Analysis 1/2 (A, A), Algebra (A+, A+), Complex Analysis (A+), Statistical Methodology (A+), Bayesian Statistics (A), Modern Regression (B+), Financial Derivatives (A), Financial Engineering (A-) Coding Skills: Python, SQL - mostly self taught/learned on the job, but I have done a lot of self studying on algorithms and have a few small software projects/coding competition awards Applying to Where: Stanford, Berk, UC Davis Concerned primarily about my lack of research experience and long term critical thinking projects. I really want to emphasize my math skills, and I think I'll get pretty solid recommendations from pretty senior professors. Is there anything I can do in the next few months to increase my chances?