Graduate-level statistics throws curveballs that intro courses never prepare you for — survival analysis, mixed-effects models, high-dimensional inference. Nina earned her master's in biostatistics at Columbia and is currently pursuing her doctorate at NYU, so she's actively immersed in the theory and application behind these methods. She also served as a teaching assistant at Columbia, giving her a sharp sense of where grad students typically get stuck.

Having earned a PhD in Statistics, Sam teaches graduate-level topics like maximum likelihood estimation, Bayesian inference, and multivariate analysis with the depth that comes from years of research-level work. He's particularly strong at bridging the gap between statistical theory and practical application — connecting proofs to the computational tools students actually use in their programs.

Graduate-level statistics demands comfort with proofs and derivations that most intro courses skip — maximum likelihood estimation, Bayesian inference, and the mathematical foundations behind common tests. Brian's Caltech background in economics and computer science gave him deep exposure to these methods in both theoretical and applied contexts, and he breaks down dense notation into intuitive steps.

As a PhD student in economics at Yale, Anthony works with graduate-level statistics constantly — maximum likelihood estimation, regression diagnostics, hypothesis testing frameworks, and Bayesian methods all show up in his research. He brings that working fluency to tutoring sessions, breaking down proofs and derivations in ways that clarify the underlying probability theory. Students tackling measure-theoretic foundations or asymptotic theory get someone who's actively immersed in this material.

Graduate-level statistics throws students into multivariate analysis, hierarchical modeling, and software-driven data work that textbooks alone rarely make clear. Tashina uses MATLAB and Python in her own doctoral research in Psychological and Brain Sciences, so she can walk through both the mathematical theory and the practical implementation side by side. Rated 4.7 by students.

Graduate-level statistics is where psychology and research methods collide, and Jessi has lived that intersection — her psychology degree from Rice and ongoing bioethics work at UPenn mean she's run regressions, interpreted ANOVA tables, and designed studies with real data. She breaks down concepts like multivariate analysis and hypothesis testing by grounding them in the research contexts where they actually matter.

Graduate-level statistics throws students into the deep end — maximum likelihood estimation, mixed-effects models, Bayesian inference — and expects fluency, not just familiarity. Elliot's PhD in Neuroscience required designing and analyzing complex experimental datasets, so he teaches these methods as tools for answering real research questions. Rated 5.0 by students.

Graduate-level statistics demands comfort with concepts like hypothesis testing, regression modeling, and ANOVA that go well beyond intro courses. Dillon's engineering background — including a master's in welding engineering technology — required heavy applied statistics work, from designing experiments to interpreting multivariate data in real research contexts. He teaches the reasoning behind each method so students can choose and defend the right analytical approach.

Graduate-level statistics often means wrestling with multivariate methods, hierarchical models, and software like SPSS, Stata, or R while simultaneously trying to apply them to a thesis or dissertation dataset. Hidefusa's doctoral work in clinical neuropsychology gave him hands-on experience designing studies, running complex analyses, and interpreting output — skills he now breaks down for other graduate students navigating their own research.

Graduate-level statistics demands fluency with proofs and derivations that introductory courses barely touch — moment-generating functions, maximum likelihood estimation, and the theory behind hypothesis testing. Victor's master's in Applied Mathematics gave him direct experience with these topics, and he brings that rigor to sessions while keeping notation and logic organized. He holds a 5.0 client rating.

Graduate-level statistics throws students into the deep end — maximum likelihood estimation, Bayesian inference, multivariate regression diagnostics — and expects fluency, not just familiarity. Evan is currently completing his own graduate work in statistics, so he's actively immersed in the theory and computation these courses demand. He also codes in Python and SQL, which means he can walk through both the mathematical proofs and the applied implementation side.

Graduate-level statistics demands comfort with proofs and distributions that undergraduate courses only sketch — maximum likelihood estimation, sufficient statistics, and the theory behind hypothesis testing. Drisana is actively completing her graduate mathematics degree, so she's immersed in the rigorous thinking these courses require and can unpack dense notation into clear reasoning.