Award-Winning Statistics Graduate Level Tutors
serving Spokane, WA

I am also interested in tutoring college students preparing for the GRE general test. For test preparation, I assign a decent amount of homework each week and I spend the majority of my sessions going over the questions my students answer incorrectly.

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.

Graduate-level statistics throws curators of data into the deep end — multivariate analysis, mixed-effects models, Bayesian inference — and Michael's biology master's work required him to live in that world daily. He teaches the logic behind each method so students can choose the right test for their research design, not just run code blindly. Rated 5.0 by students.

Graduate-level statistics throws curators of data into the deep end — multivariate regression, ANOVA designs, Bayesian inference — and Daniel's accounting background means he approaches these tools with an eye toward real-world application. He unpacks the logic behind each test so students can select and defend the right method for their research questions, not just run software outputs blindly.

Graduate-level statistics often demands fluency with multivariate analysis, mixed models, and experimental design — topics Dan tackled extensively during his Master's in Plant Biology and Conservation, where statistical modeling was central to his research. He breaks down the logic behind tests like ANOVA, regression diagnostics, and maximum likelihood estimation so the methodology clicks, not just the software output. Rated 5.0 by students.

Hi! I'm Alexandre, I am a Machine Learning Engineer, so I write code to make AI do all sorts of stuff everyday. I have degrees in Applied Mathematics and Computer Science so if curious about how an area of math is useful in the real world I'd be happy to give a list of examples!

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.

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.

I am highly praised by my students and supervisors. Even today I still kept the communication with many students.

Graduate-level statistics demands comfort with proofs and distributions that most intro courses barely touch — moment-generating functions, maximum likelihood estimation, and Bayesian inference all require a different kind of mathematical maturity. Liban's dual training in mathematics and economics gives him fluency in both the theoretical framework and the applied modeling that graduate programs expect. Rated 4.8 by students, he brings the rigor without losing the intuition.

Graduate-level statistics demands fluency with concepts like maximum likelihood estimation, Bayesian inference, and multivariate analysis — not just running software but understanding the theory underneath. Michelle brings a quantitative biology perspective to these methods, having applied advanced statistical frameworks to biological data throughout her science training. Rated 4.9 by students.

Graduate-level statistics lives at the intersection of theory and application — ANOVA designs, regression diagnostics, multilevel modeling, and knowing when each tool fits. Joshua's master's work in cognitive psychology at Purdue required heavy use of these methods on real experimental data, so he teaches statistical reasoning the way researchers actually use it. He's especially sharp on translating between the math and the conceptual logic behind hypothesis testing.