Award-Winning Statistics Graduate Level Tutors
serving Bremerton, WA

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.

My tutoring approach is one of coaching, mentoring, and helping the student understand exactly what level and detail of help that they need to get ahead. Combining the personal discipline learned during 20 years as a Naval Officer with the unique expertise learned in one-on-one communication with peers and sailors as well as my five children, I can help you or your child succeed. A Bachelor's in Electrical Engineering, and a Math Minor help qualify me for tutoring in many subjects. My individualized tutoring methods also raised my step-son's Physics grade from a C to an A in one semester. I specialize in mathematics (basic arithmetic through calculus) but can help in many other subjects. Learning should be fun, but should also, at times, bring "Instant" results. I work to keep positive encouragement in every lesson, tutoring, or test preparation session. I speak fluent Spanish!

Graduate-level statistics moves quickly from probability theory into regression modeling, hypothesis testing frameworks, and ANOVA designs that require both mathematical rigor and software fluency. Juan is completing a statistics degree at the University of Florida alongside his engineering program, so he's immersed in these methods right now — from Bayesian inference to experimental design. His 4.9 rating speaks to how clearly he communicates dense material.

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.

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!

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 can feel like a different language — survival analysis, multivariate regression, Bayesian inference — especially for students outside traditional math backgrounds. Kimberly runs these methods daily in her Columbia MPH program, where biostatistics is central to public health research. She breaks down the logic behind each technique so students can apply it confidently in their own coursework and thesis work.

Graduate-level statistics throws curveballs that intro courses never touch — multivariate regression, hierarchical modeling, interaction effects in complex datasets. As a psychology PhD student who runs her own research analyses in SPSS, Kate teaches these methods through real study designs rather than abstract formulas, making output interpretation second nature.

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 lives and dies in the details — knowing when to apply a two-way ANOVA versus a mixed-effects model, or interpreting interaction terms in a multivariate regression. William's MBA training grounded him in applied statistical methods, from hypothesis testing and confidence intervals to the kind of real-world data analysis that thesis committees actually scrutinize. He breaks down output from SPSS or Excel so students understand what every p-value and coefficient truly means.

Graduate-level statistics demands comfort with mathematical proofs and distribution theory that undergraduate courses barely touch — moment-generating functions, maximum likelihood estimation, Bayesian inference. Sabry's Ph.D. training in Chemical and Biomolecular Engineering required rigorous statistical modeling for experimental data, so he approaches these topics as tools with real stakes, not just textbook exercises. He's particularly effective at connecting abstract theory to applied research contexts.

I am currently finishing my thesis. For the past two years I was an adjunct instructor at The City College of New York, teaching statistics and introductory neuroscience, where I learned the importance of communicating complicated concepts clearly at an individualized level. All of my classes performed above average, and I discovered how satisfying it is to help people understand difficult ideas. I've found that by creating a good rapport with my students I am able to more effectively impart difficult concepts to them while causing them less stress. My passion is people, which first led me to study psychology, leading to my work in statistics, and later into teaching.