Award-Winning Statistics Graduate Level Tutors
serving Rockford, IL

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 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 throws students into maximum likelihood estimation, Bayesian inference, and multivariate analysis — territory where intuition from introductory courses often breaks down. Irene holds a Ph.D. in Mathematics and Computer Science, which means she can unpack the measure-theoretic foundations behind concepts like convergence in distribution or sufficiency. She's particularly effective at bridging the gap between abstract proofs and applied problem sets.

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.

I am a Molecular Engineering major at the University of Chicago, I am currently taking time off to focus on other aspects of my career but I don't want to stop tutoring outside college campus!. I am a child of immigrants and have spent my life tutoring my siblings and younger students, and I loved working with them! See y'all in class!

Graduate-level statistics demands comfort with concepts like maximum likelihood estimation, ANOVA designs, and regression diagnostics that go well beyond introductory coursework. Sasha's training in both biology and science education gives her a practical lens for these methods — she connects statistical theory to real research applications, which is especially useful for grad students analyzing their own data.

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.

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 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.

Duncan's master's degree in statistics makes him a natural fit for graduate-level coursework in regression analysis, hypothesis testing, ANOVA, and Bayesian methods. He approaches each topic by connecting the mathematical theory to the practical decisions students need to make — choosing the right model, interpreting output, and defending assumptions. His 5.0 client rating speaks to how clearly he breaks down even the most notation-heavy material.

Graduate-level statistics demands fluency with concepts like maximum likelihood estimation, hypothesis testing frameworks, and regression diagnostics — all of which Shoaib uses regularly in his economics research at Rutgers. His master's coursework involved heavy econometric modeling, so he can unpack the intuition behind proofs and derivations that textbooks often gloss over.

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.

Graduate-level statistics demands fluency with techniques like regression modeling, ANOVA, and hypothesis testing frameworks that go well beyond introductory coursework. Scott applies these methods actively in his doctoral research at NYU, where he analyzes complex datasets on human development — so he can walk through both the theory and the practical decision-making behind choosing the right analysis for a given research question.

Graduate-level statistics demands fluency with topics like maximum likelihood estimation, multivariate distributions, and regression diagnostics that go well beyond introductory coursework. Dana holds a degree in statistics and is pursuing PhD-level economics research involving econometrics, so she's actively working with these methods. She unpacks the mathematical theory behind statistical procedures while keeping the applied interpretation clear.

Graduate-level statistics demands comfort with concepts like maximum likelihood estimation, Bayesian inference, and hypothesis testing frameworks that go well beyond intro courses. Abhi's M.S. in Data Science from UIUC and current PhD work at NYU mean he uses these tools daily in research — he teaches the theory alongside the practical intuition for when and why each method applies.

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.

Seven years of experimental psychology research means Anna doesn't just teach graduate statistics — she uses it daily, from designing multivariate models to running hierarchical regressions and interpreting interaction effects. Her PhD work at Saint Louis University required mastering advanced techniques like structural equation modeling and mediation analysis, so she teaches these methods with the fluency of someone who actually applies them. Rated 4.9 by students.