Award-Winning Statistics Graduate Level Tutors
serving Fort Collins, CO

Graduate-level statistics in medical and biomedical research relies heavily on survival analysis, logistic regression, and interpreting multivariate models — all tools Elise used extensively through her M.D. training at Creighton. She breaks down the reasoning behind test selection (why a Cox model instead of a chi-square, for instance) so the methodology clicks rather than just the formulas.

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.

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.

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 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.

Holding a Master's in Statistics, Adam digs into the graduate-level material that trips students up most — maximum likelihood estimation, Bayesian inference, multivariate distributions, and the theoretical underpinnings of hypothesis testing. He's taught and tutored across university settings, so he knows how to bridge the gap between abstract proofs and applied problem sets. Rated 4.9 by students.

Graduate-level statistics demands comfort with mathematical proofs and distributional theory that go far beyond introductory courses — maximum likelihood estimation, Bayesian inference, and multivariate analysis all require real mathematical maturity. Yasaman's Ph.D. research in Electrical and Computer Engineering at McMaster has her working with advanced statistical methods regularly, so she can unpack both the theory and the computational implementation behind each technique.

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 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 in the health sciences — biostatistics, survival analysis, logistic regression — requires more than formula memorization; it demands understanding which test fits which study design and why. Julia's Doctor of Science in Pharmacy means she's applied these methods firsthand in clinical research contexts, interpreting p-values and confidence intervals with real patient data on the line. She teaches students to think like researchers, connecting statistical output back to the questions driving the analysis.

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.

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.