Award-Winning Statistics Graduate Level Tutors
serving Aurora, CO

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.

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 more than plugging data into software — it requires understanding why you'd choose a Cox regression over a logistic model, or when maximum likelihood estimation breaks down. Naushaba's Master's in Epidemiology means she learned these methods by applying them to real research questions, from survival analysis to multivariate modeling. She teaches the reasoning behind each technique so students can defend their statistical choices in dissertations and publications.

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.

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 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 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 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 demands more than running tests — it requires understanding why you'd choose a mixed-effects model over a fixed-effects one, or when bootstrapping outperforms parametric assumptions. Snipta's computer science and cognitive science training at UT Dallas, plus hands-on research at the National Institutes of Health, means she's applied techniques like Bayesian inference and multivariate regression to real datasets, not just textbook exercises.

Graduate-level statistics becomes far more intuitive when the examples come from real research — and Nicholas has applied multivariate analysis, mixed models, and hypothesis testing frameworks extensively throughout his genetics PhD work. He breaks down concepts like maximum likelihood estimation and ANOVA designs by tying them to the kinds of messy, real-world datasets students actually encounter in their own programs. Rated 4.8 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 demands fluency with techniques like multiple regression, ANOVA, and non-parametric methods — often learned under pressure in programs that assume prior comfort with the math. Lindsay is completing her Ph.D. in Developmental and Educational Psychology at Boston College, where she applies these methods to her own research, so she teaches them as practical tools rather than abstract formulas.