Award-Winning Statistics Graduate Level Tutors
serving Modesto, CA

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 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.

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 trips up many students at the transition from descriptive methods to inferential reasoning — hypothesis testing, regression analysis, ANOVA, and the assumptions underlying each technique. Jason teaches these concepts at a technical college in Pittsburgh, which means he's constantly translating statistical theory into applied, real-world problem solving. His math and education background lets him break down dense material into steps that actually make sense.

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.

Victor earned his B.S. in Economics with a statistics concentration from Purdue, where he tackled regression analysis, hypothesis testing, and probability distributions as core coursework. That quantitative foundation, combined with his time as a Supplemental Instruction Leader, means he can unpack dense concepts like multivariate analysis or ANOVA in ways that actually click. 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.

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.

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.

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 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 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 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.