Award-Winning Statistics Tutors
serving Portland, ME

Studying conducting at Juilliard means Molly lives in data — analyzing scores, interpreting patterns, and making decisions based on complex information. She brings that same analytical mindset to statistics, breaking down probability distributions, hypothesis testing, and data interpretation into logical steps. Her 5.0 client rating speaks to how clearly she communicates even the trickiest concepts.

Engineering at Dartmouth meant Rachel lived in data — running experiments, interpreting distributions, and making decisions based on probability and hypothesis testing. She brings that practical fluency to statistics tutoring, connecting concepts like standard deviation and confidence intervals to real scenarios instead of leaving them as abstract formulas.

Industrial engineering at Georgia Tech is essentially applied statistics — probability distributions, hypothesis testing, and regression analysis were daily tools throughout Ilesh's coursework. He teaches statistics by grounding abstract formulas in real data scenarios, so concepts like standard deviation and confidence intervals actually make intuitive sense.

Interpreting p-values, choosing the right hypothesis test, and knowing when a confidence interval actually tells you something useful — these are the concepts that separate students who understand statistics from those just plugging into calculators. Zachary brings a researcher's perspective from his biochemistry and biophysics training, where statistical analysis was built into every experiment. Rated 5.0 by students.

A PhD in economics at Yale means Anthony doesn't just teach statistics — he relies on it daily, from econometric modeling to designing empirical studies that require careful handling of inference, sampling, and regression. His dual undergraduate background in physics and math gives him an unusual ability to trace statistical methods back to their mathematical roots, making concepts like maximum likelihood estimation or the central limit theorem genuinely intuitive. Rated 5.0 by students.

Most students can plug numbers into a standard deviation formula — the harder part is interpreting what the result actually means in context. Joshitha approaches statistics by connecting every calculation to real-world reasoning: why a confidence interval narrows, what a p-value does and doesn't tell you. Her engineering background at Johns Hopkins means she uses statistical thinking constantly and can show students where these ideas live outside the textbook.

What separates a strong statistics student from a struggling one usually isn't computation — it's grasping why a particular test or measure fits a particular question. Tessa's mathematics coursework at Yale, paired with her history training where she regularly evaluates quantitative evidence in primary sources, gives her a sharp eye for the reasoning behind tools like confidence intervals and hypothesis tests. Rated 4.9 by students.

During her psychology degree at Penn, Brittany used statistics constantly — hypothesis testing, probability distributions, regression analysis — as core tools for understanding research. She also tutored middle schoolers in introductory statistics as a volunteer in West Philadelphia, so she's comfortable adjusting her explanations whether someone is learning mean and median or wrestling with p-values.

Probability distributions, hypothesis testing, confidence intervals — statistics asks students to think in a fundamentally different way than most math courses. Elliot spent years running statistical analyses on neural data during his PhD research, which means he can show exactly how concepts like p-values and regression actually function in practice, not just on a formula sheet.

Studying Comparative Human Development at the doctoral level means Gabriel has spent years designing studies, interpreting data sets, and running statistical analyses firsthand. He teaches statistics by grounding concepts like probability distributions, hypothesis testing, and regression in real research questions rather than abstract formulas. That practical lens makes the subject click for students who struggle with the textbook approach.

Probability distributions, hypothesis testing, and regression analysis are central to both engineering and business — and Caroline has graduate-level training in both. Her mechanical engineering M.S. from WashU built her statistical modeling skills, while her current MBA at MIT Sloan sharpens how she interprets data for real-world decisions. She teaches the reasoning behind each method so formulas stop feeling like black boxes.

Probability distributions, hypothesis testing, and regression analysis all click faster when you've actually used them to make decisions. Hari's finance background means he's applied statistical methods to real datasets — forecasting, risk analysis, variance modeling — and he teaches the logic behind each test so students can choose the right approach on their own.