Award-Winning Statistics Tutors
serving Albuquerque, NM

As a Statistics major at UNM with plans for graduate study, Matthew lives in the world of regression models, hypothesis tests, and probability theory every day. He unpacks tricky ideas like p-values and sampling distributions by tying them to real data scenarios, so the logic behind each calculation is as clear as the arithmetic itself. Rated 4.9 by students.

I am a dedicated, highly motivated individual with a passion for enhancing the learning experiences of others. I have taught as a classroom teacher and as a volunteer at state and national parks for the past few years. I focus particularly on highlighting connections between various subject matter to individuals' everyday lives. After I graduated with my Masters degree from Johns Hopkins University in 2014, I moved to New Mexico to pursue a career in environmental conservation. I currently work as a supervisor for an AmeriCorps program that provides opportunities for young adults to gain skills working in the field of conservation while also receiving training for personal and professional development. I aspire to become a ranger with the National Park Service and a freelance writer.

I am still attending the University of New Mexico to get a Bachelor of Science in Physics. My hopes are that I can get into a good graduate program for Physics or Astrophysics so that I can become a professor. I tutor Physics, Chemistry, Geometry, Algebra, and Calculus. My favorite subjects to tutor are Physics, and Geometry. Physics and geometry are my favorite subjects to tutor since I am very passionate about physics and believe it can be taught at every level of education. Why geometry is a favorite might have seemed weird at first, but Einstein himself believed geometry is a branch of physics not math. This is why physics is for all ages. I focus on improving the students understanding of the subject. Then I focus on teaching them what critical, creative, and/or logical thinking they need to apply to the subject. For example I will start by ensuring the student has a vivid understanding of how gravity and acceleration are related before teaching them how a pendulum works or how to solve for the path of a rocket. I believe that by knowing how to critically think for a subject allows you to apply what you have learned for life, this also makes studying for finals much easier in the future. Outside of school I enjoy listening to music, working with computers in general and learning other languages. I enjoy improving who I am by meditating, reading, and exercising every day.

Studying Statistics at NYU means Dennis doesn't just teach probability distributions and hypothesis testing from a textbook — he's actively working through these concepts in his own coursework. He's especially sharp at translating the notation-heavy language of statistics into plain English, which makes topics like confidence intervals and regression analysis far less intimidating.

Between her sociology research in undergrad and her MBA coursework, Krupa has run enough regressions, hypothesis tests, and probability models to know exactly where students get tripped up. She tackles the conceptual side — why you'd choose a t-test over a z-test, what a p-value actually means — so the formulas stop feeling arbitrary. Her 4.9 rating speaks to how clearly she communicates these ideas.

Running regression analyses, interpreting p-values, and choosing between parametric and nonparametric tests are things Martha does routinely in her social psychology research at Michigan. That hands-on fluency means she can explain not just how to compute a standard deviation or set up a hypothesis test, but why each step matters and what the results actually tell you. Rated 5.0 by students.

Most students memorize the formulas for z-scores or standard deviation without ever seeing where they come from — Kathleen's math degree from Washington University means she can derive them from scratch and explain each piece along the way. She treats every statistics concept as an extension of the algebra and calculus her students already know, which makes new material feel like a logical next step rather than a disconnected set of rules.

The hardest part of statistics for most students isn't the math — it's interpreting what a p-value or confidence interval actually means in context. Vy's training in cognitive studies at Vanderbilt, which is heavily research-methods driven, means she's spent real time designing studies and running analyses. She unpacks concepts like distributions, hypothesis testing, and regression by tying them to concrete research questions.

Probability distributions, hypothesis testing, and confidence intervals make a lot more sense when you've actually used them to analyze real data. Emma applied statistical methods throughout her biology research at Duke — including fieldwork on Hawaiian monk seals — so she teaches stats as a practical tool rather than an abstract formula sheet. Rated 4.9 by students.

An economics degree means Maggie didn't just study statistics in a textbook — she applied distributions, hypothesis testing, and regression analysis to real datasets. She teaches students to interpret what a p-value actually tells them and how to choose the right test for a given scenario, building the kind of statistical intuition that carries through exams and research projects alike.

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.

Most students walk into statistics expecting another math class and get blindsided by the emphasis on interpretation — explaining what a confidence interval actually means, or why correlation isn't causation. Amber tackles that interpretive layer head-on, teaching students to read context before crunching numbers. Her theater background gives her a knack for making abstract concepts like probability distributions feel concrete and memorable.

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.

Probability distributions, hypothesis testing, and regression analysis all clicked for Crony during his neuroscience research at Brown, where he used statistics daily to interpret experimental data. He brings that applied perspective to tutoring sessions, showing students how each concept works in practice — not just on a problem set.

Studying cognitive science at Rice required Adam to run experiments, interpret data sets, and draw conclusions from statistical tests — so he teaches statistics as a practical reasoning tool, not just a math course. Whether it's regression analysis, p-values, or probability distributions, he connects each topic to real research questions that make the material intuitive.