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.

Working as a research assistant in Yale's cognitive neuroscience lab meant Emily ran statistical analyses regularly — hypothesis testing, probability distributions, and interpreting p-values were part of her daily routine. That hands-on experience makes her especially effective at explaining why a statistical method works, not just how to execute it on a calculator.

A Quantitative Methods minor at Vanderbilt means Heather spent semesters immersed in regression analysis, hypothesis testing, and probability — the exact material that trips up most statistics students. She teaches the reasoning behind each test so students can choose the right method on their own, not just follow a decision flowchart. Her 4.9 rating speaks to that approach.

Yi's graduate training in research and experimental psychology required heavy use of statistical methods — from hypothesis testing and ANOVA to regression modeling and interpreting p-values in published studies. That hands-on experience with real data analysis means she teaches statistics as a tool for answering questions, not just a set of formulas to memorize.

Kathy's economics degree from Duke meant living inside datasets — regression analysis, probability distributions, hypothesis testing, and statistical inference were daily tools, not abstract concepts. She breaks down problems by connecting the math to what the numbers actually represent, which makes interpreting results feel intuitive rather than formulaic.

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.

Understanding statistics means learning to ask the right questions about data before running any test: Is the sample random? What's the shape of the distribution? Could this result have happened by chance? Ethan's policy background gave him years of practice interrogating datasets and translating statistical output into plain-language conclusions, a skill he now brings directly to topics like hypothesis testing and regression analysis.

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.

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.

A political science degree from Brown meant Lyall spent years interpreting polling data, regression models, and probability distributions in real research contexts. He brings that applied lens to statistics tutoring, connecting concepts like standard deviation and confidence intervals to situations where the numbers actually matter. Students get someone who treats stats as a tool for making arguments, not just a formula sheet to memorize.

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.

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.

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.