Award-Winning Statistics Graduate Level Tutors
serving Springfield, MO

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 more than plugging data into software — it requires understanding why a likelihood ratio test applies in one scenario and a Wald test in another. Mayuri's PhD in Physics meant designing experiments and running advanced statistical analyses firsthand, from Bayesian inference to multivariate regression. She breaks down the mathematical derivations behind each method so the theory clicks alongside the application.

Graduate-level statistics pushes well beyond t-tests into maximum likelihood estimation, Bayesian inference, and multivariate regression diagnostics — territory where intuition from introductory courses often breaks down. Kathleen's experience across biostatistics and bioinformatics means she can ground abstract theory in real analytical workflows. Rated 4.9 by students, she brings both mathematical rigor and applied perspective to advanced coursework.

Holding a Master's in Statistics, Adam digs into the graduate-level material that trips students up most — maximum likelihood estimation, Bayesian inference, multivariate distributions, and the theoretical underpinnings of hypothesis testing. He's taught and tutored across university settings, so he knows how to bridge the gap between abstract proofs and applied problem sets. Rated 4.9 by students.

Graduate-level statistics throws curveballs that intro courses never prepare you for — survival analysis, mixed-effects models, high-dimensional inference. Nina earned her master's in biostatistics at Columbia and is currently pursuing her doctorate at NYU, so she's actively immersed in the theory and application behind these methods. She also served as a teaching assistant at Columbia, giving her a sharp sense of where grad students typically get stuck.

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 throws students into multivariate analysis, ANOVA designs, and regression modeling where the intuition behind each test matters as much as running it. Macklin applies these methods daily as a medical student analyzing clinical research data, so he teaches the reasoning behind choosing a test — not just the formulas. Rated 5.0 by students.

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.

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.

Currently, I am a student at the University of Windsor, pursuing a PhD of Mechanical Engineering under the supervision of Dr. Altenhof. In 2005, I earned a Bachelor of Science degree in Mechanical Engineering from Azad University. The undergraduate curriculum in Mechanical Engineering introduced me to a wide variety of subjects, providing me with a strong foundation in the theoretical concepts of this major. Since I was interested in expanding my knowledge, acquiring skills in new technologies and methods, and opening pathways into additional employment opportunities, I applied for a master's degree in Solid Mechanic Engineering at Azad University. In 2006, I got admitted to the MSc program in Solid Mechanic Engineering at Azad University. During the first year, the coursework helped me to obtain a more in-depth understanding of the field. I engaged in research projects, presentations, and report submissions in the second year. In addition, as a Teaching Assistant, I developed pedagogical knowledge and skills. My MSc thesis project, "Experimental and numerical study of in-plane loading of thin-walled tubes," exposed me to new skills in experimental testing and software programs (LS- DYNA). I successfully earned my MSc in 2008 and ranked in the top 10% of students in my class. This two-year graduate program not only made me an expert in the field but also enhanced my critical thinking, analytic abilities, time management, and research skills. In 2009, Azad University of Tuyserkan offered me a Lecturer position. As a researcher who couldn't leave the dynamic atmosphere of the scholastic world and as a potential teacher who was constantly engaged in critical reflection, I accepted the offer. In the beginning, I took "Assessment & Evaluation in Higher Education" and " University Teaching Skills" courses to be eligible for teaching. These two mandatory courses increased my knowledge of evaluation methods, testing, and measurement in education. I learned how to motivate and activate students, encourage personal initiative and choose the most suitable teaching style. As a lecturer, I gained a great experience in teaching. I have taught Statics, Strength of Materials, and Design of machine elements courses at the undergraduate level. I believe that it has been an excellent opportunity to provide a great learning experience for a diverse group of students. As a faculty member, I have worked cooperatively and collaboratively with the campus community, including faculty members, employees, students, and others. I have contributed to the field as a researcher by conducting scholarly activities. I received two research grants from the university, conducted research and published three papers in prestigious ISI journals, and presented my research at 20 national and international conferences on Mechanical Engineering. I have valuable experience in optimizing energy absorption in thin-walled aluminum and composite tubes. I am also experienced working with Hopkinson instruments and gas gun testing equipment. In addition, I have significant experience working with the Santam instrument and the Instron testing machine. This machine can obtain the tensile and compressive strength of the specimens, input for LS Dyna software. I also became interested in Metal Matrix Composite after designing and manufacturing diffusion instruments to prepare metal matrix composite productions. As a faculty member, I provided administrative services. In 2011, I was appointed as the head of the Mechanical Engineering department for two years. I was responsible for faculty recruitment and development, faculty evaluation, program development, program review, curriculum development, class schedule planning, etc. In 2013, I was appointed as the "Vice Chancellor for Academic Affairs" for three years. In this position, I was the chair of the Academic Affairs committee. I was responsible for planning, developing, organizing, directing, and evaluating academic programs, policies, procedures, and guidelines. These two administrative services enhanced my management skills, such as strategic thinking, planning, communication, decision-making, motivating, and interpersonal skills. Driven by my dedication to sustainable engineering, I decided to pursue a Ph.D. in mechanical engineering overseas. My goal is to expand my expertise and confront fresh challenges in the realm of composite materials. If granted the chance to engage with Long Fiber Thermoplastic (LFT) composite materials during this internship, I am excited to explore industrial-scale manufacturing and design processes. I look forward to collaborating closely with fellow research engineers, anticipating that such collaboration will significantly enhance my skills and knowledge in the field. S

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 demands comfort with proofs, distributions, and inference methods that go well beyond intro courses. Dana's Master's in analytics from Georgia Tech and her current PhD research in economics give her deep fluency with topics like maximum likelihood estimation, hypothesis testing frameworks, and regression theory. Rated 4.8 by students, she brings both the mathematical rigor and the applied intuition this level requires.

I am highly praised by my students and supervisors. Even today I still kept the communication with many students.

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.