Statistical Research

Statistical Analysis of Neural Tissue Data in Schizophrenia Research

Core Skills:
Statistical Modeling, R Programming, Logistic Regression, Data Analysis, Biomedical Data Interpretation, Research Methods, Scientific Computing

Completed an intensive summer research program focused on applied statistics and data-driven research methodologies. Applied statistical modeling techniques in R to analyze neural tissue pH datasets associated with schizophrenia diagnostics.

Used logistic regression analysis to identify statistically significant trends within the dataset, including sex-specific correlations linked to schizophrenia diagnosis. Selected for an expanded research workload alongside a three-person team due to strong technical performance and rapid mastery of statistical analysis concepts. The poster on this project can be found here.

Neural Response Analysis Using Bootstrap Statistical Methods

Core Skills:
Bootstrap Analysis, Statistical Modeling, R Programming, Neural Data Analysis, Computational Research, Data Interpretation, Scientific Computing

Conducted statistical analysis of primate neural response datasets to evaluate reactions to spatial input stimuli. Applied bootstrap resampling methods to analyze neuronal activity patterns and assess statistical reliability within complex biological datasets.

Worked with experimentally acquired neural datasets provided by Dr. Valerie Poynor, using computational and statistical techniques to interpret neural response behavior and extract meaningful trends from high-variability data.The poster on this project can be found here. 

Both these projects involved using a statistical programming language called R. After the projects were finished, we presented both of them at a STEM conference at Fullerton at the end of Summer 2018.

Advisers:

Dr. Valerie Poynor: vpoynor@fullerton.edu

Dr. Jessica Jayness: jjaynes@fullerton.edu