Mobility is one of the potential factors in contributing to the spread of COVID-19. Our goal is to estimate the effect of state-wide policies on mobility signals at a county-level.
This report is an analysis of STAR (Student-Teacher Achievement Ratio) project data specifically targeted the 1st graders’ dataset with teacher as the unit (class-level average score).
We aim to resolve the traditionally tedious and time consuming task of determining the age of abalone by constructing predictive models of the ages of abalones using other physical measures that are easier to obtain.
We aim to train a classifier to accurately classify a person whether or not has a heart disease problem. The dataset is provided by UCI Machine Learning Repository.
This poster outlines steps our student research team at BYU-Hawaii took to answer this question. Results from factor analysis, a review of literature and existing similar surveys, and information gleaned from faculty and staff members informed the survey revision pilot. Results of the pilot and next steps will be shared.