UMD’s MS in Statistics trains students in probability, inference, regression, and modern data analysis. The program blends mathematical theory with real-world application, preparing graduates for careers in research, data science, government, and academia.
Bayesian inference applied to disease prevalence estimation
Multivariate analysis of socio-economic factors in urban populations
Survival analysis modeling for patient outcomes in clinical trials
Design of experiments for agricultural yield optimization
Logistic regression models to predict student graduation risk
Time series forecasting of air quality index using ARIMA
Random forest classification of loan default in financial data
Development of bootstrapping tools for confidence interval estimation
Exploratory data analysis on national health survey datasets
Monte Carlo simulation of risk in insurance portfolios
Generalized linear models applied to education policy data
Multilevel modeling for school-level academic achievement trends
Hypothesis testing in manufacturing defect detection
Propensity score matching for observational study comparisons
Analysis of variance in energy usage across regions
Design of a statistical dashboard for municipal planning
Bayesian network modeling for cybersecurity breach prediction
Factor analysis of consumer satisfaction survey data
Cluster analysis of voting patterns across states
Poisson regression for modeling daily emergency room visits
Apply probability and data science techniques to solve real-world problems in healthcare, business, and policy with UMD’s Statistics MS.
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