The University of Chicago’s Master of Science in Statistics emphasizes theoretical depth and practical analysis. These projects empower students to explore real-world applications in fields such as health, finance, tech, and public policy through advanced modeling and inference strategies.
Bayesian Inference in Predictive Healthcare Diagnostics
Time Series Forecasting of Cryptocurrency Prices Using ARIMA and LSTM
A Comparative Study of Bootstrap vs. Jackknife Resampling Methods
Markov Chain Monte Carlo Simulations for Climate Modeling
Designing Adaptive Experiments for Clinical Trials
Evaluating the Power and Significance of Multivariate Hypothesis Tests
Hierarchical Modeling in Socioeconomic Survey Data
Classification of Cancer Genomics Using Penalized Regression Techniques
Causal Inference with Instrumental Variables in Policy Impact Analysis
Text Mining with Latent Dirichlet Allocation on Legislative Bills
Survival Analysis in E-Commerce Subscription Lifetimes
Robust Regression Methods for Outlier-Prone Datasets
Clustering Techniques for High-Dimensional Bioinformatics Data
Creating Interactive Dashboards Using R Shiny for Public Health Monitoring
Testing Stationarity in Economic Indicators with Rolling Windows
Confidence Intervals for Proportions in Small Sample Studies
Evaluation Metrics for Imbalanced Classification Problems
Statistical Disclosure Control in Sensitive Government Data
Missing Data Imputation Strategies: MICE vs. EM Comparison
Simulation-Based Assessment of Bias in Social Science Research
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