Stanford’s M.S. in Statistics prepares students to lead with data, modeling uncertainty, making predictions, and informing policy. These project ideas span classic and modern techniques in applied statistics, including healthcare, economics, climate science, and AI.
Bayesian Hierarchical Models for Medical Treatment Effect Estimation
Survival Analysis in Predicting Patient Outcomes Post-Surgery
Causal Inference Using Instrumental Variables in Economic Data
Time Series Forecasting of Renewable Energy Demand
Statistical Modeling of Public Opinion Using Polling Data
Semi-Supervised Learning for Label-Efficient Classification Tasks
Development of Robust Estimators for High-Dimensional Data
Longitudinal Data Analysis of Chronic Disease Progression
Multivariate Techniques for Environmental Impact Assessment
Meta-Analysis of Clinical Trials with Bayesian Updating
Regression Discontinuity Design in Education Policy Evaluation
Nonparametric Bootstrap Methods for Uncertainty Quantification
Sparse PCA for Dimensionality Reduction in Genomics
Markov Chain Monte Carlo Algorithms for Big Data Sets
Anomaly Detection in Network Traffic Using Statistical Learning
Evaluating Fairness Metrics in Predictive Risk Algorithms
Random Forest vs. Gradient Boosting in Predicting Loan Defaults
Mixed-Effects Modeling in Sports Performance Analysis
Estimating Treatment Effects from Observational Healthcare Data
Statistical Simulations of Voting Patterns Across US Elections
From Bayesian inference to machine learning, Collexa supports Stanford Statistics students with model-building strategies, reproducible research, and academic writing support.
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