Stanford’s Statistics program develops students' analytical, computational, and reasoning skills to draw conclusions from data. These project ideas emphasize real-world application of statistical models across industries including health, tech, sports, and public policy.
Bayesian Inference to Estimate Vaccine Effectiveness
Predicting Election Outcomes Using Logistic Regression
Survival Analysis of Patients Undergoing Cancer Treatment
Hypothesis Testing on Gender Bias in Hiring Data
Statistical Study on the Spread of Online Misinformation
Forecasting Housing Prices Using Time Series Models
Analysis of NBA Player Efficiency Using Multivariate Regression
Design of Experiments for Crop Yield Optimization
Text Sentiment Classification Using Naive Bayes
Bootstrap Methods for Confidence Interval Estimation
Classification of Heart Disease Patients Using Random Forest
Markov Chain Monte Carlo (MCMC) Methods for Disease Modeling
Study of Mental Health Trends Through Longitudinal Surveys
Clustering of Retail Customers Based on Purchasing Patterns
Variance Analysis in Student Performance Across School Districts
A/B Testing for Webpage Conversion Optimization
Modeling COVID-19 Transmission Rates Using Poisson Regression
Network Analysis of Social Media Influence in Political Campaigns
Comparison of Parametric and Non-Parametric Tests in Small Samples
Missing Data Imputation in Health Datasets Using Multiple Imputation
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