The University of Chicago’s Statistics program equips students with rigorous analytical tools and quantitative thinking. These project ideas highlight data-driven exploration in health, finance, education, marketing, and more—grounded in mathematical integrity and statistical theory.
Predictive Modeling of Student Performance Using Socioeconomic Data
Survival Analysis Techniques in Medical Research Studies
Bayesian vs. Frequentist Approaches in A/B Testing
Regression Analysis on Housing Price Determinants
Building a Statistical Model for Election Forecasting
Time Series Forecasting of Stock Prices Using ARIMA Models
Sampling Bias and Correction Techniques in Surveys
Using Chi-Square Tests to Analyze Consumer Preferences
Multivariate Analysis in Credit Scoring Models
Logistic Regression for Binary Classification Problems
Analysis of Variance (ANOVA) in Agricultural Yield Studies
Hypothesis Testing on Gender Pay Gap in Tech Industry
Monte Carlo Simulations for Risk Estimation in Finance
Clustering Algorithms Applied to Customer Segmentation
Exploratory Data Analysis of Public Health Datasets
Developing Confidence Intervals for Epidemiological Data
Ethical Use of Statistical Models in Predictive Policing
Outlier Detection Methods in Fraud Analytics
Comparing Classification Accuracy Across Machine Learning Models
Bootstrap Methods for Estimating Sampling Distributions
Collexa supports statistics majors in model design, software tools (R, Python), test validation, and statistical report writing for polished, research-ready results.
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