Ohio State’s MS in Statistics trains students in probability theory, statistical inference, linear models, and multivariate analysis, while offering elective depth in machine learning, Bayesian methods, bioinformatics, and design of experiments. Students develop data-analytic skills in R, Python, and SAS, working on real-world datasets across public health, business, education, and environmental science. The program emphasizes both mathematical rigor and applied statistical thinking.
Developing generalized linear models to predict insurance claim amounts
Designing stratified sampling frameworks for statewide education surveys
Bayesian survival analysis for treatment efficacy in cancer trials
Principal component analysis for dimensionality reduction in gene expression data
Markov Chain Monte Carlo methods for hierarchical model estimation
Time-series decomposition and forecasting of stock market volatility
Nonparametric density estimation for rare wildlife population studies
Analysis of variance (ANOVA) in crop yield experiments under irrigation treatments
Clustering retail customer profiles using mixture models
Logistic regression for predicting dropout rates in online learning platforms
Ridge and lasso regression for high-dimensional genomic data
Residual diagnostics and model improvement in predictive health analytics
Bootstrapping methods for constructing confidence intervals in small samples
Design and analysis of experiments for optimizing manufacturing processes
Outlier detection in industrial sensor readings using robust statistics
Multinomial logistic models in political survey response behavior
Comparative power analysis for different clinical trial designs
Random forest classification for image-based plant disease detection
Development of an interactive Shiny app for exploratory data analysis
Missing data imputation using EM algorithm vs. multiple imputation techniques
Model the world with precision and insight through OSU’s Statistics MS.
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