An advanced graduate-level course covering the theory and methodology of linear regression and linear statistical models. The first half covers estimation, inference, model diagnostics, hypothesis testing, model selection, and relaxation of common assumptions in ordinary least squares.
The second half places linear models as modern tools for statistical learning, covering feature selection, regularization, mixed-effects linear models, nonparametric regression, tensor regression and prescriptive analytics.
Emphasis is placed on rigorous statistical reasoning and the application of linear models to real-world data using modern developments.
An undergraduate-level course introducing the theory and methodology of statistical time series analysis, with emphasis on understanding temporal dependence, stationarity, autocorrelation, and stochastic processes.
Students develop practical skills through project-based analysis of real-world time series, applying exploratory techniques, ARMA and ARIMA models, seasonal models, forecasting methods, and model diagnostics.
Emphasis is placed on connecting statistical theory with computational practice, guiding students through the complete process of analyzing, modeling, validating, and forecasting real time series data.
An undergraduate-level course introducing the theory and practice of regression analysis as a fundamental tool for statistical modeling and inference. Topics include simple and multiple linear regression, estimation, inference, model interpretation, diagnostics, transformations, and model selection.
Students develop practical skills through project-based analysis of real-world datasets, applying regression methods to formulate questions, build models, assess assumptions, and communicate statistical results.
Emphasis is placed on developing sound statistical reasoning and computational skills, connecting regression theory with practical applications and modern data analysis.
An undergraduate-level course introducing the theoretical foundations of statistical inference, including point estimation, confidence intervals, hypothesis testing, and properties of statistical estimators. Both analytical and simulation-based inference are covered.
Emphasis is placed on developing rigorous statistical reasoning and connecting probability theory with practical inference for real-world data.
An undergraduate-level course introducing the mathematical foundations of probability. Topics include probability spaces, random variables, probability distributions, expectation, conditional probability, independence, and fundamental limit theorems.
Emphasis is placed on rigorous mathematical reasoning and developing the probabilistic foundations required for statistical modeling, mathematical analysis, and applications across the sciences.