Teaching

FALL 2017:
 

Math 439

Linear Statistical Models

This course teaches the theory and practice of linear regression, analysis of variance (ANOVA) and their extensions, including testing, estimation, confidence interval procedures, modeling, regression diagnostics and plots, polynomial regression, colinearity and confounding, model selection, geometry of least squares. The theory will be approached mainly from the frequentist perspective and use of the computer (mostly R) to analyze data will be emphasized. Prerequisite: Math 3200 and a course in linear algebra (such as Math 309 or 429), or permission of the instructor.

Math 461

Time Series Analysis

An understanding of time series models and an ability to analyze time series data are fundamentally important skills for any statistician. These skills are particularly important for students interested in economic or financial data, but the concepts and techniques are broadly applicable in any field where data consists of observations made over time. This course will introduce the concepts and applied tools for the analysis of time series data and will better prepare you for careers which involve statistical modeling and  data analysis. Topics Include: Generalities of time series and Exploratory Data Analysis: data types, trend, seasonality, nonstationarity and stationarity; Time-series in the time domain and autocorrelation; autoregressive integrated moving average (ARIMA) models; model selection methods; forecasting; frequency domain and spectral analysis;  State-Space Models and Kalman Filter; Nonlinear time series; multivariate time series. More advanced topics if time allows it. Prerequisite: Math 493 and either Math 3200 or 494; or permission of the instructor. Some programming experience may also be helpful (consult with the instructor).
FALL 2016:
 
Math 439

Linear Statistical Models.

SYLLABUS LINK

Math 493

Probability.

SYLLABUS LINK

 
SPRING 2016:

Math 3200

Elementary to Intermediate Statistics and Data Analysis.

Syllabus LINK