An introduction to regression analysis and statistical forecasting. Topics include simple and multiple linear regression, least squares estimation, inference and confidence intervals, residual analysis and model diagnostics, outliers and influential observations, variable selection and model building, polynomial and indicator variable regression, logistic regression, and time series forecasting methods including exponential smoothing and ARIMA models. Emphasis is placed on both the theoretical foundations and practical application using statistical software.

Courses in special topics of timely interest to the profession, conducted by resident or visiting faculty.

Topics include neural networks, support vector machines, classification trees, boosting and discriminant analyses. Intended for Ph.D. students and those seeking the M.S. in Statistics.

Topics include modeling with networks and graphs; linear, nonlinear, and integer programming, construction of models employing modern modeling languages; and general solution strategies.

This special topic course on Design for Humans, Environment and Economy is offered in the early summer session. 

This course provides academic credit for independent thesis research conducted under the supervision of a Georgia Tech faculty advisor.

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