The primary objective of this course is to give students a rigorous training in 
building statistical models for, and drawing inferences from, time series data, which 
arise naturally in a variety of disciplines. The course will weave several threads 
together from statistical model building techniques to an in-depth assessment of fitted 
models, while giving careful consideration to practical issues of model fitting via 
statistical software (e.g., using SAS). The emphasis is more on analyzing data using 
various statistical techniques rather than deriving theoretical results. Typically, the 
course incorporates project assignments as a framework on which students will develop 
their abilities to think critically about statistical modeling of real data, to use 
statistical software to analyze real data, and to communicate the findings of their 
analyses through writing and oral presentation.  Technology is integrated into the 
course through the use of statistical software (e.g., SAS), spreadsheet software (e.g., 
Excel), internet-based teaching tools (e.g., WebCT), e-mail, and word processing. The 
course will strive to give students a high degree of competence in analyzing time 
series data.

Course topic includes basic tools for identifying characteristics of time series data 
such as stationarity, non-stationarity, trend, seasonality and variability. Topics such 
as Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated 
Moving Average (SARIMA) and intervention models will be introduced in detail, along 
with statistical estimation techniques such as maximum likelihood and ordinary least 
squares. Emphasis will be placed on model diagnostics and assessments.