.....for
frequently collected, "real-time" dataToday, water-quality and other scientific data can be measured by automatic recorders or remotely by satellite only seconds apart from one another. Agencies have begun to store, present, and analyze these "real-time data". Data recorded this closely together usually violate the independence assumption of standard statistical procedures – "independence" implying that one observation provides no information on what value the next observation might be. The consequence is that standard statistical methods such as hypothesis tests and regression provide invalid results when used on data stored every 1, 5, or 15 minutes apart.
Our Time Series & Forecasting course focuses on performing hypothesis tests and building regression models for data measured frequently in time.
Topics include:
What is serial correlation? How to test for it?
Effective sample size for correlated data
Effects of serial correlation on hypothesis tests
Comparisons to standards for correlated data
Building regression models using time series methods
Autoregressive and Moving Average Time Series models
Forecasting WQ variables – how good is my forecast?
Bootstrap methods for time series
Next class:
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Course Outline
Course Brochure [coming soon]