Time Series Methods
Our course focuses on trend tests and regression models for data measured frequently in time ("real-time data"
Today, 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 – one observation is partly or predominantly a replicate of the previous one. The consequence is that statistical tests such as trend analysis and regression provide invalid results when used on data stored every 1, 5, or 15 minutes apart.
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 ▫
Trend analysis using time series methods ▫
Autoregressive and Moving Average Time Series models ▫
Forecasting WQ variables – how good is my forecast? ▫
Bootstrap methods for time series
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