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Activity Number: 159 - Teaching Undergraduates and High-School Students to Analyze Time Series Data
Type: Topic-Contributed
Date/Time: Tuesday, August 10, 2021 : 10:00 AM to 11:50 AM
Sponsor: Section on Statistics and Data Science Education
Abstract #317397
Title: Long-Term and Seasonal Trends of Wastewater Chemicals in Lake Mead: An Introduction to Time Series Decomposition
Author(s): Rich Wildman*
Companies: Geosyntec Consultants
Keywords: student project; time series analysis; real data; statistical judgement; water quality; Lake Mead
Abstract:

A 2010 paper published time series of concentrations of chemicals in drinking water collected from the bottom of Lake Mead, a major American water supply reservoir. Data were compared to water level using only linear regression. This creates an opportunity for students to analyze these data further. This article presents a structured introduction to time series decomposition that compares long-term and seasonal components of a time series of a single chemical (meprobamate) with those of two supporting datasets (reservoir volume and specific conductance). For the chemical data, this must be preceded by estimation of missing datum points. Results show that linear regression analyses of time series data obscure meaningful detail and that specific conductance is the important predictor of seasonal chemical variations. To learn this, students must execute a linear regression, estimate missing data using local regression, decompose time series, and compare time series using cross-correlation. This exercise uses real data and requires that students make and justify key decisions about the analysis. This project is scalable to instructor needs and student interests.


Authors who are presenting talks have a * after their name.

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