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Long-Term and Seasonal Trends of Wastewater Chemicals in Lake Mead: An Introduction to Time Series Decomposition
- Source :
- Journal of Statistics Education, Vol 25, Iss 1, Pp 38-49 (2017)
- Publication Year :
- 2017
- Publisher :
- Taylor & Francis Group, 2017.
-
Abstract
- A recent 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. Complete R code for these exercises appears in the supplementary information. This article uses real data and requires that students make and justify key decisions about the analysis. It can be a guided or an individual project. It is scalable to instructor needs and student interests in ways that are identified clearly in this article.
- Subjects :
- 0106 biological sciences
Statistics and Probability
Cross-correlation
010501 environmental sciences
01 natural sciences
Environmental science
Local regression
Education
Wastewater contaminant
Statistics
Linear regression
Econometrics
Missing values
Time series
0105 earth and related environmental sciences
lcsh:LC8-6691
Series (mathematics)
lcsh:Special aspects of education
010604 marine biology & hydrobiology
Missing data
Water level
Water quality
Statistics, Probability and Uncertainty
lcsh:Probabilities. Mathematical statistics
lcsh:QA273-280
Decomposition of time series
Subjects
Details
- Language :
- English
- ISSN :
- 10691898
- Volume :
- 25
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- Journal of Statistics Education
- Accession number :
- edsair.doi.dedup.....16bd9f70ad68edb55ec07dce228f6ceb