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首页> 外文期刊>Journal of Statistics Education >Long-Term and Seasonal Trends of Wastewater Chemicals in Lake Mead: An Introduction to Time Series Decomposition
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Long-Term and Seasonal Trends of Wastewater Chemicals in Lake Mead: An Introduction to Time Series Decomposition

机译：米德湖废水化学物质的长期和季节性趋势：时间序列分解简介

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摘要

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.
机译：最近的一篇论文发表了从美国主要供水水库米德湖底部收集的饮用水中化学物质浓度的时间序列。仅使用线性回归将数据与水位进行比较。这为学生提供了进一步分析这些数据的机会。本文介绍了时间序列分解的结构化介绍，该序列比较了单个化学物质（甲氨甲酸酯）的时间序列的长期和季节性成分与两个支持数据集（储层体积和比电导）的时间和成分。对于化学数据，必须先估算丢失的基准点。结果表明，时间序列数据的线性回归分析掩盖了有意义的细节，并且特定的电导率是季节性化学变化的重要预测因子。要了解这一点，学生必须执行线性回归，使用局部回归估计缺失数据，分解时间序列，并使用互相关比较时间序列。这些练习的完整R代码出现在补充信息中。本文使用真实数据，要求学生做出有关分析的关键决定并证明其合理性。它可以是一个指导项目，也可以是一个单独的项目。它可以按照本文明确指出的方式扩展为满足教师的需求和学生的兴趣。

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