The goals of observational dataset analysis vary with the management phase of environments threatened by anthropogenic chemicals. For example, identifying severely compromised sites is necessary to determine candidate sites to implement measures during early management phases. Among the most effective approaches is developing regression models with high predictive powers for dependent variable values using the Akaike information criterion. However, this analytical approach may be theoretically inappropriate to obtain the necessary information in various chemical management phases, such as the intervention effect size of a chemical required in the late chemical management phase to evaluate the necessity of an effluent standard and its specific value. However, choosing appropriate statistical methods based on the data analysis goal in each chemical management phase has rarely been performed. This study provides an overview of the primary data analysis objectives in the early and late chemical management phases. For each objective, several suitable statistical analysis methods for observational datasets are detailed. In addition, examples of linear regression analysis procedures using an available dataset derived from field surveys conducted in Japanese rivers are presented.

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