Water quality predictions through linear regression - A brute force algorithm approach.

ABSTRACT: Linear regression is one of the oldest statistical modeling approaches. Still, it is a valuable tool, particularly when it is necessary to create forecast models with low sample sizes. When researchers use this method and have numerous potential regressors, choosing the group of regressors for a model that fulfills all the required assumptions can be challenging. In this sense, the authors developed an open-source Python script that automatically tests all the combinations of regressors under a brute-force approach. The output displays the best linear regression models, regarding the thresholds set by users for the required assumptions: statistical significance of the estimations, multicollinearity, error normality, and homoscedasticity. Further, the script allows the selection of linear r

SUBMITTER: P Fernandes AC 

PROVIDER: S-EPMC10106967 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature