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March 6, 2026Open Access

Statistical Prediction and Inference of Abalone Age using Multiple Regression Models

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Authors

SSSharindi SamaraweeraTexas Tech University

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Implication

Analyzes abalone age predictions using regression models, highlighting key growth factors in ecology.

Key Points

  • The central aim is to predict the age of abalones using multiple regression analysis of physical measurements.
  • Utilized the Abalone dataset from the UCI Machine Learning Repository with 4,177 observations.
  • Developed multiple linear regression models to explore relationships between shell measurements and growth rings.
  • Conducted diagnostic analyses to check assumptions like linearity and normality of residuals.
  • Applied Box-Cox transformation to improve model performance and address violations.
  • Employed model selection and ten-fold cross-validation techniques for optimal model identification.
  • Physical characteristics such as length, diameter, and weight significantly predict abalone age.
  • Regression assumptions like linearity and normality were validated or improved.
  • Identified an optimal predictive model through rigorous testing and evaluation.

Cite This Study

Sharindi Samaraweera (2026) studied this question.

synapsesocial.com/papers/69aa70e7531e4c4a9ff5b122https://doi.org/10.5281/zenodo.18867487
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