Statistical Prediction and Inference of Abalone Age using Multiple Regression Models

This study investigates the prediction and statistical inference of abalone age using multiple regression analysis. The analysis is conducted using the Abalone dataset obtained from the UCI Machine Learning Repository, which contains 4,177 observations and several physical measurements of abalone specimens. Multiple linear regression models are developed to examine the relationship between shell measurements and the number of growth rings, which serve as a proxy for age. Diagnostic analyses are performed to evaluate key regression assumptions including linearity, normality, and homoscedasticity of residuals. A Box–Cox transformation is applied to improve model performance and address assumption violations. Model selection procedures and ten-fold cross-validation are used to identify an optimal predictive model. The results indicate that physical characteristics such as length, diameter, and weight measurements significantly contribute to predicting abalone age. The findings demonstrate how statistical modeling techniques can provide useful insights into biological growth patterns and ecological data analysis.