SCIENTIFIC STUDIES IN THE FIELDS OF FISHERIES AND AGRICULTURE
SYSTEMATIC REVIEW OF LOGISTIC REGRESSION MODELS IN BIOMETRY RESEARCH
Yayıncı:
Holistence Publications
Logistic regression is one of the most widely used multivariate statistical analysis methods in scientific research for examining relationships between variables when the dependent variable is categorical and binary. In logistic regression, the predicted value of the binary outcome is expressed as a probability ranging from 0 to 1. This method has gained importance in agricultural science due to its ability to handle multivariate biometric data and provide insights into various phenomena. This study presents a systematic review and comprehensive assessment of the application of logistic regression models in biometric research within the field of animal science. Methodological approaches, data processing techniques, and the interpretation of results in the context of biometrics are thoroughly evaluated. As part of this review, the existing literature on logistic regression applications is critically assessed, with particular emphasis on the strengths of this method. The findings suggest that logistic regression can serve as an effective and valuable tool in applications involving biometric data. Furthermore, by presenting the theoretical framework and practical examples of logistic regression analysis in the context of biometric studies in livestock science, this study is expected to make significant contributions to the existing literature in this field.
