Logistic Regression and Linear Discriminant Analyses in Evaluating Factors Associated with Asthma Prevalence among 10- to 12-Years-Old Children: Divergence and Similarity of the Two Statistical Methods

Abstract

Logistic regression and discriminant analyses are both applied in order to predict the probability of a specific categorical outcome based upon several explanatory variables (predictors). The aim of this work is to evaluate the convergence of these two methods when they are applied in data from the health sciences. For this purpose, we modeled the association of several factors with the prevalence of asthma symptoms with both the two methods and compared the result. In conclusion, logistic and discriminant analyses resulted in similar models.

Talent selection in youth football: Technical skills rather than general motor performance predict future player status of football talents

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Factors affecting deforestation and forest degradation in selectively logged production forest: A case study in Myanmar

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Age, Body Mass Index, and Daytime and Nocturnal Hypoxia as Predictors of Hypertension in Patients With Obstructive Sleep Apnea

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