Predictive analytics techniques are widely used in the application field, and the most common of these is fitting data with functions. The aim of function fittings is to predict the value of a response, by combing the regressors. Univariate probit and logit models are used for the same purposes when the response variable is binary. Both models used applied for the estimation of the functional relationship between response and regressors. The question of which model performs better comes to the mind. For this aim, a Monte Carlo simulation was performed to compare both the univariate probit and logit models under different conditions. In In this paper we considered the simulation of, employing latent variable approach with different sample sizes, cut points, and different correlations between response variable and regressors were taken into account. To make a comparison between univariate logit and probit models, Pearson residuals, deviations, Hosmer 186 Abeer H. Alsoruji et al. and Lemesshow, area under Receiver Operating Characteristic (ROC) curve, and Pseudo-R square statistics which are used for qualitative data analysis, were calculated and the results were interpreted.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.