Relationship between decision changes under the study of random response (RR) using the logistic regression model
Department of Mathematices, Minab Branch, Islamic Azad University, Minab, Iran
2 Department of Mathematices, Evaz Center, Islamic Azad University, Evaz, Iran
3 Department of Statistics, Bandare Jask Branch, Islamic Azad University, Bandare Jask, Iran
Accepted: 19 July 2022
Published online: 24 August 2022
This study focuses on the uni-variate logistic regression model and the multi-variate analyzes when response variables are reliant on random response and multi-variate logistic regression in the form of an RR design, which is something that we look at for the first time. The research is divided into two sections. In the first part, we employ a single variable to express binary RR response variables. This is done during the first step of the logistic regression model. This model is referred to as a generalized linear model, and our GLM has useful characteristics such as the features of parameter estimates derived from the GLM standard (GLM). The second section of a multivariate logistic regression model incorporates response variables as one of its components. Two components of the model that have not previously been discussed will be explained by providing it in the format of (GLM) before: 1- This model contains useful properties of standard GLM, such as Properties of parametric estimates. 2- Standard GLM application can be used to test this model. This research shows how Common settings are possible in R and GLIM software and this ensures that logistic regression models may be evaluated with confidence for RR response variables. Also included are random variables to aid in investigating the link between different aspects of an individual's RR response, as well as the multi-variate logistic regression model that we developed to help accomplish just that.
Key words: Uni-variate and multi-variate logistic regression / Randomized response / Sensitive questions
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