Purpose: In this paper, the authors try to develop a model for predicting insolvency of trading companies from Republic Srpska. The research seeks determine statistically most significant financial indicator in Methodology: data sample paper consists yearly 2017 2020 two hundred Binary logistic regression was used model. Results: As result research, created that successfully classifies 82.9% so...

In statistics, logistic regression is a regression model to predict a binomially distributed response variable. Recent research has investigated the opportunity of combining logistic regression with decision tree learners. Following this idea, we propose a novel Logistic Model Tree induction system, SILoRT, which induces trees with two types of nodes: regression nodes, which perform only univar...

This paper addresses the problem of inference for a multinomial regression model in the presence of likelihood monotonicity. This paper proposes translating the multinomial regression problem into a conditional logistic regression problem, using existing techniques to reduce this conditional logistic regression problem to one with fewer observations and fewer covariates, such that probabilities...

Motivated by the two paradoxical facts that the marginal cost of following one extra candidate is close to zero and that the majority of Twitter users choose to follow only one or two candidates, we study the Twitter follow behaviors observed in the 2016 U.S. presidential election. Specifically, we complete the following tasks: (1) analyze Twitter follow patterns of the presidential election on...

In this study, we first explore whether different exposure groups of workplace bullying exist, employing a large, heterogeneous sample. The results show six different exposure groups: almost 30.5% is not bullied since they report hardly any negative act at work at all, 27.2% face some limited work criticism, 20.8% face limited negative encounters, 8.3% is occasionally bullied, 9.5% are predomin...