The goal of this study is to introduce a statistical method regarding the analysis of specific latent data for regression analysis of the discrete data and to build a relation between a Probit regression model (related to the discrete response) and normal linear regression model (related to the latent data of continuous response). This method provides precise inferences on binary and multinomial models which particularly in the case of small samples, has preference to maximum likelihood methods. The Probit regression model for binary outcomes can be easily and precisely explained using different normal distributions for latent data modeling. Applying this approach and using Gibbs sampler method needs simulation of standard distributions such as multivariate normal distribution. Therefore, it can be easily implemented by many softwares and it provides a general method for analyzing binary (or polychotomous) response regression models.

The Economics of Happiness is one of the relatively new areas in economics, which in recent years has found a significant place in the policy equation in most countries in the world. Today, it has been proven that there is a direct relationship between employee happiness and productivity of organizations, and this has led organizations to take steps to achieve greater productivity, the happiness of their employees. Since creating a happy organization is one of the strategic needs for long-term success, this shows that the importance of this issue is more important for important organizations in a country. In this research, socio-economic factors affecting happiness and its relationship with the productivity of taxpayers of large taxpayers have been investigated. In this paper, a sample of 150 people was selected by simple random sampling method and after collecting the questionnaires, the data were analyzed using SPSS23 and Eviews9 software. In addition, Cronbachchr('39')s alpha coefficient, which evaluates the reliability of the questionnaires, in this study was. /83, which indicates acceptable reliability for these questionnaires. The results of this study showed that in the case of the dependent variable of happiness, productivity, age, high spiritual, salary, very high job satisfy, sport, high rewards received, very high rewards received, high faith in God and very high faith in God variables were directly and singnification related to the happiness variable and variables employment, house, daily work hour, high job security and very high job security are inversely and singnification related to the variable of happiness. In the case of the dependency variable of productivity, the variables of happiness, high phisical, very high physical, very high spiritual, salary, high job security, very high job security, high rewards received, very high rewards received, very high family satisfy, high faith in God variables were directly and singnification related to the productivity variable, and the variables of education, high job satisfy and very high job satisfy are inversely and singnification related to the variable of productivity variable.

Inspired by the interactive discrete choice logit models [Aggarwal, 2019], this paper presents the advanced families of discrete choice models, such as nested logit, mixed logit, and Probit models to consider the interaction among the attributes. Besides the DM’ s attitudinal character is also taken into consideration in the computation of choice probabilities. The proposed choice models make use of Choquet integal and the recent attitudinal Choquet integral. The models are termed as Choquet nested logit (CNL), Choquet mixed multinomial logit (CMMNL), and Choquet multinomial Probit (CMP). These are further extended to represent the DM’ s attitudinal character, and termed as attitudinal CNL, attitudinal CMMNL, and attitudinal CMP.

Corporate directors are influenced by overconfidence, which is one of the personality traits of individuals; it may take irrational decisions that will have a significant impact on the company's performance in the long run. The purpose of this paper is to validate and compare the Naive Bayesian Classification algorithm and Probit regression in the prediction of Management's overconfident at present and in the future. Financial during the years are 2012 to 2017. To support the theoretical results, the samples were the companies admitted to the Tehran Stock Exchange, (financial data of 1292 companies/year in total). Data collection in the theoretical part of the study benefitted from the library method, and for calculating data, Excel software was used, and in order to test the research hypotheses Matlab 2017 and Eviews10. 0 were used. The empirical fi ndings demonstrate that, Gained nonlinear prediction model of the Naive Bayes Classification algorithm, has high ability to predict, and the Probit regression model, has limited ability to predict the over-confidence of management. Finally, the artificial intelligence prediction model (naive Bayesian classification algorithm) has better result compared with statistical binary regression prediction model (Probit regression).

Introduction & Objective: Asthma is a chronic inflammatory airway disease. Asthma affects one in 13 school age children and is a leading cause of school absenteeism. It seems that prevalence of asthma is increasing wordwide. Many factors are identified and reported as factors related to asthma. This study was carried out to determine the prevalence of asthma and associated factors in 600 children under six years using logistic regression and Probit.Materials & Methods: This cross-sectional study was conducted on 600 children under six years old. Questionnaire was constructed based on ISSAC questionnaire and its reliability was determined with a pilot study and calculated by the Cronbach's alpha equal to 69 percent. Cluster sampling based on household records as clusters was performed. Questionnaires were completed by trained staff under supervision of an expert person and by interviewing parents and children.Results: The prevalence of asthma was estimated to be 3.10 (7.89 to 12.78) percent. Based on fitting models to data, factors such as gender, maternal nutrition, exclusive breast feeding to 6 months, smoking at home by a family member and having a history of respiratory allergy in families were significantly associated with asthma prevalence (p-value £0.05).The results also demonstrated that the both models are almost identical in evaluating the data.Conclusion: This study showed that estimated asthma prevalence is equal to average prevalence reported in Iran. Protective factors, such as exclusive breast feeding as a strategy can be appropriated in children's health care programs and should be much more considered.

The aim of this study was to identify similar areas of physiography and climatic potential of walnut in Baft city. Logistic regression and discriminant analysis were used for the purpose of classifying of study area using a set of predictor variables to find the suitable areas to grow walnut. For this purpose, environmental conditions affecting walnut trees used included Annual Mean Temperature, Annual Precipitation, Precipitation of Driest Month, Precipitation of Wettest Month, Elevation, Slope Gradient and Aspect. 70% of the data, as training data and the remaining 30%, are used as model validation data. Using Probit logistic regression and discriminant analysis, the effect of variables used on walnut in Baft County was calculated and then map of walnut was calculated. The results showed effect of physiographic and climatic variables on walnut. The results of validation data set in this study showed that Probit logistic regression with 75% and discriminant analysis with 86% able to accurately explain the favorable areas for walnut cultivation. Suitable physiographic areas and climatic potentials for planting walnut trees are located in the central, northern, and northwestern regions of Baft area.