logistic regression

In this article, logistic regression models are studied in which the response variables are two (or multiple) values and the explanatory variables (predictor or independent) are ordinary variables, but the errors have a vagueness nature in addition to being random. Based on this, we formulate the proposed model and determine the estimation of the coefficients for a case with only one explanatory variable using the method of least squares. In the end, we explain the results with an example.

‎When working on a set of regression data‎, ‎the situation arises that this data‎ ‎It limits us‎, ‎in other words‎, ‎the data does not meet a set of requirements‎. ‎The generalized entropy method is able to estimate the model parameters‎ ‎Regression is without applying any conditions on the error probability distribution‎. ‎This method even in cases where the problem‎ ‎Too poorly designed (for example when sample size is too small‎, ‎or data that has alignment‎ ‎They are high and‎ .‎..) is also capable. ‎Therefore‎, ‎the purpose of this study is to estimate the parameters of the logistic regression model using the generalized entropy of the maximum‎. ‎A random sample of bank customers was collected and in this study‎, ‎statistical work and were performed to estimate the model parameters from the binary logistic regression model using two methods maximum generalized entropy (GME) and maximum likelihood (ML)‎. ‎Finally‎, ‎two methods were performed‎. ‎We compare the mentioned‎. ‎Based on the accuracy of MSE criteria to predict customer demand for long-term account opening obtained from logistic regression using both GME and ML methods‎, ‎the GME method was finally more accurate than the ml method‎.

Nonrespose is a source of error in the survey results and national statistical organizations are always looking for ways to control and reduce it. Predicting nonrespons sampling units in the survey before conducting the survey is one of the solutions that can help a lot in reducing and treating the survey nonresponse. Recent advances in technology and the facilitation of complex calculations have made it possible to apply statistical learning methods, such as regression and classification trees or support vector machines, to many issues, including predicting the nonresponse of sampling units in statistics. In this article, while reviewing the above methods, the nonresponse sampling units are predicted using them in an establishment survey and it is shown that a combination of the above methods is more accurate in predicting the correct nonresponse than any of these methods.

In the present era, classification of data is one of the most important issues in various sciences in order to detect and predict events. In statistics, the traditional view of these classifications will be based on classic methods and statistical models such as logistic regression. In the present era, known as the era of explosion of information, in most cases, we are faced with data that cannot find the exact distribution. Therefore, the use of data mining and machine learning methods that do not require predetermined models can be useful. In many countries, the exact identification of the type of groundwater resources is one of the important issues in the field of water science. In this paper, the results of the classification of a data set for groundwater resources were compared using regression, neural network, and support vector machine. The results of these classifications showed that machine learning methods were effective in determining the exact type of springs.

In this study, it was used logistic regression model to predictive of variables effect of education (highly educated or non-highly educated), age (young or adult), sex (male or female), location (urban or rural) and family size (low or high) on unemployment rate probability. It also was tried to analyze the changes of unemployment probability trend of these individuals during 2005 to 2013.This study was done based on the raw labor force survey results that conducted seasonality by Statistical Center of Iran (SCI) from 2005 up to now. The study results shows all variables have significant effect on unemployment probability and age, location, sex, education and family size have highest effects respectively.

This study develops a model of auto insurance demand using logit models, based on risk aversion measure along with other individual's characteristics such as age, sex, marital status, occupation, education, usage, engine size, number of accidents, driving experience and age of the car. Data for this study was collected during 2012, by random sampling methods and questionnaire. Using logistic regression model and With 497 completed questionnaires our results show that risk aversion has a significant and positive relationship with the auto insurance demand. Also marital status, governmental job, education and number of accidents variables have a significant and positive relationship with the auto insurance demand and sex and age of the car variables have a significant and negative relationship with the auto insurance demand.