Analysis of Categorical Data Under Logistic Regression Model

2016 ◽  
pp. 157-177
Author(s):  
Parimal Mukhopadhyay
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Categorical Data ◽  
Logistic Regression Model

scholarly journals Utilizing the Logistic Regression Model in Analyzing the Categorical Data of Economic Effects

2021 ◽  
Vol 12 (4) ◽  
pp. 638-646
Author(s):  
Mahdi Wahhab Neamah, Et. al.
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Categorical Data ◽  
Regression Models ◽  
Logistic Regression Model ◽  
Ordinary Least Squares ◽  
Economic Effects ◽  
Ordered Categories ◽  
Qualitative Variable ◽  
Qualitative Variables

The categorical data has a significant role in representing statistical binary variables, and they are analyzed by means of grouping the response variable into ordered categories. Thereby, the dependent variable becomes of type binary qualitative variable. The data related to the financial position of world countries is classified within the categorical data. This work is to study the economic effects of an individual's different factors on determining the richness or poorness levels of a selected population of countries. Moreover, a logistic regression model is to be created to estimate these levels. As a sample of research, the categorical data relevant to the financial status of 20 Arabic countries were drawn from the website of the World Bank, WB. In addition, for comparison purpose, another similar set of categorical data was generated by MATLAB too. The paper has been based on two hypotheses, first is the well-known regression models, like the ordinary least squares or maximum likelihood, are not accurate in case of binary qualitative variables. Second, is utilizing the logistic regression model as an alternative model to achieve the paper goal.  The paper results, for both WB data and MATLAB data, have successfully proved the ability of the logistic regression model in manipulating the categorical data and predicting the coefficients of the corresponding regression models.   

scholarly journals TAVSİYE ETME DAVRANIŞININ ALGILANAN HİZMET KALİTESİ DEĞİŞKENLERİ İLE İLİŞKİSİ: ATATÜRK HAVALİMANI ÖRNEĞİ

2018 ◽  
Vol 6 (3) ◽  
Author(s):  
Bekir TUNCER ◽  
Yıldıray KIZGIN
Keyword(s):  
Logistic Regression ◽  
Quality Of Service ◽  
Service Quality ◽  
Regression Model ◽  
Significant Relationship ◽  
Categorical Data ◽  
Logistic Regression Model ◽  
Perceived Service Quality ◽  
Evaluation Scores

Passengers using the airports are getting more and more publicity of airport services, with comments on various platforms such as Skytrax, TripAdvisor and Google Rewievs. On these platforms, they can evaluate and rate various aspects of airport services. The main purpose of the study is to suggest the airport passengers' behavior to recommend the airport; the reasons for using the airport (transit, incoming-outgoing), travel purposes (holiday-work) and the quality of the perceived service quality variables. For this purpose, the evaluation scores obtained from www.airlinequality.com website were analyzed. The logistic regression model examines the perceived quality of service and how passengers are influenced by travel intentions and types, coded as categorical data such as recommending and advising travelers to recommend airport services. As a result of the analysis, it was found that there was a significant relationship between the seating areas in the airport, the directions of the guiding signs and the terminal staff variables and the behavior of the airport where the passengers were receiving service. It was determined that there was no significant relationship between the purpose of travel (work / holiday), reason for using the airport (incoming-outgoing / transit), time spent in the queue and cleaning of the terminal.

scholarly journals Work absence and multimorbidity in Portugal: results from the 1st National Health Examination Survey

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
J Matos ◽  
C Matias Dias ◽  
A Félix
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Chronic Diseases ◽  
National Health ◽  
Logistic Regression Model ◽  
Health Examination ◽  
Work Absence ◽  
Health Examination Survey ◽  
Absence From Work ◽  
The Impact

Abstract Background Studies on the impact of patients with multimorbidity in the absence of work indicate that the number and type of chronic diseases may increase absenteeism and that the risk of absence from work is higher in people with two or more chronic diseases. This study analyzed the association between multimorbidity and greater frequency and duration of work absence in the portuguese population between the ages of 25 and 65 during 2015. Methods This is an epidemiological, observational, cross-sectional study with an analytical component that has its source of information from the 1st National Health Examination Survey. The study analyzed univariate, bivariate and multivariate variables under study. A multivariate logistic regression model was constructed. Results The prevalence of absenteeism was 55,1%. Education showed an association with absence of work (p = 0,0157), as well as professional activity (p = 0,0086). It wasn't possible to verify association between the presence of chronic diseases (p = 0,9358) or the presence of multimorbidity (p = 0,4309) with absence of work. The prevalence of multimorbidity was 31,8%. There was association between age (p < 0,0001), education (p < 0,001) and yield (p = 0,0009) and multimorbidity. There is no increase in the number of days of absence from work due to the increase in the number of chronic diseases. In the optimized logistic regression model the only variables that demonstrated association with the variable labor absence were age (p = 0,0391) and education (0,0089). Conclusions The scientific evidence generated will contribute to the current discussion on the need for the health and social security system to develop policies to patients with multimorbidity. Key messages The prevalence of absenteeism and multimorbidity in Portugal was respectively 55,1% and 31,8%. In the optimized model age and education demonstrated association with the variable labor absence.

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