Decision Tree and Logistic Regression Analysis to Explore Factors Contributing to Harbour Tugboat Accidents

2020 ◽  
pp. 1-26
Author(s):  
Remzi Fiskin ◽  
Erkan Cakir ◽  
Coşkan Sevgili
Keyword(s):  
Data Mining ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Decision Tree ◽  
Influential Factor ◽  
Factors Affecting ◽  
Port Authorities ◽  
Prevention Programmes ◽  
Tree Algorithms ◽  
Using Data

As tugboats interact very closely with ships in restricted waters, the possibility of accidents increases in these operations. Despite the high accident possibility, there is a gap in studies on tugboat accidents. This study aims to analyse accidents involving tugboats using data mining. For this purpose, a tugboat accidents dataset consisting of a total of 496 accident records for the period from 2008 to 2019 was collected. Logistic regression and decision tree algorithms were implemented to the dataset. The results revealed that tugboat propulsion type is the most important and influential factor in the severity of tugboat accidents. The inferences drawn from these results could be beneficial for tugboat operators and port authorities in enhancing their awareness of the factors affecting tugboat accidents. In addition, the outputs of this study can be a reference for management units in developing strategies for preventing tugboat accidents and can also be used in effective planning for practicable prevention programmes and practices.

Factors affecting desired family size in Bangladesh

1994 ◽  
Vol 26 (3) ◽  
pp. 369-375 ◽  
Author(s):  
M. Kabir ◽  
Ruhul Amin ◽  
Ashraf Uddin Ahmed ◽  
Jamir Chowdhury
Keyword(s):  
Regression Analysis ◽  
Family Size ◽  
Contraceptive Use ◽  
Policy Implications ◽  
Work Status ◽  
Factors Affecting ◽  
Rural Bangladesh ◽  
Contraceptive Prevalence ◽  
Using Data ◽  
Desired Family Size

SummaryFactors affecting desired family size in rural Bangladesh are examined using data from contraceptive prevalence surveys conducted between 1983 and 1991. The analysis suggests that mothers having two sons and one daughter are more inclined to perceive their family as complete than those having three sons and no daughter. Logistic regression analysis indicates that important determinants of desire for more children are age of woman, current contraceptive use status, work status, and family planning worker's visit. The policy implications of these findings are discussed.

Predicting customer churn in mobile industry using data mining technology

2017 ◽  
Vol 117 (1) ◽  
pp. 90-109 ◽  
Author(s):  
Eui-Bang Lee ◽  
Jinwha Kim ◽  
Sang-Gun Lee
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Online News ◽  
Partial Least Square ◽  
Churn Prediction ◽  
Mining Technology ◽  
Content Type ◽  
Customer Churn ◽  
Mobile Industry ◽  
Using Data

Purpose The purpose of this paper is to identify the influence of the frequency of word exposure on online news based on the availability heuristic concept. So that this is different from most churn prediction studies that focus on subscriber data. Design/methodology/approach This study examined the churn prediction through words presented the previous studies and additionally identified words what churn generate using data mining technology in combination with logistic regression, decision tree graphing, neural network models, and a partial least square (PLS) model. Findings This study found prediction rates similar to those delivered by subscriber data-based analyses. In addition, because previous studies do not clearly suggest the effects of the factors, this study uses decision tree graphing and PLS modeling to identify which words deliver positive or negative influences. Originality/value These findings imply an expansion of churn prediction, advertising effect, and various psychological studies. It also proposes concrete ideas to advance the competitive advantage of companies, which not only helps corporate development, but also improves industry-wide efficiency.

scholarly journals Factors Affecting Palm Farmers’ Decisions to Dispense Plantage-Product Zakat in Labuhanbatu Selatan Regency

2018 ◽  
Vol 3 (3) ◽  
pp. 13-24
Author(s):  
Amalia Agustin Syn ◽  
Khalifah Muhammad Ali ◽  
Didin Hafidhuddin
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Fresh Fruit ◽  
Factors Affecting ◽  
Fresh Fruit Bunches ◽  
The Many

Zakat is one of the five points in rukun Islam and consists of two types: zakat nafs (soul) and zakat maal (wealth). One of the many kinds of zakat maal is plantage-product zakat. Labuhanbatu Selatan Regency is an area that contains 160,785.04 hectares of palm plantation land as recorded in 2016. The area also produced 7,493,696.18 tons of Fresh Fruit Bunches (FFB) in the same year. The objective of this study is to identify the potential of plantage-product zakat (specifically palm plantage) and analyze the factors that affect farmers’ decision to dispense plantage-product zakat in Labuhanbatu Selatan Regency. Logistic regression analysis was the method of analysis used in this study. Based on data obtained from Dinas Perkebunan Provinsi Sumatera Utara 2016, the potential of plantage-product zakat in Labuhanbatu Selatan Regency reached 25.6 billion rupiahs in 2014; 21.6 billion rupiahs in 2015, and 370.4 billion rupiahs in 2016. The variables that significantly affected farmers’ decisions to dispense plantage-product zakat were comprehension of zakat, faith, rewards, Islamic study, and frequency of worship.

scholarly journals Factors affecting internet use among university students in Sarawak, Malaysia: an empirical study

2020 ◽  
Vol 7 (3) ◽  
pp. 848
Author(s):  
M. Mizanur Rahman ◽  
M. Taha Arif ◽  
Fready Luke ◽  
Santha Letchumi ◽  
Fatin Nabila ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
University Students ◽  
Logistic Regression Analysis ◽  
Internet Use ◽  
Binary Logistic Regression ◽  
Problematic Internet Use ◽  
The Internet ◽  
Binary Logistic Regression Analysis ◽  
Factors Affecting

Background: The internet has become an indispensable tool for communication, academic research, information and entertainment. However, heavy users of the internet lead to less confidence in social skills and the tendency to be isolated. The study aimed to assess the pattern of internet use and factors affecting problematic internet use among university students.Methods: This cross-sectional study conducted among the students of a university in Sarawak, Malaysia. A multistage cluster sampling technique was adapted to select the participants. Data were collected from 463 students by self-administered questionnaire. Hierarchical binary logistic regression analysis was done to determine the potential factors for problematic internet use.Results: The mean age of the students was 22 years, with a standard deviation of 1.6 years. Two-fifths (61.8%) of the students had no problematic internet use. However, 35.4% had moderate and 2.8% had severe problematic internet use. Hierarchical binary logistic regression analysis found that age of the students, year of study, duration of daily internet use and use of social networking like Skype appeared to be potential predictors of problematic internet use (p<0.05).Conclusions: This study was conducted in only one university, thus did not depict the overall scenarios of the country. The implications of the findings are still worth noting in the process of designing internet addiction studies among university students. Overall, this study has unearthed some useful insights which can serve as a guide to more elaborate studies.

Analysis of cognitive characteristics and influencing factors of mass sports rehabilitation in Taiyuan

2019 ◽  
Author(s):  
Yaye He ◽  
Jiangong Wang ◽  
Liangyuan Zhao
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Health Status ◽  
Influencing Factors ◽  
Logistic Regression Analysis ◽  
Education Level ◽  
Factors Affecting ◽  
Exercise Rehabilitation ◽  
And Gender ◽  
Socio Economic Factors

Abstract Background: To assess the awareness regarding sports rehabilitation among residents of Taiyuan. Method: From September 27, 2018 to March 29, 2019, 1200 residents who met the inclusion/ criteria were selected using convenient sampling method. The population was surveyed by self-designed questionnaires, and single factor and two-category logistic regression analysis (stepwise forward method) was used to identify the factors influencing awareness of mass sports rehabilitation in Taiyuan. Results: A total of 1200 questionnaires were issued, of which 1167 were collected and 1101 were valid. The corresponding recovery and effective recovery rates were 97.25% and 94.34% respectively. The overall rate of awareness of exercise rehabilitation was 80.7%, and education level, occupation, income and health status were significant influencing factors (R<0.05). The results of two-class logistic regression analysis showed that age, occupation, education level, income level and health status were the influencing factors affecting the public's perception of the sports rehabilitation concept (R<0.05), whereas gender, occupation, education level and health status influenced understanding of the establishment of the rehabilitation department in Taiyuan (R<0.05), and gender, age, education level and health status affected understanding of the types of patients receiving rehabilitation (R<0.05). Conclusion: There is a high general awareness regarding sports rehabilitation, and is influenced by various socio-economic factors.

scholarly journals Investigation of Factors Affecting the Achievement of University Students with Logistic Regression Analysis: School of Physical Education and Sport Example

SAGE Open ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 215824402090208
Author(s):  
Yeliz Eratlı Şirin ◽  
Mustafa Şahin
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Academic Success ◽  
Physical Education ◽  
University Students ◽  
Logistic Regression Analysis ◽  
Factors Affecting ◽  
Course Success ◽  
Effective Factor ◽  
The Way

In this study, the factors affecting the success of university students were analyzed by logistic regression analysis. In the study, success variable was defined according to the survey information applied to 360 university students studying in School of Physical Education and Sport in Çukurova University and Kahramanmaraş Sütçü İmam University in Turkey, in 2017–2018 academic year. The relationship between the answers to the Likert-type scale questions affecting success variables and the course success was estimated by logistic regression analysis. According to the results of the research, because independent variables such as mother’s education status, age, and class were statistically insignificant, they were not included in the multivariate model. According to the findings, variables such as gender, the university they studied, the way they chose their department, and father’s education are seen as important in the growth of students’ academic success. In addition to this, the variables such as counseling about their profession, support of department’s instructors, and communication with instructor have been found to be considerably effective on success. It was observed that the way they chose their department (willingly–compulsorily) was the most effective factor, and father’s education was the second effective factor. As a result, the success levels of the students were found to differ according to the sociodemographic characteristics and their relations with the instructors. On the contrary, as the instructors’ guidance, support, and communication skills are effective contributors on student’s success, it has been concluded that instructors should take these factors into account.

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