scholarly journals ASSESSMENT OF ABNORMALLY LOW TENDERS: A MULTINOMIAL LOGISTIC REGRESSION APPROACH

2015 ◽  
Vol 23 (6) ◽  
pp. 848-859
Author(s):  
Murat GUNDUZ ◽  
H. Volkan KARACAN
Keyword(s):  
Logistic Regression ◽  
Control Systems ◽  
Multinomial Logistic Regression ◽  
Past Research ◽  
Negative Effects ◽  
Statistical Tool ◽  
Factors Affecting ◽  
Regression Approach ◽  
Negative Impacts ◽  
Significant Factors

This study was performed in order to reveal factors affecting abnormally low tenders (ALTs) and to minimize negative effects of them. A thorough literature review was carried out to observe past research about the reasons of and possible solutions to ALTs. A questionnaire was prepared and submitted to construction professionals to capture negative impacts of ALTs based on the interviews with experts and past literature. 430 companies responded to the questionnaire. The data analysis was carried out by the multinomial logistic regression statistical tool. Having quality control systems and restricted procedure with prequalification procurement systems were main significant factors to reduce ALTs. Based on all significant factors, recommendations were made to construction professionals and companies to reduce adverse effects of ALTs.

scholarly journals Determinant factors of fishermen income and decision-making for providing welfare insurance: An application of multinomial logistic regression

2021 ◽  
pp. 175-184
Author(s):  
Sukono Sukono ◽  
Riaman Riaman ◽  
Titin Herawati ◽  
Jumadil Saputra ◽  
Endang Soeryana Hasbullah
Keyword(s):  
Logistic Regression ◽  
Multinomial Logistic Regression ◽  
Poverty Line ◽  
Coastal Environment ◽  
Negative Effects ◽  
Average Income ◽  
Cross Sectional ◽  
Factors Affecting ◽  
The Right ◽  
Environment Condition

As a country surrounded by the ocean, Indonesia is categorized as a country that has marine potential. The fishermen communities’ economy depends on ocean. However, the fishermen communities live below the poverty line and their average income is less than regional minimum wage. In conjunction with the issue, this study seeks to investigate the factors affecting the income of fishermen communities and right decision to fishermen in covering with welfare insurance in Cirebon, Indonesia. The quantitative study is designed using cross-sectional approach. The data collected by applying random sampling with open-ended questions and interview. A total of 100 fishermen’s have participated in this study. The study used some factors in measuring the fishermen community income, namely coastal environment condition, fish catching technology and location, operational capital, climate (season) condition, fishermen’s age, fishermen’s education, and fishing experience. The data are analyzed using the multinomial logistic regression model by assisting the statistical software, i.e., SPSS-23. The results show that coastal environment condition, fish catching technology and location, operational capital, climate (season) condition, fishermen’s age, fishermen’s education, and fishing experience have significant effects on fishermen income. Interestingly, the factor of coastal environment condition and climate (season) condition have significant negative effects on fishermen income. In conclusion, this study identified that two important factors reduced the welfare level of fishermen (via income). Also, in line with that things, the right decision which can provide to support and assist the fishermen community was by providing the welfare insurance. It is purposely to give them the protection from various risks faced by fishermen.

scholarly journals An Investigation of Factors Affecting the Willingness to Invest in Renewables among Environmental Students: A Logistic Regression Approach

2019 ◽  
Vol 11 (18) ◽  
pp. 5012 ◽  
Author(s):  
Evangelia Karasmanaki ◽  
Spyridon Galatsidas ◽  
Georgios Tsantopoulos
Keyword(s):  
Logistic Regression ◽  
Renewable Energy ◽  
Logistic Regression Model ◽  
Renewable Energy Sources ◽  
Clean Energy ◽  
Small Scale ◽  
Factors Affecting ◽  
The World ◽  
Regression Approach ◽  
Significant Factors

Renewable energy sources (RES) have gained increased popularity across the world mainly due to their ability to contribute to environmental protection through the generation of infinite ‘clean’ energy. To achieve a greater diffusion of renewables, however, small-scale investments implemented by individuals are critically important. In contrast to citizens whose attitudes have been consistently explored by research, there is little evidence on the attitudes towards investments among environmental students who will occupy positions of responsibility and play key roles in the environmental sector in the future. Hence, the purpose of the present study is to identify the most important factors that affect environmental students’ willingness to invest in renewable energy (RE) by developing a logistic regression model. According to our analysis, the participants in their majority expressed their willingness to invest, while environmental values, the low risk and profitability of renewable investments, as well as preferences for certain energy types were significant factors determining this willingness. However, willingness to invest was irrespective of the current taxation and subsidies, suggesting that significant improvements are required in these areas. The present study could be particularly useful for policymakers since the necessary steps to create favorable investment environments in Greece and elsewhere are highlighted.

A Multinomial Logistic Regression Approach for Arrhythmia Detection

2017 ◽  
Vol 8 (4) ◽  
pp. 17-33 ◽  
Author(s):  
Omar Behadada ◽  
Marcello Trovati ◽  
Georgios Kontonatsios ◽  
Yannis Korkontzelos
Keyword(s):  
Logistic Regression ◽  
Big Data ◽  
Multinomial Logistic Regression ◽  
Vascular Diseases ◽  
Arrhythmia Detection ◽  
Medical Sector ◽  
The World ◽  
Regression Approach ◽  
Insight Into ◽  
Tools And Methods

Cardiovascular diseases are the leading causes on mortality in the world. Consequently, tools and methods providing useful and applicable insights into their assessment play a crucial role in the prediction and managements of specific heart conditions. In this article, we introduce a method based on multi-class Logistic Regression as a classifier to provide a powerful and accurate insight into cardiac arrhythmia, which is one of the predictors of serious vascular diseases. As suggested by our evaluation, this provides a robust, scalable, and accurate system, which can successfully tackle the challenges posed by the utilisation of big data in the medical sector.

scholarly journals Factors Affecting Mortality in Patients Admitted to the Hospital by Emergency Physicians despite Disagreement with Other Specialties

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Engin Ozakin ◽  
Arif Alper Cevik ◽  
Filiz Baloglu Kaya ◽  
Nurdan Acar ◽  
Fikri M. Abu-Zidan
Keyword(s):  
Blood Pressure ◽  
Logistic Regression ◽  
Systolic Blood Pressure ◽  
Logistic Regression Model ◽  
Admission Diagnosis ◽  
Emergency Physicians ◽  
Factors Affecting ◽  
Admission Decisions ◽  
Service Departments ◽  
Significant Factors

Background. Emergency physicians (EPs) face critical admission decisions, and their judgments are questioned in some developing systems. This study aims to define the factors affecting mortality in patients admitted to the hospital by EPs against in-service departments’ decision and evaluate EPs’ admission diagnosis with final discharge diagnosis. Methods. This is a retrospective analysis of prospectively collected data of ten consecutive years (2008–2017) of an emergency department of a university medical center. Adult patients (≥18 years-old) who were admitted to the hospital by EPs against in-service departments’ decision were enrolled in the study. Significant factors affecting mortality were defined by the backward logistic regression model. Results. 369 consecutive patients were studied, and 195 (52.8%) were males. The mean (SD) age was 65.5 (17.3) years. The logistic regression model showed that significant factors affecting mortality were intubation (p<0.0001), low systolic blood pressure (p=0.006), increased age (p=0.013), and having a comorbidity (p=0.024). There was no significant difference between EPs’ primary admission diagnosis and patient’s final primary diagnosis at the time of disposition from the admitted departments (McNemar–Bowker test, p=0.45). 96% of the primary admission diagnoses of EPs were correct. Conclusions. Intubation, low systolic blood pressure on presentation, increased age, and having a comorbidity increased the mortality. EPs admission diagnoses were highly correlated with the final diagnosis. EPs make difficult admission decisions with high accuracy, if needed.

Updating the 1:50,000 Dutch soil map using legacy soil data: A multinomial logistic regression approach

Geoderma ◽  
2009 ◽  
Vol 151 (3-4) ◽  
pp. 311-326 ◽  
Author(s):  
Bas Kempen ◽  
Dick J. Brus ◽  
Gerard B.M. Heuvelink ◽  
Jetse J. Stoorvogel
Keyword(s):  
Logistic Regression ◽  
Multinomial Logistic Regression ◽  
Soil Map ◽  
Regression Approach

scholarly journals Analisis Faktor Penentu Kualiti Pengurusan Zakat: Kajian Terhadap Lembaga Zakat Selangor (Lzs) di Sepang

2018 ◽  
Vol 11 (1) ◽  
pp. 27-44
Author(s):  
Maryam Abd Rashid ◽  
Hairunnizam Wahid ◽  
Sanep Ahmad
Keyword(s):  
Factor Analysis ◽  
Logistic Regression ◽  
Quality Of Service ◽  
Regression Analysis ◽  
Service Quality ◽  
Logistic Regression Analysis ◽  
Factors Affecting ◽  
Five Factors ◽  
Significant Factors

Abstract Service quality is an important element in assessing the ability of institutions to meet its goals. The objective of this paper is to examine the factors that probably affect the quality of service for zakat institution, especially in Sepang using the SERVQUAL model which has five factors; reliability, react, assurance, empathy and tangible. A total of 103 respondents from Muslim entrepreneurs have participated in this study. This study uses several approaches, including exploratory factor analysis (EFA) and logistic regression analysis. Findings indicate that empathy and tangible are significant factors affecting the quality of service LZS in model studied. Several implications have been discussed in this study. Keywords : LZS’s service quality, Muslim entrepreneurs, SERVQUAL Abstrak Kualiti perkhidmatan adalah satu elemen penting dalam menilai keupayaan institusi untuk memenuhi matlamatnya. Objektif kertas ini adalah untuk melihat faktor yang dijangka mempengaruhi kualiti perkhidmatan institusi zakat khususnya di daerah Sepang dengan menggunakan model SERVQUAL yang mempunyai lima faktor iaitu kebolehpercayaan, bertindak balas, jaminan, empati dan kewujudan. Sejumlah 103 responden yang terdiri daripada usahawan muslim telah mengambil bahagian dalam kajian ini.  Kajian ini menggunakan beberapa ujian termasuklah analisis tinjauan faktor (EFA) dan analisis regresi logistik. Dapatan kajian menunjukkan faktor empati dan kewujudan adalah signifikan dan mempengaruhi kualiti perkhidmatan LZS dalam model yang dikaji. Beberapa implikasi kajian turut dibincangkan dalam kajian ini. Kata kunci : kualiti perkhidmatan LZS, usahawan muslim, SERVQUAL

Factors affecting the number of building defects and the approaches to reduce their negative impacts in Malaysian public universities’ buildings

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ali Hauashdh ◽  
Junaidah Jailani ◽  
Ismail Abdul Rahman ◽  
Najib Al-Fadhali
Keyword(s):  
Environmental Sustainability ◽  
Qualitative Data ◽  
Maintenance Phase ◽  
Research Strategy ◽  
Content Type ◽  
Factors Affecting ◽  
Building Maintenance ◽  
Safety And Health ◽  
Negative Impacts ◽  
Significant Factors

Purpose The largest share of a building maintenance budget goes towards preventing or repairing building defects. Also, building defects shorten a building’s lifetime, impact the user’s safety and health, prevent the buildings from performing their functions well and repairing building defects generates waste. Therefore, this study aims to specify the factors that affecting the number of building defects and how to reduce their negative impacts. Design/methodology/approach A case study was used as a research strategy and convergent parallel mixed methods were used as research design. Quantitative and qualitative data were collected concurrently, followed by independent analyses of the quantitative and qualitative data, and then merged the two sets of results according to the procedure of using the convergent parallel design. Descriptive statistics analysed quantitative data, whilst qualitative data was analysed by the content analysis technique. Findings The findings of this study explored the factors that affect the number of defects in buildings, the significant factors were related to the building’s life cycle in terms of design, construction, operation and maintenance phase; relevant attributes were construction teams, building users and maintenance teams. The study also addressed the approaches to minimise the negative impacts of those factors. Their negative impacts mainly contributed to increased building defects that increase maintenance costs, affect users’ safety and health, reduce buildings’ lifespan and cause environmental impact due to resource extraction. Originality/value The existing studies have not adequately addressed the significant factors that affect the number of building defects. Also, emerging technologies and environmental sustainability considerations related to building defects have not been linked in previous related work. Therefore, the present study has contributed to filling this gap.

scholarly journals Determining Factors of Community Participation in Waste Bank

2021 ◽  
Vol 940 (1) ◽  
pp. 012085
Author(s):  
C Meidiana ◽  
T Sekito ◽  
W Sasongko
Keyword(s):  
Sustainable Development ◽  
Logistic Regression ◽  
Community Participation ◽  
Community Involvement ◽  
Waste Reduction ◽  
Factors Affecting ◽  
Determining Factors ◽  
Waste Bank ◽  
Waste Separation ◽  
Significant Factors

Abstract The study aims to analyze the factors affecting community involvement in the waste separation and reduction through waste bank for low participation through benefits offered to its member. Logistic regression was used for the analysis, and there were 26 dependents variables applied to the analysis. The result showed that six variables, i.e., age, occupation, participation in social activities, knowledge of sustainable development, distance from HH to WB, and availability of dissemination of 3R, affect people’s motivation to be member and actively involved in waste reduction through the waste bank. All variables except distance are proportional to the motivation of the community to participate in waste bank. The attained model is used to calculate the probability of community participation in waste banks afterward. Using different conditions, where variables are set with values of zero and one representing absence and availability of related variables, respectively various probability values come out ranging from 0% to 94.8% which is the highest probability value if all significant factors are available.

scholarly journals Evaluation of probability of linkages between enterprises and farmer's potatoes in Lam Dong province

2019 ◽  
Vol 18 (01) ◽  
pp. 1-8
Author(s):  
Nam H. Tran
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Multinomial Logistic Regression ◽  
Potato Production ◽  
Farm Size ◽  
Multinomial Logistic Regression Model ◽  
Factors Affecting ◽  
And Gender

In this study, the collaboration between buyer and the famers in potato production was evaluated by using a multinomial Logistic regression model with MLE estimating. The data were collected by directly interviewing of 245 farmers at the Xuan Tho commune, Da Lat city and Don Duong district, Lam Dong province. Results of the research showed that a tight collaboration between the companies and farmers would increase in a higher productivity. The estimation showed that the probability of farmer which would and would not collaborate with buyer were 14.6% (Y2/Y1) and 63.0% (Y3/Y1). The factors affecting the probability of linkages between enterprises and farmers were enterprises and farmers were experience, farm, size, profit, policy supports and gender. The results also revealed that when price of potatoes increase, farmers would not comply with agreement.

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