• Title/Summary/Keyword: multiple regression techniques

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Seasonality and Long-Term Nature of Equity Markets: Empirical Evidence from India

  • SAHOO, Bibhu Prasad;GULATI, Ankita;Ul HAQ, Irfan
    • The Journal of Asian Finance, Economics and Business
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    • v.8 no.4
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    • pp.741-749
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    • 2021
  • The research paper endeavors to investigate the presence of seasonal anomalies in the Indian equity market. It also aims to verify the notion that equity markets are for long-term investors. The study employs daily index data of Sensex, Bombay Stock Exchange, to understand its volatility for the period ranging from January 2001 to August 2020. To analyze the seasonal effects in the stock market of India, multiple regression techniques along with descriptive analysis, graphical analysis and various statistical tests are used. The study also employs the rolling returns at different time intervals in order to understand the underlying risks and volatility involved in equity returns. The results from the analysis reveal that daily and monthly seasonality is not present in Sensex returns i.e., investors cannot earn abnormal returns by timing their investment decisions. Hence, the major finding of this study is that the Indian stock market performance is random, and the returns are efficient. The other major conclusion of the research is that the equity returns are profitable in the long run providing investors a hope that they can make gains and compensate for the loss in one period by a superior performance in some other periods.

Factors Affecting Employee Performance: A Case Study of Railway Maintenance and Engineering Organizations in Thailand

  • POLANANT, Kanut;ROJNIRUTTIKUL, Nuttawut
    • The Journal of Asian Finance, Economics and Business
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    • v.9 no.9
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    • pp.271-281
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    • 2022
  • The objectives of the research are to study the effects of emotional intelligence (EI), reward management (RM), and occupational health and safety (OHS), on employee performance (EP) within a Thai motor service and repair firm. Starting in January 2022 through the end of March 2022, the researchers used simple random sampling techniques to select 88 employees for the case study. The research instrument was a questionnaire with an IOC value between 0.67-1.00 and a reliability value α of 0.78. Survey participants were asked to contribute their opinions to a five-level opinion survey which was hosted on Google Forms. Descriptive statistics analysis (mean and standard deviation) and multiple linear regression analysis were done using SPSS for Windows version 21. The results showed that employee opinions concerning EI, RM, OHS, and EP were at a high level, with the three hypotheses testing showing statistical significance (p ≤ 0.01). The decision coefficients (R2) all revealed relationship strength with RM = 0.861, OHS = 0.853, and EI = 0.731.

Enhancing prediction accuracy of concrete compressive strength using stacking ensemble machine learning

  • Yunpeng Zhao;Dimitrios Goulias;Setare Saremi
    • Computers and Concrete
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    • v.32 no.3
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    • pp.233-246
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    • 2023
  • Accurate prediction of concrete compressive strength can minimize the need for extensive, time-consuming, and costly mixture optimization testing and analysis. This study attempts to enhance the prediction accuracy of compressive strength using stacking ensemble machine learning (ML) with feature engineering techniques. Seven alternative ML models of increasing complexity were implemented and compared, including linear regression, SVM, decision tree, multiple layer perceptron, random forest, Xgboost and Adaboost. To further improve the prediction accuracy, a ML pipeline was proposed in which the feature engineering technique was implemented, and a two-layer stacked model was developed. The k-fold cross-validation approach was employed to optimize model parameters and train the stacked model. The stacked model showed superior performance in predicting concrete compressive strength with a correlation of determination (R2) of 0.985. Feature (i.e., variable) importance was determined to demonstrate how useful the synthetic features are in prediction and provide better interpretability of the data and the model. The methodology in this study promotes a more thorough assessment of alternative ML algorithms and rather than focusing on any single ML model type for concrete compressive strength prediction.

Antecedents of Purchase Decision of Over-The-Counter (OTC) Medicine from Pharmaceutical Distribution Channels in Jordan

  • ALMRAFEE, Mohammad Nabeel Ibrahim
    • Journal of Distribution Science
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    • v.21 no.1
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    • pp.1-12
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    • 2023
  • Purpose: The primary purpose of this research is to understand the potential influence of various factors, namely, pharmacies' recommendations, families' and friend recommendations, price, country of origin, and past experience, on the purchasing decision of nonprescription medicines in the Jordanian context. Research design, data, and methodology: A survey was conducted among 220 Jordanian consumers through a self-administered questionnaire. Further, the authors utilized the mall intercept method as a convenience sampling technique to recruit the respondents who shop at different pharmacies. The data were analyzed using various statistical techniques, such as frequency and percentage for describing the demographics of the sample, Cronbach's alpha for testing the reliability of the data, skewness and kurtosis to check the normality of data, and further, multiple regression using SPSS version 25 was performed for examining the hypotheses. Results: The findings revealed that pharmacists' recommendation, recommendations from friends and family, and price positively influenced consumers' purchase decisions of OTC medicines in Jordan, whereas consumers' past experience and country of origin had no influence on consumers' purchasing decisions of OTC medicines. Conclusions: The paper examines the influence of various factors on customers' purchase decisions of OTC medicines, draws conclusions, and makes recommendations. Also, research limitations are mentioned.

Identification of Supply Chain Management Performance Assessment Criteria for Textile and Apparel Enterprises in Distribution Science

  • Nhu-Mai Thi NONG;Duc-Son HA
    • Journal of Distribution Science
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    • v.22 no.7
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    • pp.73-82
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    • 2024
  • Purpose: This study aims to identify the assessment criteria on textile and apparel supply chain management performance. Research design, data, and methodology: An integrated method of Delphi, quantitative survey, and ANP, in which Delphi with Kamet principle was applied to define the set of criteria, quantitative survey with reliability and validity test was utilized to ensure the match between the set of criteria and the whole textile and apparel industry, and ANP was used to derive weights of these criteria. Results: The set of supply chain management performance evaluation criteria composes of seven criteria namely order fulfillment quality, agility, costs, asset management, information sharing, innovation, and product development and 19 sub-criteria. Conclusions: This study theoretical contribution is the proposition of the set of evaluation criteria on supply chain performance. Regarding practical contribution, the study findings are guidelines for T&A companies in assessing and improving their supply chain capability. However, the findings are only for Vietnamese T&A context. Future research, therefore, may be expanded to other regions or countries' T&A industry. Additionally, future step to this study may be the utilization of other techniques of MCDM or methodological approaches like multiple regression, PLSSEM in defining weights of criteria or performance evaluation.

Assessment of Left Ventricular Function with Single Breath-Hold Magnetic Resonance Cine Imaging in Patients with Arrhythmia

  • Bak, So Hyeon;Kim, Sung Mok;Park, Sung-Ji;Kim, Min-Ji;Choe, Yeon Hyeon
    • Investigative Magnetic Resonance Imaging
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    • v.21 no.1
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    • pp.20-27
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    • 2017
  • Purpose: To evaluate quantification results of single breath-hold (SBH) magnetic resonance (MR) cine imaging compared to results of conventional multiple breath-hold (MBH) technique for left ventricular (LV) function in patients with cardiac arrhythmia. Materials and Methods: MR images of patients with arrhythmia who underwent MBH and SBH cine imaging at the same time on a 1.5T MR scanner were retrospectively reviewed. Both SBH and MBH cine imaging were performed with balanced steady state free precession. SBH scans were acquired using temporal parallel acquisition technique (TPAT). Fifty patients ($65.4{\pm}12.3years$, 72% men) were included. End-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), ejection fraction (EF), myocardial mass, and LV regional wall motion were evaluated. Results: EF, myocardial mass, and regional wall motion were not significantly different between SBH and MBH acquisition techniques (all P-values > 0.05). EDV, ESV, and SV were significant difference between the two techniques. These parameters for SBH cine imaging with TPAT tended to lower than those in MBH. EF and myocardial mass of SBH cine imaging with TPAT showed good correlation with values of MBH cine imaging in Passing-Bablok regression charts and Bland-Altman plots. However, SBH imaging required significantly shorter acquisition time than MBH cine imaging ($15{\pm}7sec$ vs. $293{\pm}104sec$, P < 0.001). Conclusion: SBH cine imaging with TPAT permits shorter acquisition time with assessment results of global and regional LV function comparable to those with MBH cine imaging in patients with arrhythmia.

A Study on Predicting Installation Scale of Photovoltaic Panels and Hydrogen Fuel Storage Facilities to Achieve Net Zero Carbon Emissions Exploiting Idle Sites of Military Bases (군부대 유휴부지를 활용한 탄소 순 배출량 제로 달성을 위한 태양광 패널 및 수소 연료 저장시설의 설치 규모 예측)

  • Donghak Moon;Jiyong Heo
    • Journal of the Korea Institute of Military Science and Technology
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    • v.27 no.1
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    • pp.8-14
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    • 2024
  • In this study, the scale of renewable photovoltaic(PV) panels and hydrogen fuel storage facilities required to achieve "net zero carbon emissions" in military facilities were predicted based on actual electricity consumption. It was set up to expect the appropriate installation size of PV panel and hydrogen fuel storage facility for achieving carbon neutrality, limited to the electricity consumption in the public sector, including national defense and social security administration in Yeongcheon. The experimental results of this paper are largely composed of two parts. First, representative meteorological factors were considered to predict solar power generation in the Yeongcheon area, and solar power generation was estimated through a multiple regression model using deep learning techniques. Second, the size of solar power generation facilities and hydrogen storage facilities in military bases was estimated with the amount of solar power generation and electricity consumption. As a result of this analysis, it was calculated that a site of 155.76×104 m2 for PV panels was needed and a facility capable of storing 27,657 kg of hydrogen gas was required. Through these results, it is meaningful to demonstrated the prospect that military units can lead the achievement of "carbon net zero 2050" by using PV panels and hydrogen fuel storage facilities on idle sites of military bases.

Prevalence and risk factors of helminth infections in cattle of Bangladesh

  • Rahman, A.K.M.A.;Begum, N.;Nooruddin, M.;Rahman, Md. Siddiqur;Hossain, M.A.;Song, Hee-Jong
    • Korean Journal of Veterinary Service
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    • v.32 no.3
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    • pp.265-273
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    • 2009
  • A cross-sectional survey was undertaken to identify risk factors and clinical signs associated with parasitic helminth infections of cattle in Mymensignh district of Bangladesh. A nonrandom convenience sampling method was used to select 138 animals from 40 farmers/herds. The eggs per gram of faeces (epg) for nematodes and trematodes were determined by McMaster and Stoll's methods respectively. Animal-level and herd-level data were recorded by means of a questionnaire. Multi-collinearity amongst explanatory variables were assessed using $2{\times}2{\times}\;X^2$ test and one variable in a pair was dropped if $P{\leq}0.05$ formultiple logistic regression models. Association study between outcome and explanatory variables was conducted using classification tree, random forests and multiple logistic regression. A positive epg was considered as infected. Analyses were performed using $STATA^{(R)}$, version 8.0/Intercooled and $R^{(R)}$, Version 2.3.0. Seventy eight percent of the cattle were found to be infected with at least one type of helminth. Twenty four pairs of combinations of explanatory variables showed significant associations. Male animals (OR=3.3, P=.006, 95% CI=1.4, 7.7) were associated with significantly increased prevalence of nematode infection. Female cattle of the study area are mostly cross-breed, kept indoor, fed relatively good diet and not used for draught purpose. Males are used for draught purpose thereby more exposed to nematode infective stage and provided with relatively poor diet. So stressed male cattle may become more susceptible to nematode infection. All of the three statistical techniques selected gender and lumen motility as most important variables in association with nematode infection in cattle. The result of this survey can only be extrapolated to the periurban cattle population of traditional management system.

Runtime Prediction Based on Workload-Aware Clustering (병렬 프로그램 로그 군집화 기반 작업 실행 시간 예측모형 연구)

  • Kim, Eunhye;Park, Ju-Won
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.38 no.3
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    • pp.56-63
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    • 2015
  • Several fields of science have demanded large-scale workflow support, which requires thousands of CPU cores or more. In order to support such large-scale scientific workflows, high capacity parallel systems such as supercomputers are widely used. In order to increase the utilization of these systems, most schedulers use backfilling policy: Small jobs are moved ahead to fill in holes in the schedule when large jobs do not delay. Since an estimate of the runtime is necessary for backfilling, most parallel systems use user's estimated runtime. However, it is found to be extremely inaccurate because users overestimate their jobs. Therefore, in this paper, we propose a novel system for the runtime prediction based on workload-aware clustering with the goal of improving prediction performance. The proposed method for runtime prediction of parallel applications consists of three main phases. First, a feature selection based on factor analysis is performed to identify important input features. Then, it performs a clustering analysis of history data based on self-organizing map which is followed by hierarchical clustering for finding the clustering boundaries from the weight vectors. Finally, prediction models are constructed using support vector regression with the clustered workload data. Multiple prediction models for each clustered data pattern can reduce the error rate compared with a single model for the whole data pattern. In the experiments, we use workload logs on parallel systems (i.e., iPSC, LANL-CM5, SDSC-Par95, SDSC-Par96, and CTC-SP2) to evaluate the effectiveness of our approach. Comparing with other techniques, experimental results show that the proposed method improves the accuracy up to 69.08%.

Effect of regional emergency medical access on the death rate of elderly individuals with ischemic heart disease (지역별 응급의료접근성이 노인의 허혈성 심장질환 사망률에 미치는 영향)

  • Ko, Eunjung;Cho, Keun-Ja
    • The Korean Journal of Emergency Medical Services
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    • v.25 no.2
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    • pp.19-38
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    • 2021
  • Purpose: This study aimed to investigate the relationship between emergency medical service accessibility in different regions and the sudden death rate in elderly patients with ischemic heart disease using data analysis techniques and suggest improvements in regional emergency medical services. Methods: The study collected data from the NEDIS database and Statistics Korea. Data on a total of 75,867 patients aged ≥65 years were reviewed among patients with ischemic heart disease who visited emergency medical institutions in 2018. Frequency analysis, chi-square test, multiple logistic regression analysis, and simple logistic regression analysis were performed using SPSS PC Window 25.0. Results: With an emergency medical resource per 100km2, there was a concomitant reduction in the risk of death. There was a decrease in the death rate by 0.967, 0.970, 0.997, and 0.391 times with the increase in the presence of a fire department, an ambulance, a paramedic, and a regional medical center, respectively. Furthermore, a decrement in the death rate was witnessed 0.844, 0.825, and 0.975 times with the initiation of a local emergency medical center, a local emergency medical institution, and an angiography device, respectively(p <.001). Conclusion: To improve the accessibility of emergency medical services, the population and geometric area of the region should be considered essential factors when deploying emergency medical resources.