Regression Analysis Approach Research Articles

AUDIT COMMITTEE INDEPENDENCE, DIRECTOR OWNERSHIP, AUDIT QUALITY AND NON-PERFORMING LOANS

The rising trend of Non-Performing Loans (NPLs) in the banking sector of Bangladesh epitomises a common phenomena of loan default that mostly arises from unwillingness to repay tendency of the borrowers who usually fit into the defined higher class of the society. An appropriate governance and control framework can effectively control this growing trend of NPLs, supplemented by the loan default culture. In view of this setting, this paper looks for the impact of audit committee independence, director ownership, external audit quality, CEO power, and bank size to control NPLs. After identifying two endogenous variables in a system of linear equations, this paper employs the system generalized method of moment (GMM) approach of regression analysis. This paper finds the significance of audit committee independence, director ownership, and external audit quality in controlling NPLs. However, this paper finds no significant impact of CEO power on lowering NPLs. Bank Size, measured in relative sense, has non-linear impact on NPLs. This paper recommends that the existing governance guidelines and BASEL-II accords fail to boost up the asset quality that controls NPLs and ensures capital adequacy. Keywords: Bangladesh; banking sector; CEO power; NPLs

Pengaruh Profitabilitas, Makroekonomi, Firm Size Terhadap Financial Distress Dengan Nilai Perusahaan Sebagai Variabel Moderating

One of the key contributors to the country's economic health is the banking industry. From 2017 to 2021, there were eight banking companies that were liquidated. So it is necessary to anticipate from the beginning so that bankruptcy does not occur. One way to see if a company is in financial distress can be done using the Altman's Z-Score method. This investigation was carried out with the intention of measuring and knowing the extent to which profitability, macroeconomic, and firm size ratios are proxied by Return On Assets (ROA), inflation, and total assets to financial distress using the Altman's Z-Score method through a moderating variable in the form of firm value proxied by PBV. This is a quantitative study that relies on secondary data gathered through a literature review. The population of this study is the whole banking industry in Indonesia, with a total sample of 23 enterprises acquired using the purposive sampling approach. This study employs panel data using the Moderated Regression Analysis (MRA) approach, which is carried out using Econometric Views (Eviews) version 10. According to the findings of this study, profitability had a favorable influence on financial distress, but macroeconomics, business size, and firm value had no effect. Meanwhile, firm value is deemed unable to moderate the independent variable on the dependent variable in this study, so it is unknown whether firm value increases or weakens the independent variable on the dependent variable.

The Mediating Role of Financial Accountability in the Relationship between Internal Control Environment and Financial Performance of Savings and Credit Cooperatives in Mid-Western Uganda

Purpose: This paper aimed at establish the mediating effect of financial accountability in the relationship between internal control environment and financial performance of savings and credit cooperatives (SACCOs). The purpose of this paper is to establish the mediating role of financial accountability in the association between internal control environment and financial performance.
 Methodology: A cross-sectional research design and positivist paradigm were used to collect data using a close-ended questionnaire. Also, a standard regression analysis, Medgraph program, and Baron and Kenny approach were adopted to test for the mediation effects. Based on the target population of 122 SACCOs, a sample of 93 SACCOs was selected using probability sampling technique.
 Findings: It is clearly revealed that the true drivers of financial performance in the SACCO sector are financial accountability and internal control environment. Nevertheless, financial accountability exhibits a partial form of mediation in the association between internal control environment and financial performance.
 Unique Contribution to Practice and Policy: In the event that financial accountability is established as a conduit in the relationship between internal control environment and financial performance, managers in the SACCO sector should work on reinforcing the agents of financial accountability (i.e., value for money and level of transparency) in an effort to enhance sustainable financial performance.

PENGARUH KUALITAS PELAYANAN, CITRA MEREK, DAN LOKASI TERHADAP KEPUTUSAN MENGINAP DI HOTEL MERCURE JAKARTA GATOT SUBROTO

This study aims to analyze and determine the effect of service quality, brand image, and location on the decision to stay at the Mercure Jakarta Gatot Subroto Hotel. The population in this study is the consumer of the Mercure Jakarta Hotel Gatot Subroto who has stayed at least 1 time. The data used in this study is primary data. The sample in this paper is 100 respondents at the Mercure Hotel Jakarta Gatot Subroto using accidental sampling technique. The research method used is quantitative and uses multiple linear regression analysis approach. The results of this study state that partially the Service Quality and Brand Image variables have no significant effect on the Decision to Stay at the Mercure Jakarta Gatot Subroto Hotel, while the Location Variable has a significant effect on the Decision to Stay at the Mercure Jakarta Gatot Subroto Hotel. And simultaneously the variables of Service Quality, Brand Image and Location have a significant effect on the dependent variable, namely the decision to stay.

Interplay of renewable energy investment efficiency, shareholder control and green financial development in China

The study aims to test the interplay of renewable energy investment efficiency, shareholder control, and green financial development in the context of China. To estimate the connections regression analysis approach and the Richardson model are used. This model is specifically used to assess China's renewable energy investment efficiency. The study's findings suggest that renewable energy companies and green finance development assist in alleviating investment shortages. Moreover, shareholder control has a significant connection with both the study constructs. Intermediary effects between short- and long-term bank loans account for 9.38% of the overall impact. Green finance development has a role in the efficiency of renewable energy investments via short- and long-term bank loans. The study analysis further showed that the annual growth rate was roughly 1%, lower in the Chinese context. Around 4% yearly growth in 2012–2013 indicated that the economy started a strong development period. Green finance development in China was found to have a negative influence on bank loan issuance and hindered the efficiency of renewable energy investment to some degree in 2018, yet the amount of development was marginally decreased with an effective degree of 0.17. Moreover, a risk factor is also significant that green financing growth may increase under-investment in renewable energy enterprises, although reducing credit availability mitigates this risk. According to this study, to complete and build a green financial development system, government should use rules and regulations. Financial institutions and renewable energy firms need to work together to enhance their management and construct financing channels to support the growth of renewable energy enterprises. However, multiple practical implications are recommended on it.

Traffic crash changes following transitions between daylight saving time and standard time in the United States: New evidence for public policy making

Introduction: This study examined the impact of daylight saving time changes on traffic crashes in the United States. Using a data-driven regression analysis approach, the study analyzed 2014–2016 crash data from six states spanning all four major time zones in the contiguous United States. Method: The researchers developed regression models for a number of analysis scenarios and by days of week separately. Based on the crash data, the team used an eight-week impact period for the spring time change and a four-week impact period for the fall time change. Results: The regression analysis showed that the spring time change was followed by an overall crash reduction of 18% during the eight-week period immediately after the time change, with a considerably higher crash reduction for freeway crashes (24%), rural crashes (24%), or non-intersection crashes (21%). The time change back to SDT in fall was followed by a 6% overall crash increase during the following four weeks, with considerably higher increases for freeway crashes (15%), non-intersection crashes (9%), nighttime crashes (28%), single-vehicle crashes (28%), and crashes in urban areas (12%). This study provides additional insights, including in many cases more comprehensive knowledge on how the changes to and from DST each year affect roadway traffic crashes. Practical Applications: The findings of this project further add to the current understanding on how the time changes affect public health in the form of traffic crashes. They also serve as additional evidence for public policy makers to better weigh the benefits and impacts associated with the time changes in the United States for relevant policy makings.

Asymmetric volatility dynamics in cryptocurrency markets on multi-time scales

This study investigates the scale-dependent structure of asymmetric volatility effect in six representative cryptocurrencies: Bitcoin, Ethereum, Ripple, Litecoin, Monero, and Dash. By developing the dynamical approach of DFA-based fractal regression analysis, we detect whether the volatility of price changes is positively or negatively related to return shocks at different time scales. We find that the asymmetric volatility phenomenon varies by scale and cryptocurrency, and the structure is time-varying. Contrary to what is typically observed in equity markets, minor currencies show an “inverse” asymmetric volatility effect at relatively large scales, where positive shocks (good news) have a greater impact on volatility than negative shocks (bad news). The consequences are discussed in the context of who is trading in the market and heterogeneity of the investors.

Notre nom, un marqueur d’identité

Vaccinations are considered the major tool to curb the current SARS-CoV-2 pandemic. A randomized placebo-controlled trial of the BNT162b2 vaccine has demonstrated a 95% efficacy in preventing COVID-19 disease. These results are now corroborated with statistical analyses of real-world vaccination rollouts, but resolving vaccine effectiveness across demographic groups is challenging. Here, applying a multivariable logistic regression analysis approach to a large patient-level dataset, including SARS-CoV-2 tests, vaccine inoculations and personalized demographics, we model vaccine effectiveness at daily resolution and its interaction with sex, age and comorbidities. Vaccine effectiveness gradually increased post day 12 of inoculation, then plateaued, around 35 days, reaching 91.2% [CI 88.8%-93.1%] for all infections and 99.3% [CI 95.3%-99.9%] for symptomatic infections. Effectiveness was uniform for men and women yet declined mildly but significantly with age and for patients with specific chronic comorbidities, most notably type 2 diabetes. Quantifying real-world vaccine effectiveness, including both biological and behavioral effects, our analysis provides initial measurement of vaccine effectiveness across demographic groups.

Forecasting of Total Column Ozone using Regression Analysis and LSTM-RNN Machine Learning Approach

Objectives: Atmospheric Ozone plays an important role in global climate change, human health and environmental conditions. Accurate and timely prediction of variation in Total Column Ozone (TCO) concentration is very important for both climatology and environment. In this context, the present study aims to utilize the advancement of Artificial Intelligence in Machine learning and a conventional method to develop models for the prediction of TCO concentration using historical time series data over a tropical region, India. Methods: Long Short-Term Memory (LSTM) deep learning networks are very useful in classifying, processing and making predictions based on time series data. In this work, Multiple Linear Regression (MLR) model, Long Short-Term Memory (LSTM) models are developed for forecasting TCO over a tropical region. The Statistical Parameters such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) were used to analyze and evaluate the performance of the proposed models. Findings: In the present study the TCO concentration varied between 217.4 DU and 288.6 DU. The performances of MLR and LSTM model were compared. From the results, it is evident that the values of RMSE, MAE and MAPE of the MLR model are 3.807924, 2.93766, 1.15 respectively whereas the values for LSTM model are 3.074492, 2.574, 1.01 respectively. LSTM was found to be the accurate model for the study data set and the predicted TCO concentration has a very good correlation with the actual observations and it does not rely on the meteorological parameters. Novelty: The LSTM models are usually employed in the field of machine translation, speech recognition and for the prediction of traffic levels, stock levels and air pollutants. This work utilizes the fast numerical machine learning computing library Tensorflow and a high level neural network library Keras that runs on top of Tensorflow to develop a LSTM network model for the prediction of the concentration levels of TCO. Keywords: Total Column Ozone; Regression; Forecasting; Climate Change; Long Short-Term Memory

Analisis peran kompetensi karyawan dan kualitas layanan rumah sakit dalam peningkatan kepuasan pasien

Advances in digital technology today have brought significant changes in various aspects, especially in the health care industry business. The purpose of this study was to examine the effect of employee competence and the quality of hospital services on patient satisfaction in hospitals in the city of Bandung. This research was conducted in a hospital in the city of Bandung using a sample of 90 patients. The data was then analyzed using a multiple regression analysis approach using SPSS 25 software. The results of this study indicate that partially and simultaneously the variables of employee competence and service quality have a significant influence on patient satisfaction. Based on the results of the coefficient of determination obtained a value of 87.9%. These results indicate that the two variables studied, namely employee competence and service quality have a high enough influence in influencing patient satisfaction at Bandung City Hospital.

Analysis of the Implementation of Enterprise Resources Plan (ERP) on Company Performance (Case Study of PT Sapta Sentosa Jaya Abadi)

PT Sapta Sentosa Jaya Abadi is a plantation company located in four different provinces. The company operates the oil palm business (cultivation and processing factory). The company was founded in 2002. Previously the company relied on manual operation in every of their administration, realizing the importance of building an information system based on Enterprise Resources Plan (ERP), the company collaborated with third parties s developed ERP which they called Go Palms. Indeed, Go Palm makes work easier and ultimately improves the company's performance, but performance data from 2016 to 2020, actually shows a downward trend. Lack of socialisation, makes employees as users find it difficult both in adapting and using information systems in their work to find out how the role of information systems drives performance, the authors researched the role of ERP on company performance through Quality of Information Systems. The research method used is quantitative with the Regression Analysis Approach as a data processing method. The research was conducted on all 10ERP users. The results show that: 1) there is a simultaneous influence of Go Palm's Information System Quality on the Company's Performance; 2) There is a partial effect of Product Operation on Company Performance; 3) there is no partial effect of Product Revision on Company Performance, and; 4) There is a partial effect of Product Transition on Company Performance at PT. Sapta Sentosa Jaya Abadi. The suggestion in this study is that management needs to carry out socialization and training related to the use of the ERP system, so the employees know why they need to do work using an ERP information system and also the benefits they can get. Keywords: Enterprise Resources Plan (ERP), Performance Appraisal Management, Quality of Information System.

Research on Chlorophyll-a Concentration Retrieval Based on BP Neural Network Model—Case Study of Dianshan Lake, China

The Chlorophyll-a (Chl-a) concentration is an important indicator of water environmental conditions; thus, the simultaneous monitoring of large-area water bodies can be realized through the remote sensing-based retrieval of the Chl-a concentrations. The back propagation (BP) neural network learning method has been widely used for the remote sensing retrieval of water quality in first and second-class water bodies. However, many Chl-a concentration measurements must be used as learning samples with this method, which is constrained by the number of samples, due to the limited time and resources available for simultaneous measurements. In this paper, we conduct correlation analysis between the Chl-a concentration data measured at Dianshan Lake in 2020 and 2021 and synchronized Landat-8 data. Through analysis and study of the radiative transfer model and the retrieval method, a BP neural network retrieval model based on multi-phase Chl-a concentration data is proposed, which allows for the realization of remote sensing-based Chl-a monitoring in third-class water bodies. An analysis of spatiotemporal distribution characteristics was performed, and the method was compared with other constructed models. The research results indicate that the retrieval performance of the proposed BP neural network model is better than that of models constructed using multiple regression analysis and curve estimation analysis approaches, with a coefficient of determination of 0.86 and an average relative error of 19.48%. The spatial and temporal Chl-a distribution over Dianshan Lake was uneven, with high concentrations close to human production and low concentrations in the open areas of the lake. During the period from 2020 to 2021, the Chl-a concentration showed a significant upward trend. These research findings provide reference for monitoring the water environment in Dianshan Lake.

Projections of urban built-up area expansion and urbanization sustainability in China's cities through 2030

In the context of socioeconomic initiatives to address world development sustainability such as the Sustainable Development Goals (SDGs), China's urbanization with increasing intensity and quickening pace has always been central to debates over the past few decades. Although the United Nations (UN) recommend to estimate and predict the local-scale urbanization sustainability by using the SDG indicator 11.3.1: “ratio of land consumption rate to population growth rate (LCRPGR)”, there remains a lack of reliable projections and applicable assessments at the city level, especially for the future urban expansion. Until now, limited knowledge of the urbanization sustainability dynamics in the next decade has been the primary challenge to inform the realization of urban sustainable development goal under the framework of SDGs. To evaluate the geographic region- and population size-specific urbanization sustainability at the city level in mainland China between 2020 and 2030, an integrated downscaling approach of trend extrapolation and regression analysis was developed based on historical statistics of urban built-up area and subnational scenarios of population and gross domestic product (GDP) under the Shared Socioeconomic Pathways (SSPs). The SSPs-consistent urban built-up area projections across different scales showed that the logistic regression had a better modeling performance than the panel data regression, while there was little difference between the linear and exponential extrapolation methods. Despite the geographical heterogeneity and size-related disparity, the scenario modeling results suggested that the average value of LCRPGR indicator in mainland China was expected to decline continuously, and would reach at the ideal state with a coordination between urban expansion and population growth through 2030. Specifically, the population growth stagnation of cities in Northeast China and the allometric urban expansion of cities in Western China should be the focus of strengthened regulations, and the urban growth of megacities would experience a steady and slower trend compared to their surrounding super-large cities and large cities. In summary, China's urbanization process might have headed toward a more sustainable and coordinated future under feasible SSPs scenarios. The local-scale forecasts will not only fill the data gap of SSPs-consistent urban land demand for all county- and prefecture-level cities in mainland China, but can also provide a reference for forecasting and assessment of urbanization sustainability to meet the SDGs in urban areas such as indicator 11.3.1.

Deteksi Batimetri Perairan Dangkal di Pulau Menjangan, Provinsi Bali Menggunakan Citra Landsat

Remote sensing-based research in Indonesia using satellite imagery frequently faces the challenge of cloud coverage due to the tropical country. One spatial data that can be extracted from satellite imagery is bathymetry. However, cloud-covered water bathymetric extraction still needs to be examined. This study aims to understand the ability of Landsat 7 ETM+ acquired on 29 July 2013, and Landsat 8, acquired on 24 July 2020, as the representative of non-cloudy image compared to Landsat 8, acquired on 9 August 2020, as the cloudy image. Stumpf algorithm was applied, including a statistical approach of linear regression analysis with in-situ data measurement from Single Beam Echo-Sounder (SBES) to derive the absolute bathymetric map with several classes of depth ranging from 0 – 2 m up to 10 m. To assess the accuracy, RMSE and confusion matrix was used. The result shows that Landsat 7 ETM+ yields the highest R2 with 0,52, while the lowest total RMSE (8,167 m) and highest overall accuracy of about 69% from the confusion matrix was achieved by the cloudy image of Landsat 8. Nevertheless, the highest absolute depth value yield by Landsat 8 non-cloudy image with 16,1 m. This research confirms that the highest R2 value does not always produce the best model, but it is still promised to be used. Furthermore, the quality of the imagery based on its percentage of cloud coverage is affecting the resulted model.

Simulation of Phenol and Chlorophenol Removal Using Combined Adsorption and Biodegradation: Regression Analysis and Data-Mining Approach

Simulation of Phenol and Chlorophenol Removal Using Combined Adsorption and Biodegradation: Regression Analysis and Data-Mining Approach

Mapping of the silica-rich rocks and serpentinites using newly defined thermal indices from Advanced Spaceborne Thermal Emission and Reflection Radiometer thermal infrared data of Udaipur-Rakhabdev region, Rajasthan, India

Advanced Spaceborne Thermal Emission and Reflection Radiometer L1T data were used for the derivation of the new indices for the identification and mapping of the silica-rich and mafic-ultramafic rocks of the Udaipur-Rakhabdev region. The newly derived silica-rich rock index Ronak Jain (QI-RJ) was used in four bands except band 10 as [((band 13 – sqrt(band 11))/band 14)/band 12]. The mafic-rich rocks were also defined by a new index (MI-RJ), as (band 10 + band 14) / band 13. The new indices had advantages in accurate mapping compared with those given by Ninomiya (QI-N), Rockwell and Hofstra (QI-RH), and Guha and Vinod Kumar (QI-AV) for mapping of silica-rich rocks by removing striping noise. Similarly, mafic minerals were mapped by indices given by Ninomiya (MI-N) and Guha and Vinod Kumar (MI-AV) for ultramafics, but they have not mapped the discontinuous outcrops of serpentinites. The statistical regression analysis approach was used for the prediction of the goodness of fit between the new indices and already existing indices. The silica index (QI-RJ) is highly correlated with the QI-RH and QI-AV and represents low values for QI-N. In a similar way, MI-RJ shows a high correlation with the MI-N and MI-AV indices. The regression analyses for the QI-RJ with MI-RJ, MI-N, and MI-AV, show a negative trend but have high values of regression correlation. The generated maps from the newly defined index were also compared with the indices maps generated from the existing indices and were found to have better maps of litho-units. Field collected data and petrological studies also validated the presence of silica-rich rocks and ultramafics of the region mapped with the help of newly defined indices, and a better spatial accuracy of maps was obtained. The new mafic index was more suitable for mapping serpentinites and their alteration products exposed in the area.

Analysis of the major factors that effect power consumption: A multiple regression analysis approach

The erratic power supply in recent times has been a matter of great concern not only to the consumers but also to the Power Holding Company of Nigeria (PHCN) organization itself. The nagging problem is compounded by the spasmodic nature of the phenomenon and hence calls for a special scientific investigation. The paper uses a multiple regression analysis technique to investigate the major factors that affect power Consumption in Jos Metropolis – traditionally tin-mining Camp. On the initial assumption that erratic power supply can only be due to the disparity between supply and demand. The paper sets out to answer the following two questions; i) what factors have been responsible for such increase in demand and the behaviour of the relationship that exist between these factors; ii) what likely measures are to be taken to restore the balance between demand and supply. We were able to identify the major factors as Residential Industrial and Commercial. Some interesting relationships were established.

Energy and ICT Tax Effects on Foreign Direct Investment in a Low-Income Economy

The importance of excellent tax policies in increasing foreign direct investment inflows should be stressed in all growing economies. Foreign direct investment (FDI) is critical to increasing productivity, particularly in developing nations. Taxes emanating from energy related business have also triggered this inquiry due to fumes being contended within the environment and the effect on human existence. Prior researchers have investigated a variety of issues including trade liberalisation, property taxes, market shares, corporate taxation, and rising prices. Fewer researchers have examined the tax implications of energy and information and communication technology (ICT) development as a predictor of FDI in low-income countries. Following the introduction of taxes on energy and ICT activities in Nigeria, foreign investments’ responses have not been tried out in studies and yet the dwindling level has been an issue of policy concern. As a result, this study seeks to fill the gaps by evaluating the effects of energy and ICT taxes on FDI from 2010 to 2020. The data applied for this analysis are obtained from World Bank, Federal Inland Revenue Service (FIRS) and Central Bank of Nigeria. Considering the outcome of this investigation, the paper concludes that the ICT development tax is detrimental to FDI inflows using the econometric approach of regression analysis. The correlational analysis also provides evidence that ICT taxation has a strong negative association with FDI. Other factors, such as trade openness and energy taxes, neither have a substantial relationship nor impact on FDI. The study indicates that improving policies to minimise ICT taxation will benefit the expanding economy by recruiting new foreign investors and retaining those who are currently present in the country

THE INFLUENCE OF THE WORKING CLIMATE AND MOTIVATION ON THE PERFORMANCE OF EMPLOYEES IN THE MARINE AND FISHERIES SERVICE BOALEMO COUNTY

The objectives of this study are: 1) to find out how much influence partially the working climate has on the performance of employees in the Boalemo District Marine and Fisheries Service; 2) to find out how much influence partially motivates the performance of employees in the Boalemo District Marine and Fisheries Service; 3) to know how much influence simultaneously the working climate and motivation on the performance of employees in the Boalemo District Marine and Fisheries Service.
 The quantitative descriptive research method approach with the type of survey research that explains the causal relationship and performs hypothesis testing using a multiple regression analysis approach.
 The results showed that:1) the working climate has a positive and significant effect on the performance of employees in the Boalemo District Marine and Fisheries Service with an influence contribution of 55%. This means that the higher the working climate, the higher the performance of employees in the Boalemo District Marine and Fisheries Service; 2) motivation negatively and significantly affects the performance of employees in the Boalemo District Marine and Fisheries Service with an influence contribution of 72%. This means that the higher the motivation, the lower the performance of employees in the Boalemo District Marine and Fisheries Service; 3) simultaneously the influence of the working climate and motivation will have a positive and significant effect on the performance of employees in the Marine and Fisheries Service with a 59% contribution. This means that if the working climate and motivation are applied simultaneously it will further improve the performance of its employees.

Driving forces of population change following the Canterbury Earthquake Sequence, New Zealand: A multiscale geographically weighted regression approach

AbstractThe Canterbury Earthquake Sequence (CES), which includes the 2010 and 2011 Christchurch earthquakes, is one of the deadliest disasters in New Zealand history. Following the CES, displacement of the affected population occurred, leading to an out‐migration from affected areas and changes to places of residence. This paper investigates the spatial changes in population following the CES, using a multiscale geographically weighted regression (MGWR) analysis approach to examine if there is a relationship between population change within the Canterbury region, and potential driving forces across two time periods: 2006–2013 and 2013–2018. The findings of this study could assist in informing future decision making and planning for earthquake events and to increase the effectiveness of land use policy decisions for post‐disaster recovery in New Zealand.