Multiple Linear Regression Approach Research Articles

Fintech's influence on green credit provision: Empirical evidence from China’s listed banking sector

We explore the impact of financial technology (fintech) advancements on green credit provision, investigating publicly traded banks in China from 2007 to 2022. We particularly focus on credit modelling innovation, examining the non-linear dynamics between fintech evolution and green credit distribution. Results reveal a positive U-shaped correlation. Initial stages of fintech are associated with increased green credit risk, negatively affecting the volume of green credit. However, more established fintech infrastructures significantly enhance green credit volumes by improving resource allocation and credit risk assessment. Utilizing a multiple linear regression approach, we highlight the transformative nature of fintech in advancing sustainable banking practices, particularly through innovations in credit modeling that enhance green credit risk management and resource allocation efficiency.

Pengaruh Live Streaming , Flash Sale , dan Cashback Terhadap Perilaku Impulse Buying pada Pengguna E-Commerce Shopee di Surabaya

E-commerce is one of the newest trends in online purchasing that customers experience as a result of digital marketing. In September 2023, Shopee surpassed all other e-commerce platforms in Indonesia in terms of popularity. A number of Shopee promotional features, including live streaming, flash sales and cashback, can stimulate impulse purchases. The aim of this research is to determine the simultaneous and partial influence of live streaming, flash sales and cashback on Shopee e-commerce impulse buying behavior. An associative methodology combined with a quantitative approach was used in this research. The sampling strategy is the use of tactical purposive sampling combined with non-probability sampling. The questionnaire contains 100 responses aged between 18 and 40 years which were distributed via Google Forms as part of the data collection method, are residents of Surabaya and have made several purchases at Shopee. A multiple linear regression approach was then used to evaluate the data, and F and t tests were used to assess the hypothesis. The SPSS 29 test findings in this research show that positively and significantly live streaming, flash sales and cashback all have a big impact on impulse buying behavior at the same time or simultaneously. Meanwhile, partially live streaming (X1) contributed 54.1%, flash sales (X2) contributed 40.2%, and cashback (X3) contributed 71.3%, had a positive and significant effect on impulse buying behavior (Y). for Shopee e-commerce users in Surabaya.

Predicting Personal Exposure to PM2.5 Using Different Determinants and Machine Learning Algorithms in Two Megacities, China

The primary aim of this study is to explore the utility of machine learning algorithms for predicting personal PM2.5 exposures of elderly participants and to evaluate the effect of individual variables on model performance. Personal PM2.5 was measured on five consecutive days across seasons in 66 retired adults in Beijing (BJ) and Nanjing (NJ), China. The potential predictors were extracted from routine monitoring data (ambient PM2.5 concentrations and meteorological factors), basic questionnaires (personal and household characteristics), and time-activity diary (TAD). Prediction models were developed based on either traditional multiple linear regression (MLR) or five advanced machine learning methods. Our results revealed that personal PM2.5 exposures were well predicted by both MLR and machine learning models with predictors extracted from routine monitoring data, which was indicated by the high nested cross-validation (CV) R2 ranging from 0.76 to 0.88. The addition of predictors from either the questionnaire or TAD did not improve predictive accuracy for all algorithms. The ambient PM2.5 concentrations were the most important predictor. Overall, the random forest, support vector machine, and extreme gradient boosting algorithms outperformed the reference MLR method. Compared with the traditional MLR approach, the CV R2 of the RF model increased up to 7% (from 0.82±0.13 to 0.88±0.10), while the RMSE reduced up to 18% (from 19.8±5.4 to 16.3±4.5) in BJ.

Evaluating Machine Learning-Based Approaches in Land Subsidence Susceptibility Mapping

Land subsidence (LS) due to natural and human-driven forces (e.g., earthquakes and overexploitation of groundwater) has detrimental and irreversible impacts on the environmental, economic, and social aspects of human life. Thus, LS hazard mapping, monitoring, and prediction are important for scientists and decision-makers. This study evaluated the performance of seven machine learning approaches (MLAs), comprising six classification approaches and one regression approach, namely (1) classification and regression trees (CARTs), (2) boosted regression tree (BRT), (3) Bayesian linear regression (BLR), (4) support vector machine (SVM), (5) random forest (RF), (6) logistic regression (LogR), and (7) multiple linear regression (MLR), in generating LS susceptibility maps and predicting LS in two case studies (Semnan Plain and Kashmar Plain in Iran) with varying intrinsic characteristics and available data points. Multiple input variables (slope, aspect, groundwater drawdown, distance from the river, distance from the fault, lithology, land use, topographic wetness index (TWI), and normalized difference vegetation index (NDVI)), were used as predictors. BRT outperformed the other classification approaches in both case studies, with accuracy rates of 75% and 74% for Semnan and Kashmar plains, respectively. The MLR approach yielded a Mean Square Error (MSE) of 0.25 for Semnan plain and 0.32 for Kashmar plain. According to the BRT approach, the variables playing the most significant role in LS in Semnan Plain were groundwater drawdown (20.31%), distance from the river (17.11%), land use (14.98%), NDVI (12.75%), and lithology (11.93%). Moreover, the three most important factors in LS in Kashmar Plain were groundwater drawdown (35.31%), distance from the river (23.1%), and land use (12.98%). The results suggest that the BRT method is not significantly affected by data set size, but increasing the number of training set data points in MLR results in a decreased error rate.

Development of a Predictive Model to Correlate the Chemical Structure of Amines with Their Oxidative Degradation Rate in a Post-Combustion Amine-Based CO2 Capture Process Using Multiple Linear Regression and Machine Learning Regression Approaches.

In this study, the degradation behavior of 30 different amines was investigated, which were categorized into four distinct groups: alkanolamines, sterically hindered alkanolamines, multialkylamines, and cyclic amines. These experiments were conducted over a period of 14 days at a temperature of 60 °C, with a feed gas comprising 99.9% O2 flowing at a rate of 200 mL/min. The primary objective was to establish a correlation between the chemical structures of these amines and their susceptibilities to degradation. To assess this, the concentration of the amines at various time points was measured to determine their degradation rates. Results showed that secondary amines exhibited degradation rates higher than those of primary and tertiary amines. Amines with cyclic structures demonstrated lower oxidative degradation rates. Longer alkyl chain lengths decreased degradation rates in all amine types because of their electronic and steric hindrance properties. A higher number of hydroxyl groups increased the degradation rate by destabilizing the free radical. An increase in hydroxyl groups in nonsterically hindered amines increased the degradation rate by decreasing free radical stability. In contrast, for sterically hindered amines, an increase in hydroxyl groups decreased the degradation rate because the steric hindrance effect is now more dominant than the electron-withdrawing effect. An increase in the number of amino groups led to higher degradation rates due to the presence of more reactive sites for free radical formation. Amines with tert-alkyl groups exhibited higher degradation rates than those with straight chains. Moreover, branched alkyl groups located between amino and hydroxyl groups significantly increased the degradation rates. Two degradation models, a semiempirical statistical model and a CatBoost machine learning regression model, were developed to predict amine degradation rates based on their chemical structure and relevant properties. To train these models, a data set of 27 different amines was used, while another set of 3 amines was reserved for testing the model's predictive performance. The average absolute deviations (AAD) achieved were, respectively, 22.2 and 0.3%.

Enhancing rainfall estimation for water resource management through multiple linear regression approach in Khordha District, Odisha, India

Enhancing rainfall estimation for water resource management through multiple linear regression approach in Khordha District, Odisha, India

Modeling the feasibility of Se-rich corn cultivation in Se-deficient agricultural fields using random forest algorithm.

Selenium constitutes an essential trace element for the human body. Moderate Se intake plays a pivotal role in preserving overall health. The absorption of Se by plants is primarily influenced by the available Se levels in soils, rather than by the soil total Se content, offering potential for exploring Se-rich crops in Se-deficient regions. In this study, we explore the factors influencing the Se bioaccumulation coefficient in corn based on a land quality geochemical survey at a 1:50,000 scale and establish predictive models for corn seed Se content using random forest and multiple linear regression approaches. The results indicate that the surface soil in the study area is deficient in Se (0.18-1.21mg/kg), but 54% of the corn grain samples met the standards for Se-rich products (0.02-0.30mg/kg). The factors influencing the Se biological enrichment coefficient in corn seeds are soil pH and CaO and MgO content, with impact levels of 0.54, 0.42, and 0.35, respectively. Compared to multiple linear regression models, the RF model provides more accurate and reliable predictions of corn Se content. The random forest model indicates that approximately 41% of the farmland within the study area is conducive to the cultivation of naturally Se-rich corn, which is a 26% increase in the planting area compared to recommendations based solely on soil Se content. In this research, we introduce an innovative methodological framework for organically cultivating naturally Se-rich corn within regions affected by Se deficiency.

Exploring the Relation between Contextual Social Determinants of Health and COVID-19 Occurrence and Hospitalization

It is prudent to take a unified approach to exploring how contextual social determinants of health (SDoH) relate to COVID-19 occurrence and outcomes. Poor geographically represented data and a small number of contextual SDoH examined in most previous research studies have left a knowledge gap in the relationships between contextual SDoH and COVID-19 outcomes. In this study, we linked 199 contextual SDoH factors covering 11 domains of social and built environments with electronic health records (EHRs) from a large clinical research network (CRN) in the National Patient-Centered Clinical Research Network (PCORnet) to explore the relation between contextual SDoH and COVID-19 occurrence and hospitalization. We identified 15,890 COVID-19 patients and 63,560 matched non-COVID-19 patients in Florida between January 2020 and May 2021. We adopted a two-phase multiple linear regression approach modified from that in the exposome-wide association (ExWAS) study. After removing the highly correlated SDoH variables, 86 contextual SDoH variables were included in the data analysis. Adjusting for race, ethnicity, and comorbidities, we found six contextual SDoH variables (i.e., hospital available beds and utilization, percent of vacant property, number of golf courses, and percent of minority) related to the occurrence of COVID-19, and three variables (i.e., farmers market, low access, and religion) related to the hospitalization of COVID-19. To our best knowledge, this is the first study to explore the relationship between contextual SDoH and COVID-19 occurrence and hospitalization using EHRs in a major PCORnet CRN. As an exploratory study, the causal effect of SDoH on COVID-19 outcomes will be evaluated in future studies.

Macroeconomic Factors of Stock Market Development (The Case of Saudi Arabia)

Most economists recognize that a healthy capital market can enhance economic growth. In 2016, Saudi Arabia launched Vision 2030, and the Financial Sector Development Program is part of the Vision. The program aims to achieve economic diversification, reduce oil dependence, and trigger economic growth. Capital market development has an integral part to play in achieving Vision 2030 goals. For this reason, it is essential to investigate the factors that could influence it. The literature on the impact of macroeconomic factors on the stock market development is inconclusive; thus, it is difficult to generalize the outcome of previous studies. This research investigates the macroeconomic and institutional determinants of stock market development in Saudi Arabia from 2008 to 2021 by using a multiple linear regression approach. Following previous research, our analysis explores the impact of several factors, including Global Trade Openness, Market Depth, Macroeconomic Stability, Control of Corruption, Income level, Private capital flows, Financial intermediary development, Economic Development, Domestic Savings and investment. Among the nine evaluated factors, only four were significant. We find that the determinants of stock market development in Saudi Arabia are Economic Development, Income level, Control of corruption, and Market Depth.

Correlating mechanical and rheological filament properties to processability and quality of 3D printed tablets using multiple linear regression

Filament formulation for FDM is a challenging and time-consuming process. Several pharmaceutical polymers are not feedable on their own. Due to inadequate filament formulation, 3D printed tablets can also exhibit poor uniformity of tablet attributes. To better understand filament formulation process, 23 filaments were prepared with the polymer mixing approach. To yield processable filaments, brittle and pliable polymers were combined. A 20 % addition of a pliable polymer to a brittle one resulted in filament processability and vice versa. Predictive statistical models for filament processability and uniformity of tablet attributes were established based on the mechanical and rheological properties of filaments. 15 input variables were correlated to 9 responses, which represent filament processability and tablet properties, by using multiple linear regression approach. Filament stiffness, assessed by indentation, and its square term were the only variables that determined the filament’s feedability. However, the resulting model is equipment-specific since different feeding mechanism exert different forces on the filaments. Additional models with good predictive power (R2pred > 0.50) were established for tablet width uniformity, drug release uniformity, tablet disintegration time uniformity and occurrence of disintegration, which are equipment-independent outputs. Therefore, the obtained model outcomes could be used in other research endeavours.

Determinants of capital adequacy ratio bank in Indonesia

Banking as a delegate establishment assumes a significant part in the economy and performs well and is sounds. The Capital Adequacy Ratio (CAR) is a measure of how important capital is to banking. OJK has set a new classification for banks based on their core capital. The objective of this research is to ascertain the factors that influence the Capital Adequacy Ratio (CAR) of Indonesian stock exchange-listed banks. The multiple linear regression approach is utilized in this study. The Capital Adequacy Ratio (CAR) is the dependent variable, and the Return on Assets, Loan-to-Deposit Ratio, Net Interest Margin, Operating Expenses, and Operating Income are the independent variables. In view of the exploration results, there is a huge impact of Return On Resources (ROA), Net Interest Edge (NIM), Working Expenses of Working Pay (BOPO), and Credit to Store Proportion (LDR) on the Capital Sufficiency Proportion.

Deciphering Financial Indicators: Analyzing the Role of Current Ratio, Debt to Equity Ratio, and Net Profit Margin in Shaping Stock Prices of Pharmaceutical Companies on the Indonesian Stock Exchange (2020-2022)

This study was designed to assess how the current ratio, debt to equity ratio, and net profit margin affect the stock prices of pharmaceutical firms listed on the Indonesian stock exchange from 2020 to 2022. Seven companies were chosen using purposive sampling for this analysis. The study was based on secondary data, such as the firms' annual financial reports and stock prices. A multiple linear regression approach was used for the analysis. It was found that the current ratio and net profit margin have a significant effect on stock prices individually, but the debt to equity ratio does not. When considered together, however, the current ratio, debt to equity ratio, and net profit margin do have a significant combined effect on stock prices.

Using graphlet degree vectors to predict atomic displacement parameters in protein structures.

In structural biology, atomic displacement parameters, commonly used in the form of B values, describe uncertainties in atomic positions. Their distribution over the structure can provide hints on local structural reliability and mobility. A spatial macromolecular model can be represented by a graph whose nodes are atoms and whose edges correspond to all interatomic contacts within a certain distance. Small connected subgraphs, called graphlets, provide information about the wiring of a particular atom. The multiple linear regression approach based on this information aims to predict a distribution of values of isotropic atomic displacement parameters (B values) within a protein structure, given the atomic coordinates and molecular packing. By modeling the dynamic component of atomic uncertainties, this method allows the B values obtained from experimental crystallographic or cryo-electron microscopy studies to be reproduced relatively well.

Pengaruh Overtime Dan Insentif Terhadap Produktivitas Pekerja Konstruksi Pada Proyek Pembangunan Rumah Susun Pesisir Barat Lampung

This study aims to look at work productivity in order to obtain optimal work results, then efforts to improve work productivity first need to know the factors that affect work productivity with Overtime and Intentive variables as independent variables, the effect of overtime implemented within a certain period will affect the productivity of workers and the influence of incentives is one of the tools to encourage and direct the activities of subordinates to achieve company goals. In this research design using a causal method (cause and effect) and using a quantitative approach. The method used is the multiple linear regression approach using SPSS applications. The results of this study are Over time partially does not have an effect on Productivity, Incentives partially have an influence and significant on Productivity and Over time (X1), and Incentives (X2) together on Productivity (Y).

A Statistical Forecast Model for Extratropical Cyclones Including Intensity and Precipitation Type

Abstract Winter extratropical cyclones (ETCs) are dominant features of winter weather on the east coast of North America. These storms are characterized by high winds and heavy precipitation (rain, snow, and ice). ETCs are well predicted by numerical weather prediction models (NWPs) at short- to midrange forecast lead times, but prediction on seasonal time scales is lacking. We develop a set of multiple linear regression models, using stepwise regression and cross validation, to predict the number of storms expected to affect a specific location throughout the winter storm season. Each model in the set predicts a specific storm type (e.g., snow, rain, or bomb storms). This set of models is applied in a probabilistic forecast framework that uses the probability density function of the prediction in combination with climatological mean storm activity. The resulting forecast makes statements about the likelihood of below-average, average, or above-average activity for all storms and for each of the type-specific subsets of storms. Though this forecast framework could in theory be applied anywhere, we demonstrate its skill in forecasting the characteristics of the winter storm season experienced in Halifax, Nova Scotia, Canada. Significance Statement Winter storms are a disruptive but inevitable part of life on the eastern coast of North America all the way from the Carolinas to Labrador. Knowing each fall what to expect for the upcoming winter storm season is not only a matter of public interest, but also of great public safety and financial importance. Here we develop a model that uses the state of the atmosphere over the month of September to forecast the upcoming winter storm characteristics for a specified region of interest. Our model uses a multiple linear regression approach to make skilled forecasts including probability statements about the level and type of storm activity. Forecasts can be used to inform planning for the winter ahead.

A Comprehensive Analysis of the Drivers Behind the Effective Tax Rate of Financial Times Stock Exchange (FTSE) in Malaysia

This study examines corporate effective tax rates (ETR) of Malaysian multinational corporations (MNC) listed in the Financial Times Stock Exchange (FTSE) Bursa Malaysia Kuala Lumpur Composite Index (KLCI) and FTSE Bursa Malaysia Mid 70 Index. The main objective of this study is to examine the relationship between the ETR of MNCs with subsidiaries in tax haven countries, and selected corporate characteristics such as firm size, leverage, capital intensity, return on assets, inventory intensity, profit margin, and foreign directors. Using a multiple linear regression approach on a balanced panel sample of 68 companies (321 firm-years) spanning 2017 to 2021, the study provides evidence for the variability of corporate ETRs in which the average corporate ETRs falls below the STR for the financial year 2021 of 24%. The statistical results also reveal that lower ETRs are associated with highly leveraged companies, and greater investments in fixed assets. Further, a negative coefficient for return on assets and profit margin suggests that companies have benefited from tax incentives provided by the government. Hence, this study contributes to tax literature and policymakers on factors that influence the corporate ETR, especially MNC with subsidiaries in tax haven countries. Tax authorities should implement tight monitoring to avoid these MNCs extensively engaging in tax planning which will result in revenue loss to the Malaysian income tax collection.

ANALYZING THE FACTORS THAT AFFECT THE AMOUNT OF PLASTIC WASTE

One of the biggest environmental issues facing the planet today is plastic garbage, which was created by people and has an effect on them as well. Although though governments have implemented policies, rules, and other initiatives to increase public awareness of and engagement in decreasing plastic trash, the statistics has not changed, and this amount keeps rising every year. Several nations appear to be having trouble solving this problem. In addition, according to the World Bank, the country produces more plastics the wealthy its citizens are. By evaluating behavior and educating people, culture and education can be the answer to this problem. As people get more educated, their awareness of and care for the environment increases. The relationship between each of these factors—culture, education, national income, and government role and environmental problems might potentially help minimize plastic waste. Consequently, the goal of this study is to pinpoint the variables that may have an impact on the volume of plastic garbage and examine how these variables may affect the quantity of plastic waste in various nations. Hofstede's national culture dimension (Power Distance, Collectivism and Individualism, Masculinity and Femininity, Uncertainty Avoidance, Long-term Orientation and Short-term Orientation, Indulgence and Restraint), wealth per capita, education, and the government's plan and policy on sustainable consumption are the variables that will be examined. In this study, worldwide data for the year 2018 is gathered from 66 different nations utilizing secondary sources, and the data is then analyzed using the Multiple Linear Regression approach. According to the findings, there is a strong correlation between individualism and collectivism and the volume of plastic garbage. The results of this study should help each nation's government reduce the quantity of plastic garbage that is generated.

Predicting Indonesia’s Gross Domestic Product (GDP): A Comparative Analysis of Regression and Machine Learning Models

This research paper presents an analysis of Indonesia's quarterly Gross Domestic Product (GDP) growth spanning a significant 13-year period, from the first quarter of 2010 to the fourth quarter of 2022. The study focuses on utilizing four key economic indicators to gain insights into the country's economic performance during this timeframe. To develop accurate predictive models, we utilize Multiple Linear Regression (MLR), K-Nearest Neighbours (K-NN), and Artificial Neural Network (ANN) approaches. The models are compared based on performance metrics, including the Mean Absolute Error (MAE) and the Root Mean Squared Error (RMSE). Our findings indicate that the MLR model outperforms the machine learning models, K-NN and ANN, in forecasting Indonesia's GDP. This suggests that a simpler and more explainable model, such as MLR, suffices to provide meaningful and interpretable results. The paper's insights are valuable to economists, policymakers, and researchers, offering a practical and understandable means to predict Indonesia's economic trajectory. Keywords: Artificial Neural Network (ANN), Gross Domestic Product (GDP), K-Nearest Neighbours (K-NN), Multiple Linear Regression (MLR), Predictive Models.

QSAR, Molecular Docking & ADMET Studies of Pyrrolo[2,3-d] Pyrimidine Derivatives as CDK4 Inhibitors for the Treatment of Cancer

Background:: Cancer is caused by dysregulation of the cell cycle, which results in abnormal proliferation and the inability of cells to differentiate or die. Cyclins and cyclin-dependent kinases (CDK4) inhibitors are drugs that target a specific enzyme, CDK4 that controls cell cycle progression in cancer. Aim & Objective:: The aim of this study is to obtain an optimized pharmacophore of pyrrolo[2,3-d] pyrimidine as a CDK4 inhibitor using QSAR studies. This aids in determining the link between structure and activity in newly developed chemical entities (NCE’s). To perform molecular docking and ADMET analysis to determine the binding affinity and drug-likeness of NCE’s. Materials and Methods:: The Multiple linear regression approach (MLR) method was utilised to generate the QSAR Model using the programme QSARINS v.2.2.4. For molecular docking, the Autodock vina software was employed. While the Swiss ADME and ToxiM online tools were used to predict toxicity. Results and Discussion:: The best models generated for 2D QSAR had correlation coefficients of R2= 0.9247 & Q2= 0.924 and for 3D QSAR, coefficients were R2 = 0.9297 and Q2 = 0.876. A novel series of 68 derivatives was designed based on QSAR investigations. Molecule C-58 has shown maximum binding affinity in molecular docking as compared to the standard Ribociclib. result: For 2D QSAR, the best models produced has correlation coefficients of R2= 0.9247 and Q2= 0.924. The 3D-QSAR model obtained with R2= 0.9297 and Q2 = 0.876. Based on QSAR studies, a new series of 68 derivatives was generated Conclusion:: Fifteen compounds have shown potential as CDK4 inhibitors based on docking studies, pharmacokinetic behavior and toxicity profile. The maximum binding affinity was demonstrated by molecule C-58. other: N/A

Application of adaptive neuro-fuzzy inference system in modelling home-based trip generation

This study aims to investigate the feasibility of using the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multiple Linear Regression (MLR) for modelling home-based trip generation in Salfit City, Palestine. The research compares the performance of these two methods and provides insights into their efficiency for different trip purposes. The methodology involves developing separate trip generation models for various purposes, including total daily household trips generated (HBALL), home-based work (HBW), home-based education (HBE), and other home-based trips (HBO). The underlying principles and implementation details of the ANFIS architecture are also discussed. Objective evaluation metrics like Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared are used to assess the effectiveness of each model. The results indicate that ANFIS performs well for modeling HBALL and HBO trips, which exhibit more complex behavior with wider data ranges and higher average daily trip counts. Compared to the MLR approach, ANFIS shows improved accuracy and closer predictions. For instance, when modelling HBALL trips, ANFIS achieved an RMSE of 1.4880, while MLR resulted in 1.7112, representing a reduction of 13.04%. However, for HBW and HBE trip purposes, where behavior is less complicated, MLR appears to be sufficient. The R-squared values obtained with MLR are sufficiently high to capture most of the trip variations, and the results between the two approaches are closely comparable. The R-squared value for MLR in HBE trips was almost identical to that of ANFIS, which was 96.65%. Similarly, for HBW trips, MLR achieved an R-squared value of 90.36%, while ANFIS performed slightly better with an R-squared value of 92.74%. In conclusion, ANFIS shows promise for modelling systems with complex behavior, while MLR remains a suitable option for less complicated scenarios. The study emphasizes the importance of exploring different modelling techniques in transportation research to identify the most appropriate approach for specific cases.