Multiple Regression Method Research Articles

A Novel Modeling Optimization Approach for a Seven-Channel Titania Ceramic Membrane in an Oily Wastewater Filtration System Based on Experimentation, Full Factorial Design, and Machine Learning.

This comprehensive study looks at how operational conditions affect the performance of a novel seven-channel titania ceramic ultrafiltration membrane for the treatment of produced water. A full factorial design experiment (23) was conducted to study the effect of the cross-flow operating factors on the membrane permeate flux decline and the overall permeate volume. Eleven experimental runs were performed for three important process operating variables: transmembrane pressure (TMP), crossflow velocity (CFV), and filtration time (FT). Steady final membrane fluxes and permeate volumes were recorded for each experimental run. Under the optimized conditions (1.5 bar, 1 m/s, and 2 h), the membrane performance index demonstrated an oil rejection rate of 99%, a flux of 297 L/m2·h (LMH), a 38% overall initial flux decline, and a total permeate volume of 8.14 L. The regression models used for the steady-state membrane permeate flux decline and overall permeate volume led to the highest goodness of fit to the experimental data with a correlation coefficient of 0.999. A Multiple Linear Regression method and an Artificial Neural Network approach were also employed to model the experimental membrane permeate flux decline and analyze the impact of the operating conditions on membrane performance. The predictions of the Gaussian regression and the Levenberg-Marquardt backpropagation method were validated with a determination coefficient of 99% and a Mean Square Error of 0.07.

Stature estimation in male and female populations of India and Nigeria depending on other anthropometric parameters using multiple regression analysis

In cases of mass disasters, accidents, or criminal investigations where the identity of victims is unknown certain basic anthropological parameters are helpful in ascertaining these like race, sex, age and stature. Estimating stature using multiple body measurements such as shoulder breadth, foot length, thigh length, and knee height is a common approach in anthropometry and forensic anthropology. The presence of sex and population differences in anthropometric indicators allows these measurements to be used not only to estimate the stature of an individual, but also to determine sex, different races or populations based on skeletal remains. The purpose of the study is to develop and practically verify the work of regression equations for estimating stature depending on other anthropometric indicators of men and women of two ethnically diverse populations. For this study, anthropometric data were gathered from two distinct population groups: Indian (n=102) and Nigerian (n=205). Basic demographic details along with measurements of shoulder breadth, sitting shoulder height, sitting foot length, sitting knee height, and sitting thigh length were obtained using standardized techniques as per the established anthropometric protocols. Statistical analysis was performed using appropriate software packages such as SPSS, R, or SAS. The multiple regression method was used to estimate body length depending on other anthropometric indicators. As a result of the conducted multiple linear regression analysis, reliable relationships between stature and specific anthropometric measurements in Nigerian and Indian men and women were established. It was found that stature is highly likely to depend on knee height in a sitting position in Nigerian women (R2=0.531, p<0.001), as well as hip length, foot length, and shoulder height in a sitting position in Indian men (R2=0.725, p <0.001). Stature in Indian women reliably depends on hip length and foot length in a sitting position, and in Nigerian men - on hip length, foot length, shoulder width and shoulder height in a sitting position, but the regression equations have a coefficient of determination less than 0.5 (respectively, R2=0.463, p<0.001 and R2=0.405, p<0.001) and therefore do not have much significance for forensic purposes. Additional groups (30 people for each category) were used to test the obtained regression equations. The high correlation coefficients (0.6<r<0.75) observed in both test groups indicate the reliability of the regression models and the suitability of the selected anthropometric measurements for the estimation of stature in these populations. The obtained data emphasize the importance of taking into account factors specific to the population when developing regression equations for the estimation of stature and emphasize the usefulness of anthropometric measurements in predicting this indicator for different gender and demographic groups of the population, although their further verification on larger and more diverse samples is necessary.

Corruption prevention in organizational clustering in Indonesia: through the role of the HU-model in detecting corruption

PurposeThis paper aims to test the effectiveness of the Haryono Umar (HU)-model used in corruption prevention strategies through corruption detection as a tool for detecting corruption because the mode of corruption is increasingly dynamic and complex by focusing on the causes of corruption: pressure, opportunity, rationalization, capability and lack of integrity.Design/methodology/approachThe research uses multiple regression methods, classification and regression trees and the HU-model application system developed by researchers. The research sample uses secondary data from financial reports on the Indonesia stock exchange according to organizational clustering (such as red, grey and green areas).FindingsThe research result showed that of the 470 sample companies, there were 445 companies, or 98.9%, in the red cluster (indicated corruption), 19 companies, or 4.04, in the green clusters or not indicated corruption and six companies, or 1.28%, were included in the grey cluster or potential corruption. By knowing the cluster of an organization, efforts to prevent corruption can be made effective and efficient. Implementing the HU-model proves that the amount of pressure, the abundance of opportunities, the ease of rationalization and the high level of position and authority strengthen the drive for corruption if there is a lack of integrity.Research limitations/implicationsEach internal organization can use this model independently and find conditions related to corruption so that they can immediately take action to prevent it.Originality/valueThe application of the HU-model is a discovery in preventing corruption by focusing on the possibility of corruption occurring in each organization through organizational clustering.

Analysis of New Energy Vehicle Development in China Based on LSTM and ARIMA Models

This study comprehensively analyses the development factors of new energy vehicles in China based on data and literature, adopts principal component analysis to reduce the dimensionality to deal with the influence indicators, and combines multiple regression and LSTM model to predict the development trend of the industry. For the relationship between electrification and the ecological environment, multiple linear regression and ARIMA methods are tried to explore the impact of carbon emissions on the ecological environment. These methods provide a comprehensive perspective for a comprehensive understanding of the development of new energy vehicles and provide a reference basis for future industrial development and ecological environment improvement.

Quantitative Structure Photovoltaic Properties Relationship of Coumarin Dyes Derived

The performance of an organic solar cell is strongly influenced by the structure of the photosensitizer. In this work, the open-circuit voltage (<I>V<SUB>OC</SUB></I>) and conversion efficiency (<i>η</i>) of a series of coumarin dyes are quantitatively related to the structure of nine coumarin derivatives. The Quantitative Structure Property Relationship (QSPR) is performed using the statistical method of multiple linear regression. In addition, descriptors determined from the ground state at the Cam_B3lyp/6-31G(d, p) level of theory and from the 2D structure of the molecules are mathematically related to the photovoltaic properties. These VOC and <i>η </i>models are accredited with very good statistical indicators (<I>R<sup>2</sup></I> = 0.906 and 0.918; Q<sub>cv</sub><sup>2</sup>= 0.845 and 0.849; S= 0.045 and 0.112; F = 14.524 and 16.846). These statistical indicators confirm the robustness and performance of the models developed. The results show that Voc improves with decreasing surface tension (<i>ts</i>) and increasing number of cycles (<i>cycle</i>). As for the conversion efficiency of light radiation into electrical energy, it is optimal when the light harvesting efficiency (<i>LHE<sub>th</sub></i>) and the excited state lifetime (<i>τ<sub>th</sub></i>) are high. In conclusion, these models have good predictive capabilities and can be used to predict and explain the open-circuit voltage and efficiency of coumarin derivatives that belong to the same field of application.

Analysis of the market value of Premier League attacker

The main purpose of this study is to use the method of multiple linear regression to conduct a comprehensive discussion on "Factors affecting the market price of Premier League striker players". In the era of increasingly hot soccer, the transfer of stars is a big attraction in the transfer period every year, but there are still many clubs signing overpaid and underpaid players. The overall objective of this study is to find the determinants of players' price, so as to provide a reference for clubs to improve the utilization of funds in the transfer period. In this study, a dataset of player data for the 17-18 Premier League season was first downloaded via Kaggle. Then, the dataset obtained from Kaggle was used for empirical analysis to identify correlations that significantly affect the market price of players, and multiple linear regression analysis was performed after processing these data. Through the calculations, it was determined that match performance and goals scored had a significant positive impact on market value, and age and match possession had a non-significant negative impact on market value, which suggests that there is a need for the relevant team managers to optimize these aspects in order to promote a virtuous cycle of club development and team performance.

Analysis of the market value of Premier League attacker

The main purpose of this study is to use the method of multiple linear regression to conduct a comprehensive discussion on "Factors affecting the market price of Premier League striker players". In the era of increasingly hot soccer, the transfer of stars is a big attraction in the transfer period every year, but there are still many clubs signing overpaid and underpaid players. The overall objective of this study is to find the determinants of players' price, so as to provide a reference for clubs to improve the utilization of funds in the transfer period. In this study, a dataset of player data for the 17-18 Premier League season was first downloaded via Kaggle. Then, the dataset obtained from Kaggle was used for empirical analysis to identify correlations that significantly affect the market price of players, and multiple linear regression analysis was performed after processing these data. Through the calculations, it was determined that match performance and goals scored had a significant positive impact on market value, and age and match possession had a non-significant negative impact on market value, which suggests that there is a need for the relevant team managers to optimize these aspects in order to promote a virtuous cycle of club development and team performance.

Predicting the Views of TED Talks using Machine Learning Model

A forecast of such accuracy would have significant implications in this respect for content creators, marketers, and researchers alike. The paper questions whether machine learning models can predict the views that any certain TED Talks receive with a dataset containing features on the duration of the talks, characteristics of the speaker, the date of publication, metrics indicating audience engagement, and so on. In this work, we employ techniques of multiple regression, decision trees, and ensemble methods to identify empirically which factors have the most influence and to come up with the best models for the prediction. Indeed, the results obtained easily indicate that the ensemble models-especially Random Forest and Gradient Boosting- perform way above the rest. These features help to emphasize the fact that machine learning will help guide a content strategy to better reach audiences and give insight into a deeper understanding of how digital content is consumed. Keywords: TED Talks, machine learning, predictive modeling, view prediction, Random Forest, Gradient Boosting, digital content analysis, audience engagement.

Factors Influencing Farmers’ Willingness to Pay for NPK Fertilizer in Specialty Coffee: Insights from Pagar Alam, Indonesia

Robusta coffee cultivation in Pagar Alam City has not been carried out optimally because most farmers do not practice precision farming patterns in the use of production inputs to increase coffee plant productivity, including the use of unsubsidised coffee-specific fertilisers. In order to be accepted by farmers, fertiliser companies must pay attention to the willingness to pay of farmers for these fertilisers. For this reason, the study aims to: (1) determine farmer perceptions of nutrient content, ease of use and quality of fertiliser, (2) determine the value of farmers’ willingness to pay for unsubsidised NPK fertiliser for coffee in Pagar Alam City, and (3) determine the effect of nutrient content, ease of use and quality of fertiliser and other factors on farmers' WTP in Pagar Alam City. The research location was determined purposively with the consideration that Pagar Alam City is one of the highest coffee producing areas in Indonesia. A total of 100 coffee farmers were used as samples and interviewed using a prepared questionnaire. Data on farmers' willingness to pay was obtained by using the contingent valuation method (CVM). Descriptive analysis was used to determine the level of farmers' perception of nutrient content, ease of use and quality of fertiliser. Multiple linear regression methods was used to analyze the effect of nutrient content, ease of use and fertiliser quality and other factors on farmers' WTP. The results showed that the average value of farmers' WTP for unsubsidised NPK fertiliser for coffee was IDR 11,160 per kg which is lower than the price of unsubsidised NPK fertiliser commonly paid by coffee farmers. The study found that fertiliser quality, ease of fertiliser uses and fertiliser nutrient content, farm income level, land size, age, farming experience, household size and farmer membership status in farmer groups significantly affect the farmers’ WTP.

PENGARUH SOSIAL MEDIA MARKETING, E-SERVICE QUALITY, DAN REVIEW CONTENT TERHADAP MINAT BELI KONSUMEN BOX OUTLET SINGARAJA

This study aims to examine the influence of Social Media Marketing, E-Service Quality, and Review Content on consumer buying interest in Singaraja Box Outlet Stores. The population in this study includes all Singaraja Box Outlet Store consumers from various walks of life, both men and women. The sample used in this study was 85 consumers selected through a questionnaire technique distributed using Google Form. This study used a quantitative approach and the data was analyzed by multiple linear regression method using IBM SPSS Statistics 25 application. The results showed that Social Media Marketing, E-Service Quality and Review Content proved to have a positive and significant influence on consumer buying interest in Singaraja Box Outlet Stores

Debt is Not Always Bad Even in Difficult Economic Conditions

The problem faced by the company is the use of debt that is not according to plan. The purpose of this study is to determine whether debt policy affects company performance. This study was conducted on manufacturing sector companies listed on the Indonesia Stock Exchange in 2018–2023 with a population of 296 companies, the sample used was 73 companies for six years. Thus, the number of samples used in this study was 438. The company performance variable is proxied by (ROE) while the independent variables in this study are debt policy proxied by (DER and DER*KE), investment decisions (KI), managerial ownership (MNJR), and economic conditions (KE). The data analysis method uses multiple linear regression methods with SPSS software version 26. The results of the study show that the debt policy variable (DER) has a negative and significant effect on company performance (ROE) and the debt policy variable*economic conditions (DER*KE) has a positive and significant effect on company performance (ROE). This means that when economic conditions are supportive, companies that use debt to finance operational activities or business expansion tend to experience increased performance. Funds obtained from debt can be used for investment, new product development, or market expansion. Proper use of debt can increase a company's return on equity (ROE), as long as the rate of return on the investment financed by debt is higher than the cost of debt. Timing the debt decision is crucial. In improving economic conditions, companies can take advantage of low interest rates and increase production capacity.

PENGARUH GAYA KEPEMIMPINAN DAN MOTIVASI TERHADAP ORGANIZATIONAL CITIZENSHIP BEHAVIOR (OCB) RUMAH SAKIT PERTAMINA CIREBON

This research aims to determine the influence of autocratic leadership style and work motivation on Organizational Citizenship Behavior (OCB) at Pertamina Hospital Cirebon Distribution.Respondents in this study were employees of Pertamina Hospital Cirebon Distribution using multiple linear regression methods and SPSS analysis tools.The results of this research show that autocratic leadership style influences Organizational Citizenship Behavior (OCB) and work motivation also influences Organizational Citizenship Behavior (OCB).

Pengaruh Struktur Modal dan Rasio Aktivitas Terhadap Profitabilitas

This research aims to find out how much influence capital structure and activity ratios have on profitability in automotive sector companies listed on the Indonesia Stock Exchange in 2018-2022. Sample selection was carried out using purposive sampling to obtain seven companies that met the requirements as samples. The data used in this research is secondary data in the form of financial reports obtained from the Indonesia Stock Exchange (IDX) with a total of 35 financial reports observed. This research uses the statistical method of multiple linear regression in the SPSS version 26 application. The results of this research show that capital structure has a positive and significant effect on profitability, which means that every increase in capital structure by one percent will increase profitability; the activity ratio has a negative and significant effect on profitability, which means that every increase in the activity ratio will reduce profitability and; Simultaneously, capital structure and activity ratios have a positive and significant effect on profitability in automotive sector companies.

Pengaruh Kepemimpinan dan Budaya Organisasi Terhadap Kinerja Pegawai Satuan Polisi Pamong Praja Kabupaten Karawang

This study aims to examine the effect of leadership and organizational culture on employee performance at the Karawang Regency Pamong Praja Police Unit. This analysis uses the independent variables of Leadership and Organizational Culture and the dependent variable is employee performance. The sample of this study were employees of the Karawang Regency Pamong Praja Police Unit. data collection was carried out by distributing questionnaires to employees whose respondents were 52 people. The analysis technique in this study used multiple linear regression methods. The results of this study indicate that there is no partial influence of leadership variables on employee performance, there is a positive and significant partial influence of organizational culture variables on performance, leadership and organizational culture together have a positive and significant effect on performance.

STRATEGY TO INCREASE THE TAX RATIO THROUGH DIGITAL ECONOMY TAXATION: REVIEW OF LESSONS FROM ASEAN COUNTRIES

This study aims to investigate the effect of tax increase strategies on the Tax Revenue Ratio, moderated by the Digital Economy in Indonesia. The data used is panel data consisting of 60 observations over a specific period. The analysis was conducted using multiple linear regression methods and other statistical tests to examine the research hypotheses. The analysis results indicate that, although there are initially intriguing indications, there is insufficient statistical evidence to support a significant effect of the tax increase strategy on the Tax Revenue Ratio within the context of the sample studied. Similarly, the Digital Economy does not appear to have a significant influence on the Tax Revenue Ratio. Additionally, there is no significant moderating effect of the Digital Economy on the relationship between the tax increase strategy and the Tax Revenue Ratio. Nonetheless, this study's findings provide valuable insights for further understanding the dynamics between tax strategies, the digital economy, and the Tax Revenue Ratio. The policy implications of these findings underscore the need for further research and a deeper understanding of the factors influencing the Tax Revenue Ratio in Indonesia, particularly in facing the challenges and opportunities of the continuously evolving digital economy. Keywords: Tax Increase Strategy, Digital Economy, State Tax Revenue, Tax Policy, Digital Economy Dynamics

Pengaruh Personal Identity, Social Identity, Kredibilitas Influencer Terhadap Green Purchasing Behavior

This research tests and analyzes personal identity, social identity, influencer credibility on green purchasing behavior. And knowing which variables are the most dominant in influencing green purchasing behavior. The research uses a quantitative approach that uses causal relationships. The sample selection criteria in this study were aged 12 years to 43 years who had purchased and used Tisoo brand tissue and were in the Jabodetabek area. Questionnaires were collected from 100 respondens, the data was then processed using the multiple linear regression method. Based on the results of the analysis, it is known that the personal identity variable partially has a positive effect on green purchasing behavior. Meanwhile, the social identity variable and the influencer credibility variable partially have a negative effect on green purchasing behavior. Simultaneous hypothesis testing states that personal identity, social identity and influencer credibility have a positive effect on green purchasing behavior. and the most dominant variable in influencing green purchasing decisions is the personal identity variable. The implication is that companies like Tisoo should emphasize environmental values in their marketing and select influencers who understand eco-friendly products to encourage green purchasing.

Prediction of Total Soluble Solids Content Using Tomato Characteristics: Comparison Artificial Neural Network vs. Multiple Linear Regression

Tomatoes are among the world’s most significant vegetables, both in terms of production and consumption. Harvesting takes place in tomato production when the important quality attribute of total soluble solids content reaches its maximum possible level. Tomato total soluble solids content (TSS) is among the most crucial attribute parameters for assessing tomato quality and for tomato commercialization. Determination of total soluble solids content by conventional measurement methods is both destructive and time-consuming. Therefore, the tomato processing industry needs a rapid identification method to measure total soluble solids content (TSS). In this study, we aimed to estimate how much soluble solids there are in beef tomato fruit by Artificial Neural Networks (ANN) and Multiple Linear Regression (MLR) methods. The models were assessed using the Coefficient of Determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) metrics. The training data set results of the MLR model established to estimate the amount of brix in tomato fruit, calculated as MAE: 0.2349, RMSE: 0.3048, R2: 0.8441, and MAPE: 5.5368, while, according to the ANN model, MAE: 0.0250, RMSE: 0.031, R2: 0.9982 and MAPE: 0.5814. According to the metric outcomes, the ANN-based model performed better in both the training and testing parts.

SUSTAINABLE ENTREPRENEURSIP AND ENVIRONMENTAL DYNAMISM OF SELECTED METER MANUFACTURING COMPANIES IN LAGOS STATE

In the fields of business and environmental studies, sustainable entrepreneurship and environmental dynamism are inevitable concepts that drives the economy .Sustainable businesses may need to be especially inventive in highly dynamic circumstances, utilizing their knowledge of sustainability to foresee and adapt to changes.Therefore, the study was designed to investigate sustainable entrepreneurship and environmental dynamism of selected meter manufacturing companies in Lagos State. The study adopted a survey research design and the study relied on primary data through structural self-administered questionnaire. The targeted population of this study is 207 mid-level and top management level employees of MMCs in Lagos state. The list of MMCs was drawn from Directory of Certified Electricity Meters and Instruments Manufacturing /Assembling Companies and Nigerian Electricity Management Services Agency who served as a regulatory body to MMCs in Nigeria. Taro Yamane formula was used to choose 136 participants to which the questionnaires were administered and returned. Multiple regression and Pearson correlation method of analyses were used for hypotheses of the study. Findings revealed that social and economic sustainability are significant predictors of market dynamism of MMCs in Lagos State. The study concludes that both social sustainability and economic sustainability have a positive and significant influence on market dynamism. The study therefore, recommended that MMCs should always come up with both economic and social sustainability policy that can mitigate the effect of market dynamism and improve their operations.

P011 PREVALENCE OF HYPERTENSION AND ASSOCIATION WITH RED MEAT AND VEGETABLE CONSUMPTION IN A MEGACITY IN NIGERIA: A POPULATION-BASED SURVEY

Background and Objectives: Hypertension is a growing burden worldwide, but the impact is worse in developing countries especially in cities, due to epidemiologic transition, rapid urbanization and socioeconomic dynamics. We determine the current prevalence of hypertension, and association with red meat and vegetable consumption in Lagos, a megacity in Nigeria. Methods: This was a population-based opportunistic cross-sectional survey of 2244 voluntary adults ≥ 18years residents of Lagos megacity in Nigeria. Socio-demographic and clinical characteristics were obtained. We measured blood pressure by Omron M3 automatic upper arm monitor after ≥5 min of rest. We defined hypertension as systolic blood pressure of ≥140 mmHg and/or diastolic blood pressure of ≥90 mmHg or use of antihypertensive drugs. Qualitative information on meat and vegetable consumption was documented. Chi squared and multiple logistic regression methods were used to determine association and predictor of hypertension respectively, and odds ratio with confidence interval as the measure of effect size. P value of < 0.05 was taken as statistical significance. Results: 2244 participants were analyzed, with mean age of 46±14 years, and 75% were women. More than 26% were overweight or obese, 43.2% drinks alcoholic beverages, and 30.3% men and 3.5% women smoked cigarettes. The overall prevalence of hypertension was similar in males (38.1%) and females (37.4%), and it increases with age, reaching 46.7% in ≥ 60years. Daily consumption of meat (38.6% versus 28%(rarely), p =0.03), and infrequent consumption of vegetables (47.7%% versus 19.2%9 (daily intake), p = 0.006) were associated with higher prevalence of hypertension. Older age (OR, 2.133(CI, 1.61 – 2.83)), and family history of hypertension (OR, 1.51(CI, 1.20 – 1.92)) were the predictors of hypertension. Conclusions: The prevalence of hypertension is high among middle age urban dwellers in Lagos, Nigeria. Avoidance of red meat, and increased consumption of vegetables, may reduce the burden of hypertension in the population.

The Dynamics of Food security in Indonesia: A Study on the Availability, Affordability, and Utilization of Food in Districts and Cities with the Lowest Index in 2022

In 2022, a total of 70 districts (16.83% of the total 416 districts) and 4 cities (4% of the 98 cities) were reported to have low scores in the Food security Index (FRI). This research aims to analyze the impact of the availability, affordability, and utilization of food on the Food security Index (FRI) in districts and cities with the lowest Food security Index in Indonesia. The research is quantitative with a correlational design, aiming to analyze the effect of availability, affordability, and utilization of food on the Food security Index. The study was conducted in 74 districts and cities that had the lowest Food security Index in 2022. Secondary data were obtained from the National Food Agency of the Republic of Indonesia, and data analysis was carried out using Multiple Linear Regression methods. The research findings affirm that the availability of food has a significant positive impact on the Food security Index in districts and cities with the lowest Food security Index. Meanwhile, the affordability of food also shows a significant positive influence on the Food security Index at the same local level. However, the utilization of food is not proven to have a significant impact on the Food security Index in districts and cities with low levels of food security.