Regression Techniques Research Articles

Underweight Among Scheduled Tribe Non-Pregnant Adolescent Women: A Trend Analysis and Determinants from NFHS

BackgroundThis study examines the trends in underweight prevalence among non-pregnant adolescent women from Scheduled Tribes (ST) in India between 2015 and 2021. It also explores how demographic and socio-economic factors influence underweight status among ST adolescent women aged 15–19 and assesses state-level variations in prevalence.Data and MethodsThe analysis is based on secondary data from the fourth, and fifth rounds of the National Family Health Survey (2015–16 and 2019–21), including 42,164 ST non-pregnant adolescent women aged 15–19. Bivariate and multivariate logistic regression techniques were used.ResultsThe prevalence of underweight among ST non-pregnant adolescent women declined from 47.2% in 2005–06 to 42.6% in 2015–16 and 41.6% in 2019–21. Key factors associated with underweight status include religion, marital status, household wealth, dietary practices, anaemia, and region of residence.ConclusionST adolescent women represent a vulnerable group at high risk of undernutrition. Enhancing access to affordable, nutritious, and safe food and strengthening anaemia-focused interventions are critical to improving their health outcomes.

The Impact of Macroeconomic Factors on Gold Prices

This study analyzes the influence of significant macroeconomic variables, like interest rates, inflation, GDP growth rates, and currency rates, on gold prices. Analysis of a 20-year dataset from 2003 to 2023, employing descriptive statistics and regression techniques, demonstrates substantial connections between these variables and changes in gold prices. The results show that inflation and exchange rates have a positive relationship with gold prices, while GDP growth rate has a modest inverse relationship. Interest rates exhibit a weak inverse relationship, although it is not statistically significant. The model explains approximately 93% of the variation in gold prices, underscoring the complex interplay between macroeconomic factors and gold price movements. The results are significant for investors aiming to mitigate inflation or economic instability, as well as for policymakers evaluating the effects of monetary policy on gold demand. This research contributes to the understanding of gold's role in the global financial system and highlights areas for future investigation, including the incorporation of additional factors and advanced modelling techniques. Keywords: Gold prices, Macroeconomic factors, GDP growth rate, Interest rates, Inflation, Exchange rates, Regression analysis.

The Influence of School Committees and Operational Assistance for Educational Units on School’s Quality

This study aims to determine whether school committees and educational unit operational assistance influence school quality. This research was carried out at SD Negeri Muara Kuang District, Ogan Ilir Regency. This type of research is quantitative, while the partial correlation method is used. The validity test uses construct validity while the reliability test uses Cronbach’s Alpha. The analysis prerequisite test used the normality test, homogeneity test, linearity test, and multicollinearity test and the data are analyzed using sample regression and multiple regression techniques. The sample in this research is the school committee, principal, and teachers in Negen Elementry School, Muara Kuang District, numbering 61 people. Data collection techniques in this research used questionnaires and documentation. The results of the research conclude that (1) There is an influence of school committees on school quality with a correlation value of 0,109 with a correlation percentage of 1,2% while 98,8% is determined by other factors not mentioned in this research, (2) There is an influence of assistance educational unit operations on school quality with a correlation value of 0,937 with a correlation percentage of 87,8% while 12,2% is determined by other factors not mentioned in this research, (3) There is an influence of the school committees and educational unit operational assistance together on school quality with a correlation value of 0,937 with a relationship percentage of 87,8% while 12,2% is determined by other factors not mentioned in this research.

ANALYSIS PURCHASE INTENTION OF MEN'S SKINCARE: THE ROLE OF CONTENT MARKETING, PRODUCT INNOVATION, AND PERCEIVED VALUE AS PREDICTORS

In an increasingly advanced digital era, content marketing has become an important tool for reaching consumers and building strong relationships with them. Informative, relevant and interesting content can increase consumer awareness and interest in skincare products. This research aims to analyze men's skincare purchase intention by focusing on the role of content marketing, product innovation, and perceived value as the main predictors. This research methodology uses a quantitative approach by collecting data through questionnaires distributed to male respondents who use skincare products. Data analysis was carried out using regression techniques to test the relationship between the variables studied. The sample used as respondents was 170 people. The research results show that content marketing, product innovation, perceived value have a positive and significant effect on purchase intention. The results of this research have important implications for companies that market men's skincare products, namely that effective content marketing must be a top priority, because relevant and interesting content can significantly increase consumer awareness and interest. Then product innovation must continue to be improved to meet the ever-changing needs and preferences of consumers. In addition, companies must ensure that consumers perceive high value from their products, both through superior quality, reasonable prices, and emotional and social benefits.

Korelasi Antara Kecerdasan Emosional dengan Interaksi Sosial Siswa di Sekolah Dasar

This research explains whether there is a relationship between students' emotional intelligence and social interaction in class V students. This research uses quantitative research with a descriptive approach and uses a questionnaire with a Likert scale type and has 5 alternative answers. At the start of the research, the questionnaire was tested by a team of experts (validators) to ensure that the questionnaire was suitable for use in research. Then the questionnaire sheet was tested on fifth grade students, then analyzed using validity and reliability tests. You can see whether the question items are valid or not, then analyzed for research using valid and reliable question items (validity test and reliability test). Then further research was carried out using data analysis tests using simple linear regression tests and hypothesis testing was carried out using the t test, f test and coefficient of determination. The results of the research can be concluded that the research data carried out is in accordance with the collection procedures carried out using the SPSS 22 program. This is proven by the results of data analysis tests using simple linear regression techniques so that the F_regression value = 15.352 > 4.14 with a significance of 0.000<0.05 is obtained. From these results, emotional intelligence has an effective contribution of 31.7% to social interaction. These data can be said to show that there is a relationship between emotional intelligence and social interaction.

Optimizing Mesh and CFD Simulation Performance: A Multivariate Analysis Approach

The objective of the study is to analyze mesh parameterization through the weighting of results that can define an increase in precision and a reduction in simulation time. To optimize the performance of a mesh and CFD simulation, we will adopt a systematic approach. Initially, we will employ experimental design, with five factors varied in three replications. These factors directly influence three responses. After collecting the experimental data, an evaluation of the significance between the independent variables was carried out, proposing a regression function capable of explaining the behavior of the factors and their relationship with the defined responses. Next, we will identify the individual optimal points using the Ordinary Least Squares method, representing ideal configurations of factors that maximize or minimize the responses of interest. Based on the results obtained, the Payoff matrix will be assembled, containing the individual optimal points and the reactions of the other variables. Thus, the scaling of functions will be performed to formulate and employ the minimization of the Mahalanobis multivariate distance, considering an adequate weighting between the optimal responses. This approach will guarantee the attainment of balanced and efficient solutions. Finally, we will integrate the results of the Payoff matrix into the mixture arrangement methodology, allowing for the conclusion of the analysis of optimal points for each function weight, providing efficient responses for the continuous improvement of the process. This systematic approach, which combines techniques of experimental design, regression, optimization, and multivariate analysis, will be fundamental to maximize the efficiency of mesh and CFD simulation, ensuring precise and reliable results.

Pengaruh Persepsi Harga, Kualitas Pelayanan dan Lokasi Terhadap Kepuasan Pelanggan di Bengkel Body Repair CK Motor Purbalingga

The purpose of this study is to analyze the impact of price perception, service quality, and location on customer satisfaction at CK Motor Body Repair Workshop in Purbalingga. This research employs a quantitative approach with a questionnaire as the primary data collection tool. The sample consists of 113 customers of CK Motor Body Repair Workshop in Purbalingga, selected using purposive sampling with the criteria: 1) having used the services of CK Motor Body Repair Workshop at least twice, and 2) having an income. Data analysis was performed using multiple linear regression techniques with SPSS version 25. The findings indicate that 1) price perception has a positive and significant effect on customer satisfaction, 2) service quality has a positive and significant effect on customer satisfaction, and 3) location does not affect customer satisfaction. Therefore, CK Motor Body Repair Workshop should focus on offering appropriate pricing and improving service quality to enhance customer satisfaction. Location does not need to be a primary concern, allowing the workshop to redirect attention and resources towards improving pricing and service aspects that are crucial for customer satisfaction.

Pengaruh Servant Leadership, Resistance To Change, dan Organizational Commitment Terhadap Organizational Citizenship Behavior (OCB) pada Karyawan PT. Telekomunikasi Selular Regional Jawa Tengah & DIY

The objective of this research is for analyze also evaluate the effect of servant leadership, resistance to change, and organizational commitment on organizational citizenship behavior (OCB) of PT. Telekomunikasi Selular Regional Jawa Tengah & DIY. The population on this research is 148 permanent employees and there were 110 respondents with convenience sampling. The data result was collected by questionare with 5 point of likert scale. The examine uses multiple linear regression technique by SPSS software version 16. The research outcome represented that servant leadership had positive affect also significant affect on OCB in PT. Telekomunikasi Selular Regional Jawa Tengah & DIY. Beside that organizational commitment had positive affect also significant affect on (OCB) PT. Telekomunikasi Selular Regional Jawa Tengah & DIY. Meanwhile resistance to change had no affect on OCB in PT. Telekomunikasi Selular Regional Jawa Tengah & DIY. PT. Telekomunikasi Selular is a telecommunications company and there are always innovation related to telecommunication technology, so that it is not support with resistance to change.

Determinan Tingkat Pengungkapan Wajib Laporan Keuangan Pemerintah Kabupaten/Kota di Provinsi Sumatera Selatan

The research aims to examine the effects of local government size, intergovernmental revenue, job specialization, and capital expenditure on the level of mandatory financial statement disclosure of local governments. Disclosure is based on Government Regulation related to Government Accounting Standards Number 71 of 2010. Secondary data is sourced from Audit Reports (LHP) on Local Government Financial Statements (LKPD). The sample comprises 17 districts/cities in South Sumatra Province with 51 LKPD audit reports from 2020-2022. Data is analyzed using multiple linear regression techniques with Eviews 12. The study finds that local government size significantly and positively affects the level of mandatory financial statement disclosure; intergovernmental revenue significantly and negatively affects the level of mandatory financial statement disclosure; while job specialization and capital expenditure do not have a significant effect on the level of mandatory financial statement disclosure; and all four factors together significantly affect the level of mandatory financial statement disclosure

Pegaruh Kualitas Pelayan, Kualitas Produk, dan Lokasi Terhadap Kepuasan Pelanggan Piknik Coffee Eatery Purbalingga

This research aims to determine the influence of service quality, product quality, and location on customer satisfaction at Piknik Coffee Eatery Purbalinga. The sample in this study was 110 customers of Piknik Coffee Eatery Purbalingga. This research applies a quantitative approach with a questionnaire as data collection. Purposive sampling was implemented as a sampling technique, with criteria 1). Purbalingga people who have visited Piknik Coffee Eatery at least 2 times and 2). Minimum age 17 years. Multiple linear regression technique using SPSS version 25 software. The results of this research show that Service Quality has no positive and significant effect on customer satisfaction, Product Quality has a positive and significant effect on customer satisfaction, and Location has no effect on customer satisfaction of Piknik Coffee Eatery Purbalinga.

Investigating the Impact of Agricultural Credit on Wheat Farming: Evidence from Pakistan

Increasing food production is crucial for attaining the Sustainable Development Goal (SDG#2) by 2030 through supplying agricultural credit and improved inputs. Thus, the present study examined the roles of agricultural credit, applications of fertilizer, usage of pesticides, farming area, and agricultural employment in enhancing wheat production in the developing economy (Pakistan) from 1990 to 2020. This study applied the autoregressive distributed lag (ARDL) regression method for the empirical analysis. The empirical findings from the ARDL regression technique revealed that agricultural credit can significantly enhance wheat production, while improved inputs, including fertilizer and pesticide usage, also positively contribute to wheat production. Notably, agriculture credit plays a vital role in assisting farmers to purchase advanced farm inputs and indirectly increases the wheat yield. Consequently, this study suggests that the banking sector should supply agricultural credit to farmers under flexible lending conditions; therefore, farmers can easily access credit and adopt modern technologies to enhance food production and ensure food security.

DEBT FINANCING AND PERFORMANCE OF MANUFACTURING COMPANIES IN NIGERIA

Certain companies have accumulated substantial debt that surpasses their net gains, which has a negative impact on their performance and investor trust. Consequently, this can ultimately end in complete collapse and even lead to closures. Based on the above corporate challenges this study examines into the effect of debt financing on performance of manufacturing companies in Nigeria. The study employed the descriptive survey design. The secondary panel data was sourced audited financial statement of selected manufacturing firms in Nigeria from the period of 2019-2022. The panel regression technique was employed to determine the relationship and magnitude of impact between the dependent variables and independent variables. The Hausman test was employed in taking decision from the pooled effect, fixed effect and random effect model. The findings from objective one revealed that short term debt has negative significant effect on return on equity at (β:-0.23: p<0.05) while long-term debt has positive significant effect on return on equity at (β: 0.10: p<0.05). Objective two revealed that long-term debt and debt ratio has negative significant effect on earnings per share at (β:-1.57: p<0.05) and (β:-42.73: p<0.05). The study recommended that manufacturing companies should aim to reduce reliance on short-term debt, as it negatively impacts return on equity (ROE) due to higher interest rates and repayment pressures. Instead, companies should consider increasing long-term debt, which positively affects ROE by providing stable, lower-cost financing over extended periods. A balanced debt structure, with strategic use of long-term financing, can help firms enhance profitability while maintaining financial flexibility and managing risks effectively.

AI-CADR: Artificial Intelligence Based Risk Stratification of Coronary Artery Disease using Novel Non-invasive Biomarkers.

Coronary artery disease (CAD) is one of the most common causes of sudden cardiac arrest, accounting for a large percentage of global mortality. A timely diagnosis and detection may save a person's life. The research suggests a methodological framework for non-invasive risk stratification based on information only possible after invasive coronary angiography. Novel clinical, chemical, and molecular cardiac biomarkers were used as input features from an especially collected dataset. Following a thorough evaluative search in the biomarker feature space, the optimum feature and classifier or regression technique (regressor) set were selected using K-fold cross-validation. Ten machine learning (ML) classifiers were employed in classification tasks to determine the number of affected cardiac vessels, the Gensini group, and the severity of CAD with 82.58%, 86.26%, and 90.91% accuracy, respectively. Eleven approaches were used in regression tasks to calculate stenosis percentage and Gensini score, with R-squared values of 0.58 and 0.56, respectively. Following a thorough evaluative search in the biomarkers feature space, the optimum feature and classifier or regressor set were selected using K-fold cross-validation. The biomarkers and classifier or regressor combinations serve as the foundation for the proposed risk stratification framework, incorporating clinical protocol. Finally, our proposed framework is compared to state-of-the-art studies, offering a robust, well-rounded, early detection capable, and novel 'biomarkers-ML combination' approach to risk stratification.

Does Managerial Short-termism Affect Corporate Social Responsibility?

This study examines how managerial short-termism affects corporate social responsibility (CSR) engagement among Chinese listed companies, revealing important contextual variations across regions, ownership structures, and industries. Using 23,253 firm-year observations from 2007 to 2022 and employing textual analysis and regression techniques, this study examines the relationship between managerial myopia and CSR performance. Findings demonstrate that managerial short-termism negatively impacts CSR engagement, with corporate governance quality partially mediating this effect. The relationship exhibits distinct patterns. Eastern regions show heightened sensitivity compared with central and western regions; state-owned enterprises show greater vulnerability than non-state enterprises. Finally, polluting industries resist myopic effects while non-polluting ones show significant negative impacts. These heterogeneous effects underscore how institutional environments and organizational characteristics influence the relationship between managerial time horizons and social responsibility. Our findings advance understanding of CSR drivers in emerging markets and offer policymakers targeted insights for developing context-specific interventions to enhance corporate sustainability.

Is rapid eye movement sleep behavior disorder a marker of Parkinson's disease severity?

Parkinson's disease (PD) is characterized by motor and non-motor features. There are several proposed clinical markers to define disease severity. However, if rapid eye movement sleep behavior disorder (RBD) is associated with worse prognosis of both motor and non-motor findings in PD is unknown. To determine whether RBD is a marker of PD clinical severity. We cross-sectionally compared patients according to the presence of RBD and used Hoehn Yahr, Schwab and England (ADL), MDS-UPDRS, brain magnetic resonance, polysomnography and autonomic reactivity tests to evaluate PD stage and disability. Pairwise comparisons and regression techniques were used to investigate the association of PD clinical markers with RBD. We enrolled 120 PD patients. RBD was present in 46% (n=55; median age 65 years; 67% male), who were compared to PD patients without RBD (n=65, median age 62 years, 64% male). There was also a healthy control group comprising 48 subjects (median age 57 years, 54% male). Comparing PD patients with and without RBD, RBD was associated with higher MDS-UPDRS Part II scores [15(11-21) x 12(7-16), p=0.02], higher frequency of abnormal gait (43,6% x 21,5%; p=0.01), greater use of walking aids (21,8% x 4,6%; p=0.005), greater dysautonomia (56,4% x 47,7%, p=0.002) and osteoporosis [PR 1,64(1.37-1.96), p<0.001) and lower ADL scores [80(80-90) x 90(80-90); p=0.002], CONCLUSION: The presence of RBD in PD patients was associated with indirect indicators of motor impairment, lower independence in ADL, possibly a higher frequency of dysautonomia and with a higher frequency of osteoporosis.

Enhancing Predicted Visual Acuity After SmartSight Lenticule Extraction: Identifying Key Factors With Machine Learning.

To develop a predictive model aimed at assessing the likelihood of improvement in corrected distance visual acuity (CDVA) for patients undergoing lenticule extraction using the SmartSight system from SCHWIND eye-tech-solutions. This model evaluates the effectiveness and weight of various clinical and procedural parameters in predicting enhancements in visual acuity. Data from 1,262 eyes treated with the SmartSight system, encompassing 86 features, were analyzed. Regression and classification techniques were employed to estimate the probability of CDVA gain, ensuring robust results by comparing different methods. The dataset was divided into training (70%, 883 treatments) and testing (30%, 379 treatments) subsets to ensure comprehensive model evaluation using machine learning methods. Feature significance was determined via analysis of variance and principal components analysis to identify impactful parameters. Three principal components accounting for more than 70% of the data variance were identified. The first component was primarily linked to system settings, whereas the second and third components were associated with patient gender and laterality. Factors significantly influencing CDVA improvement included a higher spot-to-track distance ratio, tighter track distance, lower pulse energy, lower average laser power, larger spot distance, greater cap thickness, and lower dosage. These variables were ranked based on their impact on CDVA enhancement. Using low-energy asymmetric spacing for lenticule extraction with the SmartSight system is safe and effective. This approach improves visual outcomes for patients undergoing treatment for myopic astigmatism, offering a reliable method for predicting CDVA improvements. [J Refract Surg. 2024;40(12):e974-e984.].

Reliability and Sustainability Integrated Design Optimization for Engineering Structures with Active Machine Learning Technique

Recent strategic plans from many governments share the same emphasis on sustainable development goals. The construction industry is recognized as one of the primary contributors to greenhouse gas emissions. Stimulated by such political and practical requirements, this research aims to develop a reliability and sustainability integrated design optimization (RSIDO) framework for engineering structures. Statistical modelling is conducted which provides probabilistic models for embodied carbon coefficients (ECCs). The proposed RSIDO framework contains three main stages. The first stage solves the classical reliability-based design optimization (RBDO) problem. The second stage conducts the reliability-based embodied carbon quantification to define the sustainable reliability constraint. Lastly, the new sustainable probabilistic design constraint is embedded in the design question. Furthermore, to overcome the computational difficulty, an active-learning extended-support vector regression (AL-X-SVR) technique is proposed to improve the computational efficiency of the proposed framework. The embedded regression algorithm is presented with consolidated theoretical backgrounds. Further, an active learning strategy is implemented to select the learning samples efficiently. The applicability and effectiveness of the proposed AL-X-SVR method are demonstrated using a highly nonlinear-constrained mathematical validation. Additionally, the RSIDO framework is demonstrated through three engineering applications. This research contributes a comprehensive framework that not only addresses reliability and sustainability but also enhances computational efficiency, thereby introducing an innovative approach to design optimization in building engineering.

Impact of Organizational Shared Values and Value Congruence on Organizational Performance: A Study of IT Companies in Bangalore City

Organisational values are the principles and beliefs that help companies build culture and act as a guiding tool in shaping a company’s behaviour. They are the basis for making decisions and prioritising tasks. Also, aligning values is essential for a committed workforce to enhance performance both at individual and organisational levels. The study examines the effect of organisation-shared values and value congruence on organisational performance. This work was done based on a sample assessment of 550 employees working in IT companies in Bangalore. The response was collected using value taxonomy and value congruence statements. HR dimensions of qualitative organisation performance, such as employee satisfaction, employee morale, turnover and absenteeism, communication, HRM practices, and Top management, were tested using a questionnaire. Regression, correlation, and factor analysis techniques were used to derive the statistical values to support the interpretations. The findings indicated that shared organisational values such as individual values, organisation core values, values about relationships, and societal values have an optimistic impact on the performance of an organisation. Further, the study also revealed that higher congruence values significantly impact organisational performance.

Research on Adaptive Kalman Filtering Adjustment Based on Support Vector Regression and Bayesian Optimization

Abstract This study addresses the performance optimization of Kalman filters in combined navigation systems, particularly the dynamic adjustment of the measurement noise covariance matrix R. An innovative adaptive method that incorporates Support Vector Regression (SVR) and Bayesian optimization techniques is proposed to achieve real-time optimization of the R value. This study initially utilizes the Support Vector Regression model, with innovation data as input, to predict the most suitable R values. Subsequently, the Bayesian optimization algorithm is applied to optimize the key parameters of the SVR model, thus enhancing the accuracy and response speed of the predictions. Comparative experiments with traditional methods demonstrate that the proposed adaptive adjustment method significantly improves the filtering accuracy and stability of the combined navigation system, especially under dynamically changing environmental conditions. This research not only provides a new approach to parameter optimization for Kalman filters but also offers an effective solution for similar real-time system dynamic adjustment issues.

Meat yields and primal cut weights from beef carcasses can be predicted with similar accuracies using in-abattoir 3D measurements or EUROP classification grade.

Three-dimensional (3D) measurements extracted from beef carcass images were used to predict the weight of four saleable meat yield (SMY) traits (total SMY and the SMY of the forequarter, flank, and hindquarter) and four primal cuts (sirloin, ribeye, topside and rump). Data were collected at two UK abattoirs using time-of-flight cameras and manual bone out methods. Predictions were made for 484 carcasses, using multiple linear regression (MLR) or machine learning (ML) techniques. Model inputs included breed type, sex, and abattoir as fixed effects, and cold carcass weight, visually assessed EUROP fat and conformation classes, and 3D measurements as covariates. Machine learning techniques were only used for models including 3D measurements. The CCW and fixed effects resulted in high accuracy (SMY R2=0.72-0.90, RMSE=2.12-3.96kg, primal R2=0.56-0.67, RMSE=0.36-0.91kg), and including the EUROP covariates increased accuracies (SMY R2=0.75-0.96, RMSE=2.00-3.11kg, primal R2=0.58-0.79, RMSE=0.36-0.79kg). The 3D measurement covariates and abattoir resulted in moderate accuracy (SMY MLR R2=0.39-0.58, RMSE=3.26-10.31kg, primal MLR R2=0.33-0.52, RMSE=0.44-1.14kg) and high accuracy when combined with CCW and all fixed effects (SMY MLR R2=0.72-0.95, RMSE=1.81-3.42kg, primal MLR R2=0.52-0.74, RMSE=0.40-0.81kg). The best ML models resulted in similar accuracies to the MLR models. Models including 3D measurements produced similar accuracies to models built using conventional data recorded at the abattoir, indicting the potential for automated prediction.