Linear Statistical Methods Research Articles

Application of Linear Regression Models in Multidisciplinary Data Analysis: From Economics to Health

This paper introduces the application of regression analysis in multiple fields, with a focus on conducting linear regression analysis on five case studies. First, the study on the Consumer Price Index (CPI) and Retail Price Index (RPI) reveals a positive correlation between the two, indicating that fluctuations in retail prices significantly affect consumer prices. Second, an analysis was conducted on the impact of academic pressure on students’ mental health, finding that academic pressure significantly affects emotional and psychological states. Third, the paper compares the animation markets in Japan and China, showing differences in market development models and growth trends between the two countries. Next, the paper explores the relationship between socioeconomic status and family educational expectations, indicating that family income and parents’ education levels significantly influence educational expectations. Finally, the paper examines the relationship between age and sleep quality and duration, finding that both sleep quality and duration decrease as age increases. This paper uses linear regression models and related statistical methods, providing data support for further research in related fields.

Emotional intelligence and self-concept as predictors of academic achievement among secondary school Chemistry students in South-East Nigeria

The research investigated the predictive strengths of Emotional Intelligence (EI) and Self-concept (SC), singly and jointly on academic achievement of secondary school chemistry students. The predictive research design was adopted. The sample comprised 300 SS3 students (150female, 150male) drawn through a multistage sampling technique from 10 co-education public schools in South-East, Nigeria. Instrument for data collection was a questionnaire, which has 3 sections; section A is the demographic information, section B is a 33item Emotional Intelligence inventory adapted from Shuttle {1998), while section C is a 60item Self-concept scale adapted from Rastogi (1979). Data was analysed using the standardised multiple linear regression and the hierarchical/stepwise linear regression statistical methods. Results obtained reveal that, EI and SC significantly predict academic achievement of students both singly and jointly. Influence of gender was insignificant with both EI and SC. Based on findings, it was recommended, among others that classroom practices that aid students understanding and management of emotions as well as boost their self-confidence and capability judgement should be explore, initiated and sustained.

Genomic Prediction of Kernel Water Content in a Hybrid Population for Mechanized Harvesting in Maize in Northern China

Genomic prediction enables rapid selection of maize varieties with low kernel water content (KWC), facilitating the development of mechanized maize harvesting and reducing costs. This study evaluated and characterized the KWC and grain yield (GY) of hybrid maize in northern China and used genomic prediction to identify superior hybrid combinations with low kernel water content at maturity (MKWC) and high GY adapted to northern China. A total of 285 hybrids obtained from single crosses of 34 inbred lines from Stiff Stalk and Non-Stiff Stalk heterotic groups were used for genomic prediction of KWC and GY. We tested 20 different statistical prediction models considering additive effects and evaluating the impact of dominance and epistasis on prediction accuracy. Employing 10-fold cross-validation, it showed that the average prediction accuracy ranged drastically from 0.386 to 0.874 across traits and models. Eight linear statistical methods displayed a very similar prediction accuracy for each trait. The average prediction accuracy of machine learning methods was lower than that of linear statistical methods for KWC-related traits, but the random forest model had a high prediction accuracy of 0.510 for GY. When genetic effects were incorporated into the prediction model, the prediction accuracy for each trait was improved. Overall, the model with dominant and epistatic effects (G:AD(AA)) performed best. For the same number of markers, predictions using trait-specific markers resulted in higher prediction accuracy than randomly selected markers. When the number of trait-specific SNPs was set to 100, the prediction accuracy of GY increased by 33.27%, from 0.406 to 0.541. Out of all the 561 potential hybrids, the TOP 30 hybrids selected by genomic prediction would lead to a 1.44% decrease in MKWC compared with Xianyu335, a hybrid with a fast kernel water dry-down, and these hybrids also had higher GY simultaneously. Our results confirm the value of genomic prediction for hybrid breeding low MKWC suitable for maize mechanized harvesting in northern China. In conclusion, this study highlights the potential of genomic prediction to optimize maize hybrid breeding, enhancing efficiency and providing insights into genotype-accuracy relationships. The findings offer new strategies for hybrid design and advancing mechanized harvesting in northern China.

Mercury’s Meteorite Mysteries: A Directional Statistical Guide to Mercury’s North Pole, Hidden Hazards and Roadmap to Safe Landing Havens Based on Solar Elevation, Ice Stability, Temperature

Studying meteoroid impact patterns on planetary surfaces is critical for understanding surface dynamics and selecting safe landing sites. Mercury, one of the least explored rocky planets, presents unique challenges due to its extreme temperatures and the angular nature of its surface data. Traditional linear statistical methods are often inadequate for analyzing such directional data. This study introduces a novel approach using directional data analysis techniques to interpret the cyclical nature of meteoroid impact locations on Mercury. We employed Watson’s test, Bayesian Information Criterion scores, and a mixture of von Mises–Fisher distributions to model the distribution of impact craters and solar elevations on Mercury’s surface. Our findings indicate that while location parameters adhere to the von Mises distribution, solar elevations do not exhibit directional distribution characteristics. Additionally, by filtering datasets based on temperature thresholds and crater diameters, we pinpointed areas with a high probability of providing suitable landing surfaces. These insights enhance our understanding of the surface conditions on Mercury and provide valuable groundwork for future exploration missions.

ANALISIS PENINGKATAN PENGGUNAAN SEPEDA MOTOR DI KOTA DENPASAR

Currently, the motorcycle population in big cities in Indonesia is increasing very rapidly. Motorcycles are the most popular mode of transportation in Denpasar City. The increase in the use of motorcycles (SPM) has an impact on congestion, air pollution and high traffic accidents involving SPM. This study aims to analyze the increase in the use of SPM in Denpasar City, involving the variables of Household Characteristics, Costs, and Advantages of SPM. Primary data collection was carried out by distributing questionnaires, as many as 400 copies distributed throughout the sub-districts in Denpasar City. The questionnaire contains a Likert scale, containing statements, where respondents provide their perceptions through the available answer choices. Analysis of public perception data was analyzed using multiple linear regression statistical methods. Based on the results of the analysis, the variables of Household Characteristics, Costs, and Advantages of SPM have a significant positive effect on the Increase in the use of SPM, each with tcount = 6.110; 6.847; and 7.865> ttable = 1.966; and all sig values. 0.000 < 0.05. The influence of the three variables together is Fcount = 209.162 > Ftable = 0.072. Sig. value = 0.000 < 005. The equation model obtained is: Y = 0.296X1 + 0.294X2 + 0.290X3 + e.

Pengaruh Corporate Governance, dan Karakteristik Perusahaan Terhadap Kinerja Keuangan Perusahaan: Studi pada Perusahaan Barang Konsumen Primer Sub Sektor Makanan dan Minuman yang Terdaftar di Bursa Efek Indonesia Tahun 2019-2022

This research aims to find out how much influence corporate governance and company characteristics have on the financial performance of food and beverage sub-sector manufacturing companies listed on the Indonesia Stock Exchange in 2019-2022. Sample selection was carried out using purposive sampling to obtain eighteen companies that met the requirements as samples. The data used in this research is secondary data in the form of financial reports obtained through the Indonesia Stock Exchange (IDX) with a total of 72 financial reports observed. This research uses multiple linear regression statistical methods in the IBM SPSS Statistics 22 application. The results of this research show that: (1) corporate governance has a positive and insignificant effect on financial performance, (2) company characteristics have a positive and significant effect on the company's financial performance, and (3) simultaneously corporate and company characteristics have a positive and significant effect on the financial performance of manufacturing companies in the food and beverage sub-sector, with a coefficient of determination value of 7.9% while the remaining 92.1% is influenced by other factors not included in this research.

Influence Of Financial Literacy And Financial Management Behavior Among Students Of UIN Smh Banten

From survey data conducted by the Financial Services Authority regarding national finance, the financial literacy index in Banten is still below the national average of 45.19 percent. This was conveyed directly by the Head of OJK Jabodetabek and Banten Province in 2022. The low financial literacy index in Banten, OJK invites to jointly improve finance in the community, especially in students. Financial literacy is knowledge, skills, beliefs that influence a person's financial attitudes and behavior to improve the quality of decision making and financial management in order to achieve prosperity. This study uses a survey method in collecting answers from respondents. Using 50 samples of students at Banten State Islamic University who were randomly selected. The results of data collection through questionnaires that will be processed and analyzed using linear regression statistical methods. Based on this, it was found that financial literacy with indicators of basic knowledge of personal finance, personal financial records, and financial behavior had an effect on student financial management.

Corporate Social Responsibility, Corporate Financial Performance and the Confounding Effects of Economic Fluctuations

This study aims to explore the relationship between Corporate Social Responsibility (CSR) and corporate financial performance in five different countries: USA, UK, Germany, Japan, and Australia. Data is obtained from studies that measure CSR through various metrics, such as CSR Index, CSR Score, CSR Disclosure, CSR Rating, and CSR Spending, while financial performance is measured using various indicators such as Return on Assets (ROA), Return on Equity (ROE), Net Profit Margin, Tobin's Q, Operating Income, and Market Value. Data analysis was performed using linear regression statistical methods to evaluate the relationship between CSR variables and financial performance. It found that there was a significant positive relationship between CSR and corporate financial performance in all countries studied. Companies that are more active in CSR activities tend to record better financial performance. Variations between countries suggest that local context influences the strength of these relationships. The managerial implication of these findings is that investing in CSR can improve a company's reputation, strengthen its position in the market, and improve long-term financial performance. However, further studies are still needed to understand the factors influencing the relationship between CSR and financial performance across various industry and geographic contexts.

Estimation of potato yield using a semi-mechanistic model developed by proximal remote sensing and environmental variables

Timely monitoring of potato yield status is crucial for guiding field management and ensuring the sustainability of food trade. In recent years, non-destructive remote sensing techniques have emerged as one of the most promising methods for crop yield monitoring. However, most existing potato yield estimation models have primarily been tested at specific growth stages within a single ecosystem, highlighting the urgent need for a model that can be applied across different ecological environments. In this study, we developed a semi-physical model based on hierarchical linear modeling (HLM) to automatically correct yield estimation based on remotely sensed vegetation indices using environmentally relevant variables such as net solar radiation at the surface (ssr), air temperature at 2 m above the surface (t2m), and soil water content in the root zone (swvl). The accuracy and transferability of the model were further evaluated by comparing it with aboveground destructive sampling methods, conventional empirical models, and machine learning approaches. The results demonstrated that site-specific relationships exist between aboveground agronomic traits parameters like biomass and leaf area index (LAI) with respect to yield estimation. Conventional linear statistical and machine learning methods faced challenges in transferring their constructed yield estimation models across regions and years. However, the HLM approach exhibited excellent generalization ability across all trial samples. In the middle and later stages of potato growth, the standardized LAI determining index proved to be the most effective vegetation for predicting yield, while ssr, sktmax, and shallow swvl were identified as key environmental variables affecting yield. By integrating these variables using HLM modeling techniques, we achieved optimal generalization performance (R2 = 0.57, RMSE = 7.65 t/ha, NRMSE = 25.02 % during tuber growth stage; R2 = 0.60, RMSE = 7.37 t/ha, NRMSE = 24.13 % during starch accumulation stage). This study underscores the importance of considering environmental factors when constructing a reliable model for estimating potato yield at different spatial and temporal scales using remote sensing technology.

Analysis of the Effect of an Open Economy on Indonesia's National Income

This research aims to examine the impact of various factors, such as the value of exports and the value of imports, on Indonesia's total national income. These factors are considered as independent variables, while the amount of Indonesia's national income is the dependent variable. The research methodology involves a descriptive quantitative approach and explanatory research, with a focus on the analysis of Indonesia's national income from 1993 to 2024. The required data was obtained from secondary sources such as the Central Statistics Agency, Uncomtrade , Trademap , books, and similar research-related publications. To analyze the data, multiple linear regression statistical methods were used with the help of SPSS version 27 software. The results of the analysis show that overall (F test), the factors studied have a significant influence on the amount of national income. Furthermore, partial analysis (t test) shows that the variables of Indonesia's export value and import value also have a significant influence on Indonesia's total national income.

Hyperspectral remote sensing of forage stoichiometric ratios in the senescent stage of alpine grasslands

Senescence is an important phenological stage in the life cycle of grassland plants, and estimating grass senescence in alpine grasslands is particularly crucial for animal husbandry and grazing the utilization of grassland resources for grazing. However, the application of proximal sensing technologies to capture the variations in forage growth parameters in the senescent stage, especially regarding some key stoichiometric ratios, still faces challenges associated with indiscernible spectral properties and indeterminate absorption and reflectance features. The present study aims to demonstrate the potential and effectiveness of applying hyperspectral data to assess the forage carbon-nitrogen (C:N) ratio and calcium-phosphorus (Ca:P) ratio during the senescent stage (September to November). A progressive variable dimensionality reduction strategy based on a genetic algorithm (GA) and hybrid optimization method (ant optimization colony (ACO) and extreme learning machine (ELM)) coupled with multiple linear and nonlinear statistical methods is applied to develop C:N ratio and Ca:P ratio estimation models via combination with data from 205 sample sites collected from six field campaigns (2016–2019) on the eastern Tibetan Plateau. The results show that the important spectral bands favorable for forage C:N ratio and Ca:P ratio retrieval are generally in the red and shortwave infrared (SWIR) regions, and the proposed model presents satisfactory performance in the estimation of the forage C:N ratio (V-R2 = 0.81, V-RMSE = 6.44) and Ca:P ratio (V-R2 = 0.64, V-RMSE = 2.54) during senescence. Moreover, the model can effectively overcome the spatial differences among sampling areas and perform optimally in capturing the variations in the forage C:N ratio and Ca:P ratio in the early and middle stages of senescence. Overall, our study demonstrates that it is feasible and promising to estimate these stoichiometric ratios using hyperspectral feature bands during grass senescence at the canopy level, with potential applications that may further enhance the monitoring of forage quality and quantity.

Pengaruh Semangat Kerja dan Inovasi Kerja Terhadap Kualitas Kerja Pegawai pada Dinas Pendidikan dan Kebudayaan Provinsi Gorontalo

This research aims to find out how much influence work enthusiasm and work innovation have on the quality of work of employees at the Gorontalo province education and culture service. The population in this study were employees at the Gorontalo province education and culture service, namely 156 employees. The sampling technique used was random sampling technique. The number of samples in this study was 112 respondents and used the Slovin formula. Data collection uses questionnaires, observation, documentation and interviews. Analysis test tools using multiple linear regression analysis and statistical analysis methods using the SPSS 26 application. The results of this research show that, (1) work morale has a positive and significant effect on employee work quality; (2) work innovation has a positive and significant effect on employee work quality; (3) Simultaneously (together) work enthusiasm and work innovation have a positive and significant effect on work quality. To improve the quality of employee work, agencies need to pay attention to increasing employee morale and work innovation, so that work discipline and responsibility can be implemented that target improvements in solving problems in every job. So that if the work spirit and work innovation of employees at the Gorontalo provincial education and culture service is better, it will have an impact on improving the quality of work in every job they do.

The Moderator Effect of Cyberloafing on Workload and Employee Performance

Multi-finance companies require technology to carry out their business activities. However, its employees are prone to cyberloafing. In addition, a high workload can also affect the performance of employees. This study aimed to analyze the impact of cyberloafing and workload on the performance of multi-finance companies in Palembang. Data were collected through questionnaires. The sample in this study was company employees at multi-finance companies in Palembang. The data was analyzed using multiple linear regression statistical methods. The results of this study were expected to provide a broader understanding of the impact of cyberloafing and workload on the performance of multi-finance company employees in Palembang. The results of this study were also expected to provide insight to managers and organizational leaders in developing effective strategies to overcome cyberloafing problems and minimize employee workload to improve their performance. This research can also provide input to companies to optimize the use of technology and efficient division of workload in the workplace to increase employee productivity. Keywords: cyber loading, financing growth, workload, performance

Covid-19 Outcome Prediction Model by Using Radio-Diagnostic Methods

Research goal: The goal of this research is to create a prediction model for a possible outcome (death or survival) of COVID-19, which model incorporates the easily available radio diagnostic methods such as classical radiology and the disease scoring system. Part of the goal of this study is to define the chances and probabilities of occurrence of death as a result of the primary disease and to identify the risk factors that have the highest influence on the final outcome of COVID-19. Methodology: The methodological approach used in this study is the binary logistic regression which is part of the group of generalized linear statistical methods. Results: Results show that patients with complications and comorbidities have the highest chances of death from COVID-19 (OR 16,53 with CI 8,21 - 33,25 and 4,08 with CI 1,34 - 12,38). Men are also exposed to higher but insignificant mortality risk with OR 1,55 with CI 0,86 - 2,80. Every additional year of age increases the mortality risks by 1,06 times (CI 1,03 - 1,09), while every additional score of the primary disease leads to increased chances of unwanted outcome by 1,24 (CI 1,04 - 1,47). Conclusion: The mortality outcome of COVID-19 is not an exclusive consequence of the primary disease but it is usually determined in correlation with different comorbidities and existing complications as well as other standard influencing factors such as age and gender. Contribution and significance of the research: The primary importance of this research is the fact that it allows for an improved precision and upgrade to the basic model of standard factors by using new predictors, specifically secondary complications from the radio-graphic picture and scoring of the primary disease, which leads to higher utilization of cheap and easily available radio-diagnostic methods.

Exploration and demonstration of explainable machine learning models in prosthetic rehabilitation-based gait analysis.

Quantitative gait analysis is important for understanding the non-typical walking patterns associated with mobility impairments. Conventional linear statistical methods and machine learning (ML) models are commonly used to assess gait performance and related changes in the gait parameters. Nonetheless, explainable machine learning provides an alternative technique for distinguishing the significant and influential gait changes stemming from a given intervention. The goal of this work was to demonstrate the use of explainable ML models in gait analysis for prosthetic rehabilitation in both population- and sample-based interpretability analyses. Models were developed to classify amputee gait with two types of prosthetic knee joints. Sagittal plane gait patterns of 21 individuals with unilateral transfemoral amputations were video-recorded and 19 spatiotemporal and kinematic gait parameters were extracted and included in the models. Four ML models-logistic regression, support vector machine, random forest, and LightGBM-were assessed and tested for accuracy and precision. The Shapley Additive exPlanations (SHAP) framework was applied to examine global and local interpretability. Random Forest yielded the highest classification accuracy (98.3%). The SHAP framework quantified the level of influence of each gait parameter in the models where knee flexion-related parameters were found the most influential factors in yielding the outcomes of the models. The sample-based explainable ML provided additional insights over the population-based analyses, including an understanding of the effect of the knee type on the walking style of a specific sample, and whether or not it agreed with global interpretations. It was concluded that explainable ML models can be powerful tools for the assessment of gait-related clinical interventions, revealing important parameters that may be overlooked using conventional statistical methods.

Help-Seeking Intention and Its Determining Factors Among High School Students in Iran

Background: With a global prevalence of mental disorders among adolescents, exacerbated by the coronavirus disease 2019 (COVID-19)’s impact, understanding the dynamics of seeking assistance is crucial. Therefore, this study determined the status of the help-seeking intention and the factors affecting it among high school students in Saveh (center of Iran). Methods: In this cross-sectional study conducted in 2023, 400 students from different high schools were selected by a multi-stage stratified random sampling procedure. They were studied using the Short Form of Mental Health Continuum Questionnaire (MHC-SF), Attitude Toward Seeking Professional Psychological Help Questionnaire (ATSPPH), Help-seeking Intention Questionnaire (GHSQ), and perceived barriers related to mental health services. Data were analyzed via SPSS 21 software using linear regression and descriptive statistical methods. Furthermore, P<0.05 was considered statistically significant. Results: The mean score attitude toward mental health services, perceived barriers to mental health services, and positive mental health were 33.8±5.5, 23.5±3.8, and 42.36±10.39, respectively. Close friends were favored for informal help-seeking (4.3±0.97), while doctors preferred formal help-seeking (3.4±0.91). Furthermore, attitude toward mental health services was the most important predictor of formal help-seeking (P value<0.001; β=3.89), while positive mental health was the most important predictor of informal help-seeking (P value<0.001; β=2.93). Conclusion: The findings of this study identified friends, family, and doctors as primary sources of informal and formal help-seeking among adolescents. Mental health literacy education is crucial for these groups. Given boys’ lower help-seeking intentions and the influence of attitudes and perceived barriers, comprehensive training is recommended. Hence, it is essential to incorporate mental health literacy programs within school curricula. Moreover, school counselors should receive comprehensive training to identify and address mental health issues effectively among students.

Pengaruh Sikap Skeptisme, Profesionalisme Dan Locus Of Control Pada Kualitas Audit Investigatif Dalam Mengungkap Fraud

This study aims to determine the influence of skepticism, professionalism, and locus of control on audit quality in the context of uncovering fraud. The hypothesis in this study is that skepticism has a positive and significant effect on audit quality in uncovering fraud, professionalism has a positive and significant effect on audit quality in uncovering fraud but locus of control has a negative and insignificant effect on audit quality in uncovering fraud. The research method used is multiple linear regression statistical methods. Quantitative data is primary data. Primary data in the form of questionnaires were collected from auditors at KAP Denpasar City. The results showed that auditor skepticism and auditor professionalism had a positive effect on audit quality in uncovering fraud. However, locus of control hurts audit quality in disclosing fraud. Keywords: Skepticism, professionalism, locus of control, investigative audit quality, fraud

Corruption Vs Human Development: An Empirical Analysis

Background: Corruption is a significant issue facing the modern world. Corruption hinders social, political, economic, and environmental growth, causing inequality and a decrease in public spending. Research demonstrates that corruption adversely affects human capital improvement, ecological sustainability, and political frameworks, hindering progress at both high and low levels. Method: This paper examines the relationship between corruption and human development and how each influences the other, using 2018 data from 100 countries. This study uses a linear regression model and statistical methods like goodness of fit, hypothesis testing, and correlation coefficients to look into how the Corruption Perception Index (CPI) and the Human Development Index (HDI) affect each other. Result: The corruption perception index (CPI) and the human development index (HDI) have an important positive correlation (0.7708). Models explain 62.08% of HDI variation and 57.28% of CPI variation, depending on regression analysis. This implies that higher CPI (less corruption) notably enhances HDI (human development), and vice versa. Discussion: Findings show a strong correlation between higher human development (higher HDI) and lesser corruption (higher CPI), which has a major impact on social and economic circumstances. Though several countries have progressed with decreasing corruption, perceptions across the world remain unchanged, and numerous fields are still experiencing difficulties. Conclusion: This study's findings demonstrate a significant inverse relationship between corruption and human development. With the goal of fighting corruption and enhancing human development, it emphasizes the importance of a wide range of indicators, including technical developments, institutional reforms, and international cooperation.

The Effect of Accounting Lecturers’ Interest in Learning and Professionalism on the Level of Accounting Understanding (Empirical Study of Accounting Students, Faculty of Economics and Business, Untan, Class of 2021-2022)

This study aims to analyze the influence of learning interest and professionalism of accounting lecturers on the level of accounting understanding, with students of the Accounting Study Program, Tanjungpura University Pontianak. The type of research used is quantitative research. This study is based on the collection of primary data involving questions in a questionnaire format that is assessed using a Likert scale. The population of this study is all students of the Accounting study program, Faculty of Economics and Business, Tanjungpura University, Pontianak, Class of 2021 and 2022. The study used a sample of 83 students who were selected based on certain criteria, and hypothesis analysis was carried out through multiple linear regression statistical methods and data processing using SPSS 23. The findings of this study show that the interest in learning and professionalism of accounting lecturers which have a significant positive influence affects the level of accounting understanding of students of the Accounting study program, Faculty of Economics and Business, Tanjungpura University, Pontianak.

Students’ Multiple Perceptions of English Teaching Model Reform in Colleges and Universities under Cognitive Coupling Networks

Abstract On the basis of linear multiple regression methods such as PLSR and ICR, this paper introduces the kernel function to kernelize these linear multiple statistical regression methods, derives the KPLSR and KICR algorithms, and gives the specific implementation process. Then kernel-based multiple regression methods are applied to analyze the English teaching mode using a cognitive coupling network. According to the students’ learning data under the English teaching mode of cognitive coupling network, then the students’ learning situation and other precise calculations and statistics, so as to truly reflect the student’s learning status, presenting the students’ learning process in need of an urgent solution. Finally, the correlation between students’ multi-sensory learning styles and English performance is analyzed, and the results show that visual, auditory, kinesthetic, experiential, and independent styles all show a significant positive correlation to the total English performance, with correlation coefficients of 0.416, 0.158, 0.349, 0.339, and 0.327, respectively, among which the correlation coefficient of the visual type of perception is the largest.