Linear Regression Method Research Articles

Prakiraan Jumlah Curah Hujan Berdasarkan Data Parameter Cuaca Tahun 2011-2021 di Kota Pangkalpinang

Tropical weather prevails in the Pangkalpinang city region, complemented with a changeable rainy season that experiences monthly variations. This phenomena results in a considerable amount of precipitation in the city of Pangkalpinang, making the area known for its extreme rainfall. Using multiple linear regression approaches to anticipate monthly precipitation is the aim of this work. Rainfall (RR), air temperature (T), air humidity (RH), and air pressure are all part of the dataset that was collected over an 11-year period (2011–2021). The information was obtained from the meteorological station in Depati Amir Pangkalpinang. This study uses the multiple linear regression method. The rainfall forecast results are compared to the observed actual rainfall amounts. Based on air temperature and air humidity as predictors, the results show that the estimation of cumulative monthly precipitation in the Pangkalpinang city region for the year 2021 produced an average root mean square error (RSME) value of 112.6 mm/month. Furthermore, the T-test analysis produced a 0.9 result. With air temperature, humidity, and pressure as predictors, the total monthly rainfall for 2021 was predicted with an average root mean square error (RMSE) value of 107.9 mm/month. A T-test was also performed, and the result was 0.4. The results of this study show that the monthly rainfall forecast model for the city of Pangkalpinang that includes factors related to temperature, humidity, and pressure produces better results than the model that only includes these variables. November and December are the months with the greatest variations.

Comprehensive analysis of the mining accident forecasting and risk assessment methodologies: Case study – Stanterg Mine

Purpose. This research aims to outline a methodology for accident forecasting and risk assessment in mining operations using the Stanterg Mine as a case study. It emphasises the crucial role of reliable mining accident reporting and accurate data processing in forecasting accidents and effectively managing risks during mining operations. Methods. The research paper analyzes various methodologies for forecasting mining accidents using Excel and Simple Linear Regression Method (SLRM) to analyse data selected from the Stanterg Mine accidents. Findings. The forecast indicates that on average there are about 3 accidents per month at the Stanterg Mine. The analysis, based on a one-month study of 42 reported accidents, and assuming a steady production rate, suggests an increased risk of accidents. This is supported by a thorough assessment using a 3×3 risk assessment matrix tailored to the Stanterg Mine. Stope mining is highlighted as the most hazardous area, associated with risks ranging from moderate to extreme levels. Originality. Mining accident analysis at the Stanterg Mine involves an examination of the incidents, including factors leading to accidents, encountered hazards and their consequences. Accident forecasting entails studying historical data, identifying patterns, and using predictive modelling to anticipate future incidents. This proactive approach enables mining companies to proactively address risks and take preventative measures, reducing the probability of accidents. Practical implications. The systematic processing and analysis of mining accidents has revealed valuable insights into the practical application of risk assessment in mining operations. The examination of accidents at the Stanterg Mine provides researchers with crucial knowledge for effective risk assessment and management in the mining sector.

Analisa Faktor Yang Mempengaruhi Financial Sustainability Ratio Sektor Perbankan Di Bei Periode 2019-2022

Financial Sustainability in banking is one of the important things to measure the bank's sustainability in the future, especially from a financial perspective. The sustainability of the banking sector must be a concern because there are many parties who will have an impact if a bank experiences failure to operate, especially in its finances. This research aims to examine the influence of BOPO, DebtToEquity, ReturnOnAsset, LDR, Directors, Commissioners on the Financial Sustainability Ratio (FSR) in Idx banking sector companies -Ic Period 2019-2022. Sampling was carried out using purposive sampling technique. This type of research uses multiple linear regression methods. The object of this research is the Idx-Ic banking sector financial report for the 2019-2022 period. The research results show that only the Director variable of the six independent variables used has an influence on the Financial Sustainability Ratio (FSR) and the research model is suitable for use. Companies that want to maintain the FSR value should pay attention to the number and selection of directors they have.

The Influence of Brand Image, Product Quality, and Customer Service on Purchase Decision at Current Jaya Store in Madiun City

The purpose of this study is to ascertain whether Lancar Jaya Stores customers in Madiun City made a purchase of PVC ceiling based on brand image, product quality, and service quality. This study employs a quantitative approach to research. in studies employing multiple linear regression methods. Utilizing the Purposive Sampling method for sampling. There were 96 respondents in the sample. It is demonstrated by statistical tests that product quality has a positive and significant impact on decisions about what to buy, service quality has a positive and significant impact on decisions about what to buy, and the image of the brand”has a partial effect on purchasing decisions .. Purchasing decisions are influenced simultaneously.

Energy Efficiency Assessment of Existing Rural Houses in Nantong Based on Human Thermal Comfort

Current research on rural houses in China mainly focuses on improving energy efficiency, with relatively few studies addressing energy-saving measures and enhancing thermal comfort for residents. Therefore, this paper focuses on existing rural houses in Nantong City, Jiangsu Province, as the research object. Through on-site measurements and questionnaire surveys, it was found that the average indoor temperature of rural houses is 28.5 °C in summer and below 10 °C in winter, failing to meet the comfort needs of the villagers. To further study human thermal comfort, a linear regression method was used to establish an indoor Mean Thermal Sensation (MTS) model for Nantong’s rural houses. The neutral temperature in summer was found to be 26.46 °C, and an adaptive thermal comfort model for rural residents in the Nantong area was established. Through single-factor simulation and orthogonal experiments, the optimal comprehensive energy-saving renovation scheme was proposed. Finally, a typical rural house in Zhangzhuang Village was used as a case for building renovation practice. After the renovation, the number of thermal comfort hours increased by 145 h per year, the thermal comfort compliance rate reached 47.07%, and the overall energy-saving rate was 57.41%.

Multiple Linear Regression Method for Thermal Maturity Prediction Based On Well Logs

Multiple Linear Regression Method for Thermal Maturity Prediction Based On Well Logs

Comparative Analysis of Multiple – Linear Regression Algorithm with Random Forest Regression for Prediction of House Plot Prices

Humans basically have a basic need to have a place to live, which can be a house or shelter. Along with the rapid population growth in Indonesia, which continues to increase every year, many people do not have or have a decent place to live. Therefore, careful planning is needed so that every family can have a decent home. One very important aspect in planning investment in the form of property is predicting future house prices. One approach that can be used is to use a Random Forest and Multiple Linear Regression algorithm, which is an algorithm from Machine Learning. There are several factors that can influence the price of a house, including land area, building area, number of bedrooms, bathrooms and garage. In this research, multiple linear regression and random forest regression methods were chosen. The aim of this research is to find the best prediction results between the two methods. To achieve accurate predictions, research was carried out repeatedly by dividing the dataset into 80% for training and 20% for testing. The research results show that the random forest regression algorithm provides the best results, with an accuracy of 81.6%.

Influence of Economic Freedom, Macroeconomic Variables, and The Covid-19 Pandemic On Economic Growth

The aim of APEC cooperation is to promote sustainable and inclusive economic growth, as well as enhance cooperation and economic integration among its members. This study aims to analyze the influence of economic freedom, foreign direct investment, international trade, labor force, inflation, and the Covid-19 pandemic on economic growth in the 19 APEC member countries using multiple linear regression method. This research examines the impact of economic freedom, foreign direct investment, international trade, labor force, inflation, and the Covid-19 pandemic on economic growth. The analysis results indicate that economic freedom, although important, may have limited impact in weak institutional settings. Conversely, foreign direct investment and international trade have proven to have significant positive effects by bringing capital, technology, and innovation as well as enhancing economic efficiency. Moreover, skilled labor force and controlled inflation also support economic growth. However, the Covid-19 pandemic has caused serious disruptions by disrupting supply chains, decreasing demand, and creating economic uncertainty, highlighting the importance of economic resilience and public health. These findings emphasize the importance of policies supporting these factors to drive sustainable economic growth.

Spatiotemporal interactions between soil moisture and water availability across the Yellow River Basin, China

Study regionYellow River Basin (YRB) in China. Study focusThis study attempts to shed new light on regional land-atmosphere coupling and relevant impacts on basin-scale water resource management. Specifically, the objectives are to investigate the driving factors and physical mechanisms of SM changes via land-atmosphere coupling. New hydrological insights for the regionEcological conservation and the high-quality development in the YRB stand as a pivotal national strategy. The equilibrium of water availability (PME，precipitation minus evapotranspiration) poses a significant challenge to the sustainability of the basin's ecosystem. Unfortunately, a comprehensive examination of the regional response, spatiotemporal heterogeneity, and the mechanism governing soil moisture (SM) in response to PME remains limited. An enhanced multiple linear regression method is implemented to quantify monthly sensitivity coefficients, revealing a notable correlation: reduced SM in arid regions correlates with heightened PME. This decline in SM triggers a decrease in evapotranspiration, attenuates the cooling effect of evapotranspiration, and amplifies temperature disparities. Consequently, this process results in an intensified boundary layer and tropospheric ascending motion, thereby increasing water vapor transport. This feedback loop is most pronounced during drought conditions, particularly in summer arid areas (sensitivity coefficient =-0.27). The findings underscore the intricate interplay between land and atmosphere, elucidating the discernible impact of climate change on water resources at a sub-basin scale.

Privacy-preserving model evaluation for logistic and linear regression using homomorphically encrypted genotype data

Objective:Linear and logistic regression are widely used statistical techniques in population genetics for analyzing genetic data and uncovering patterns and associations in large genetic datasets, such as identifying genetic variations linked to specific diseases or traits. However, obtaining statistically significant results from these studies requires large amounts of sensitive genotype and phenotype information from thousands of patients, which raises privacy concerns. Although cryptographic techniques such as homomorphic encryption offers a potential solution to the privacy concerns as it allows computations on encrypted data, previous methods leveraging homomorphic encryption have not addressed the confidentiality of shared models, which can leak information about the training data. Methods:In this work, we present a secure model evaluation method for linear and logistic regression using homomorphic encryption for six prediction tasks, where input genotypes, output phenotypes, and model parameters are all encrypted. Results:Our method ensures no private information leakage during inference and achieves high accuracy (≥93% for all outcomes) with each inference taking less than ten seconds for ∼200 genomes. Conclusion:Our study demonstrates that it is possible to perform linear and logistic regression model evaluation while protecting patient confidentiality with theoretical security guarantees. Our implementation and test data are available at https://github.com/G2Lab/privateML/.

An In Silico Study Based on QSAR and Molecular Docking and Molecular Dynamics Simulation for the Discovery of Novel Potent Inhibitor against AChE.

Acetylcholinesterase (AChE) is one of the main drug targets for treating Alzheimer's disease. This current study relies on multiple molecular modeling approaches to develop new potent inhibitors of AChE. We explored a 2D QSAR study using the statistical method of multiple linear regression based on a set of substituted 5-phenyl-1,3,4-oxadiazole and N-benzylpiperidine analogs, which were recently synthesized and proved their inhibitory activities against acetylcholinesterase (AChE). The molecular descriptors, polar surface area, dipole moment, and molecular weight are the key structural properties governing AChE inhibition activity. The MLR model was selected based on its statistical parameters: R2 = 0.701, R2test = 0.76, Q2CV = 0.638, and RMSE = 0.336, demonstrating its predictive reliability. Randomization tests, VIF tests, and applicability domain tests were adopted to verify the model's robustness. As a result, 11 new molecules were designed with higher anti-Alzheimer's activities than the model molecule. We demonstrated their improved pharmacokinetic properties through an in silico ADMET study. A molecular docking study was conducted to explore their AChE inhibition mechanisms and binding affinities in the active site. The binding scores of compounds M1, M2, and M6 were (-12.6 kcal/mol), (-13 kcal/mol), and (-12.4 kcal/mol), respectively, which are higher than the standard inhibitor Donepezil with a binding score of (-10.8 kcal/mol). Molecular dynamics simulations over 100 ns were used to validate the molecular docking results, indicating that compounds M1 and M2 remain stable in the active site, confirming their potential as promising anti-AChE inhibitors.

Research on factors influencing household consumption-take wine as an example

It has been a great increase on wine consumptions these years. This article aims to identify those of the factors that have an impact on the wine consumptions and to perform clustering to summarize customer segmentation markets. The method of stratified multiple linear regression and stepwise multiple linear regression are used in this article. This article analysis fourteen important factors that may have an effect on wine consumption of a survey on 2240 registered consumers. Through the research in this article, it can be concluded that the consumption is strongly related to age, education, income, the number of times one visits the store. Meanwhile, the number of children in the family has a significant negative impact on alcohol purchase. Whats more, the study also finds that making some promotions could have a positive impact on wine consumption. This article also makes a guideline to the company to better understand their customers.

Research on Influencing Factors of Weibo Users' Intention to Use "Visitor Record" Function Based on UTAUT Model

The purpose of this paper is to explore the influencing factors of users' use of "visitor record" function from the two dimensions of technology itself and users' social media use psychology. Based on the integrated information technology acceptance and use Model (UTAUT), and based on the perspective of users' social media use, two factors of "self-disclosure degree" and "missing anxiety" were introduced, and the Weibo user group was taken as the research object through questionnaire survey method. Based on the 313 questionnaires obtained by screening, the analysis was conducted by hypothesis testing and multiple linear regression methods. The effects of integrated technology acceptance model factors and two newly introduced factors on user intention and behavior were explored. Performance expectancy, effort expectancy, social influence and facilitating factors have significant positive effects on users' intention to use the "visitor record" function of Weibo. The facilitating factors have a negative influence on the user's use behavior of the "visitor record" function of microblog. The introduction of new perspectives, including fear of missing out and degree of self-disclosure, provides new ideas for further understanding of social media user behavior and subsequent related research.

The Effect of Relationship Marketing and Customer Satisfaction on Customer Loyalty at PT. Bank Muamalat Indonesia

This study aims to find out and analyze the influence of relationship marketing and customer satisfaction on customer loyalty at PT. Bank Muamalat Indonesia Tbk Makassar Branch. The type of research used is quantitative. The population in this study is customers at PT. Bank Muamalat Indonesia Tbk Makassar Branch. Sample selection uses the purposive sampling method. The data collection method uses a questionnaire. The statistical method used in this study is the multiple linear regression method and uses SPSS 22.0 software. The results of the study show that relationship marketing has a positive and insignificant effect on customer loyalty, customer satisfaction has a positive and significant effect on customer loyalty. Simultaneously, relationship marketing and customer satisfaction have a significant effect on customer loyalty.

Temperature analysis and prediction for road-rail steel truss cable-stayed bridges based on the structural health monitoring

A growing number of road-rail steel truss bridges have been built worldwide in recent years. Due to the complex cross-sectional structure and increasing span length, these main girders are prone to developing uneven temperature fields and significant local thermal effects, which can lead to excessive temperature stresses and structural damage. An accurate assessment of the temperature distribution in road-rail steel truss girders is critical as it aids in the thermal response analysis of the bridge and the structural safety evaluation. The paper analyses the temperature distribution of the road-rail steel truss girder base on the light conditions and the structure of the main girder. A modified method for calculating the effective temperature (ET) and temperature difference (TD) is also proposed. Comparing with the conventional regional weighted averaging method, the modified method improves the accuracy of ET calculation by 20 %. A prediction method based on the long short-term memory (LSTM) network is proposed, which estimates ET and TD for road-rail steel truss girders based on the environment around the bridge. The deep learning-based method improves accuracy by 40 % compared with the conventional linear regression method for ET prediction. Meanwhile, the proposed method alleviates the current lack of prediction methods for TD of road-rail steel truss girders. A detailed analysis of the temperature of a road-rail steel truss bridge is also presented based on the structural health monitoring (SHM) system. A negative vertical temperature difference was observed in the fringe truss. The maximum transverse temperature difference (TTD) was much greater than the maximum vertical temperature difference (VTD). The above features deserve extra attention from bridge designers.

Exploring unknown parent groups and metafounders in single-step genomic best linear unbiased prediction: Insights from a simulated cattle population

This study explores how the metafounder (MF) concept enhances genetic evaluations in dairy cattle populations using single-step genomic best linear unbiased prediction (ssGBLUP). By improving the consideration of relationships among founder populations, MF ensures accurate alignment of pedigree and genomic relationships. The research aims to propose a method for grouping MF based on genotypic information, assess different approaches for estimating the gamma matrix, and compare unknown parent groups (UPG) and MF methodologies across various scenarios, including those with low and high pedigree completeness based on a simulated dairy cattle population. In the scenario where unknown ancestors are rare, the impact of UPG or MF on breeding values is minimal but MF still performs slightly better compared with UPG. The scenario with lower genotyping rates and more unknown parents shows significant differences in evaluations with and without UPG and also compared with MF. The study shows that ssGBLUP evaluations where UPG are considered via Quaas-Pollak-transformation in the pedigree-based and genomic relationship matrix (UPG_fullQP) results in double counting and subsequently in a pronounced bias and overdispersion. Another focus is on the estimation of the gamma matrix, emphasizing the importance of crossbred genotypes for accuracy. Challenges emerge in classifying animals into subpopulations and further into MF or UPG, but the method used in this study, which is based on genotypes, results in predictions which are comparable to those obtained using the true subpopulations for the assignment. Estimated validation results using the linear regression method confirm the superior performance of MF evaluations, although differences compared with true validations are smaller. Notably, UPG_fullQP's extreme bias is less evident in routine validation statistics.

Theoretical Studies on the Insertion Reaction of Polar Olefinic Monomers Mediated by a Scandium Complex

This study aimed to investigate the insertion reaction of the polar monomers mediated by the cationic rare earth metal complex [(C5H5)Sc(NMe2CH2C6H4-o)]+ utilizing a combination of density functional theory (DFT) calculations and multivariate linear regression (MLR) methods. The chain initiation step of the insertion reaction could be described by the poisoning effect and the ease of monomer insertion, which could be represented via the DFT-calculated energy difference between σ- and π-coordination complexes (ΔΔE) and insertion energy barrier (ΔG≠), respectively. The results indicate that ΔΔE and ΔG≠ can be predicted by only several descriptors using multiple linear regression methods, with a root mean squared error (RMSE) of less than 2.5 kcal/mol. Furthermore, the qualitative analysis of the MLR models provided effective information on the key factors governing the insertion reaction chain initiation.

Chemoinformatics Study of Benzodiazepine-1, 2, 3-triazole Derivatives Targeting Butyrylcholinesterase.

This study aims to assess the potential bioactivity of newly designed benzodiazepine-1,2,3-triazole derivatives using in-silico methodologies, with a primary focus on elucidating their inhibitory interactions with the butyrylcholinesterase (BuChE) enzyme, which is implicated in Alzheimer's disease. We employed multiple linear regression (MLR) methods to conduct a quantitative structure-activity relationship (QSAR) analysis on a collection of 31 benzodiazepine-1,2,3-triazole derivatives, with the goal of investigating, assessing, and predicting their activities, as well as designing novel compounds. This approach yielded highly accurate results, with coefficients of determination (R²) of 0.77 and 0.81 for the training and test datasets, respectively. Additionally, the optimized compounds were subjected to an Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) analysis, demonstrating their potential as non-hepatotoxic agents with enhanced absorption and blood-brain barrier permeability. To further validate these findings, the most favorable docking conformations were analyzed using molecular dynamics (MD) simulations with GROMACS software, predicting the stability of the formed complexes. These simulations underscored the critical role of hydrogen bonds in stabilizing the compounds at the BuChE receptor binding site. The results hold great promise for the development of innovative benzodiazepine-1,2,3-triazole derivatives as effective BuChE inhibitors, potentially leading to therapeutic interventions for Alzheimer's disease.

Lassoed boosting and linear prediction in the equities market

We consider a two-stage estimation method for linear regression. First, it uses the lasso in Tibshirani to screen variables and, second, re-estimates the coefficients using the least-squares boosting method in Friedman on every set of selected variables. Based on the large-scale simulation experiment in Hastie, Tibshirani, and Tibshirani, lassoed boosting performs as well as the relaxed lasso in Meinshausen and, under certain scenarios, can yield a sparser model. Applied to predicting equity returns, lassoed boosting gives the smallest mean-squared prediction error compared to several other methods.

Analysis of the Determining Factors of Generation Z's Interest in Working in the Agricultural Sector in Bima Regency

This research is motivated by the phenomenon of the high interest of generation Z in Bima Regency in choosing to work in the agricultural sector. This research aims to analyze the influence of various factors, such as family economic conditions, environment, personality, motivation, and farming experience, on Generation Z's interest in choosing to work in the agricultural sector in the Bima Regency area. The type of research used is quantitative-descriptive. The population is generation Z, aged 15–24 years, who work as farmers or in the agricultural sector in six sub-districts in Bima Regency, namely Lambu, Monta, Wera, Langgudu, Belo, and Sape, with a total sample of 322 respondents. Primary data collection was carried out through interviews, observation, and documentation using questionnaire instruments and Google Forms. The data analysis was carried out using the multiple linear regression method. The results of the analysis show that partially family economic factors, environment, personality, motivation, and farming experience have a positive and significant effect on generation Z's interest in working in the agricultural sector in Bima Regency. Meanwhile, simultaneously, family economic variables, environment, personality, motivation, and farming experience have a positive and significant effect on generation Z's interest in working in the agricultural sector in Bima Regency.