Multiple Linear Regression Approach Research Articles

Pengaruh Evaluasi Kinerja dan Motivasi Kerja Guru Terhadap Mutu Pendidikan di SMA Negeri 1 Bawolato

This study aims to analyze the impact of teacher performance evaluation and work motivation on the quality of education at SMA Negeri 1 Bawolato. The research employs a quantitative method with a multiple linear regression approach. The sample consists of 39 respondents, namely teachers at SMA Negeri 1 Bawolato. Data were collected through validated and reliable questionnaires and processed using SPSS version 26. The results indicate that both performance evaluation (X1) and teacher work motivation (X2) partially have a significant impact on the quality of education (Y). Partial regression tests yielded a t-value of 2.302 for the performance evaluation variable with a significance of 0.027, and a t-value of 2.959 for work motivation with a significance of 0.058. Additionally, both variables simultaneously showed a significant influence on education quality, with an F-value of 26.192, greater than the F table value (3.25), and a significance of 0.000. The determination coefficient (R Square) of 0.593 indicates that 59.3% of the variance in education quality is influenced by performance evaluation and work motivation, while the remaining 40.7% is influenced by other unexamined factors. This suggests that improving teacher performance evaluation and work motivation significantly contributes to enhancing the quality of education at SMA Negeri 1 Bawolato. The study concludes that teacher performance evaluation and work motivation are crucial factors in improving education quality. Therefore, schools are advised to continuously develop effective performance evaluation mechanisms and strategies to enhance teacher motivation to achieve better educational quality.

Pengaruh Beban Kerja dan Kompensasi Terhadap Kinerja Karyawan Grand Cemara Hotel Jakarta

This research was conducted at Grand Cemara Hotel Jakarta. The study aims to evaluate "The Effect of Workload and Compensation on Employee Performance of Grand Cemara Hotel Central Jakarta" using descriptive and verification quantitative analysis methods with multiple linear regression approaches. The independent variables in this study are Workload and Compensation, while the dependent variable is Employee Performance. The number of samples used was 52. This study aims to identify the impact of independent variables on the dependent variable. The results of the validity and reliability of the questionnaire show that all questionnaire statements are reliable and valid. Descriptively, Workload is rated "Fair", as well as Compensation and Employee Performance. The verification analysis results show that Workload and Compensation have a positive and significant effect on Employee Performance partially. Together, both have a positive and significant effect. The coefficient of determination or R2 reaches 98.70%, while the remaining 1.30% is influenced by other variables not included in the scope of this study.

Effect Of Motivation And Appreciation Of Employee Performance At The Department Of Transportation Makassar

This study aims to determine the effect of motivation and rewards on employee performance at the Makassar City Transportation Agency. This study uses a quantitative method with a multiple linear regression approach. Data were collected through questionnaires distributed to 89 employees. The results of the study indicate that motivation has a significant effect on employee performance with a T value (T-statistic) of 7.523 and a significance level of 0.000. Rewards also have a significant effect on employee performance with a T value (T-statistic) of 2.359 and a significance level of 0.021. The T value (T-statistic) in this context is a test statistic used to determine whether the coefficient of the independent variable (motivation and rewards) is significantly different from zero, which means that the variable has an effect on the dependent variable (employee performance). A higher T value indicates that the independent variable has a strong and significant effect. In addition, the combination of motivation and rewards explains 60.8% of the variation in employee performance, as indicated by the R Square value of 0.608. The conclusion of this study is that motivation and rewards play an important role in improving employee performance. Therefore, it is recommended to improve motivation programs and reward systems to achieve optimal performance.

Multimodal Temperature Readout Boosts the Performance of CuInS2/ZnS Quantum Dot Nanothermometers.

Fluorescent nanothermometers are positioned to revolutionize research into cell functions and provide strategies for early diagnostics. Fluorescent nanostructures hold particular promise to fulfill this potential if nontoxic, stable varieties allowing for precise temperature measurement with high thermal sensitivities can be fabricated. In this work, we investigate the performance of micelle-encapsulated CuInS2/ZnS core/shell colloidal quantum dots (QDs) as fluorescent nanothermometers. We demonstrate four temperature readout modes, which are based on variations in the photoluminescence intensity, energy, and lifetime and on a specific ratio of excitation efficiencies. We further leverage this multimodal readout to construct a fifth, multiparametric thermometer calibration based on the multiple linear regression (MLR) model. We show that the MLR approach boosts the thermometer sensitivity by up to 7-fold while reducing the readout error by about a factor of 3. As a result, our QDs offer the highest sensitivities among semiconducting QDs emitting in the first biological window. The obtained results indicate that CuInS2/ZnS QDs are excellent candidates for intracellular in vivo thermometry and provide guidelines for further optimization of their performance.

AI-Based Machine Learning and Multiple Linear Regression Approach to Simulate the Effect of Weather on the Crop Age at First Appearance of Potato Late Blight (Phytophthora infestans (Mont.) de Bary) Disease

AI-Based Machine Learning and Multiple Linear Regression Approach to Simulate the Effect of Weather on the Crop Age at First Appearance of Potato Late Blight (Phytophthora infestans (Mont.) de Bary) Disease

Application of Multiple Linear Regression Method in Steel Column Design under Combined Loading

The article explores using multiple linear regression (MLR) approach to predict the cross-section of beam-column steel members. These members are complex in design, as they must withstand bending and axial forces. The design process involves multiple safety checks according to design standards. The first step in the design procedure is to calculate and verify the axial capacity of the member. Following that, the flexural capacity is calculated. Finally, the combined load capacity is examined, often involving a trial-and-error process. If the initially selected section cannot withstand the applied forces, a new section must be chosen, and the calculations must be repeated. This study employs a multiple linear regression approach to investigate the relationship between member properties (such as length and cross-section) and load design parameters (axial and bending forces). The primary objective is to predict the beam-column steel member’s elastic section modulus (Sx). The regression equation derived from the study is as follows: Sx = 18.000L + 6.839Pr + 47.193Mr – 129.093, with an R-square value of 0.985. The histogram of the equation exhibits symmetry and a standard distribution, while the typical probability plot demonstrates a relatively linear trend. Two examples are presented to evaluate the efficiency of the regression equation. The first example confirms the efficiency of the equation by determining the cross-sectional size by employing the regression equation and using it to verify the safety according to EIT design standards. The second example is the application of the equation for estimating the size of other types of steel members. This study suggests that the regression equation can effectively predict the size of beam-column steel members, providing reliable results for practical design applications.

Drillability Evaluation and Sensitivity Analysis of the Fencheng Formation Strata in Maye Exploration Area

Abstract Predicting drillability ratings and selecting suitable drill bits are crucial for improving drilling efficiency. This investigation employed Principal Component Analysis (PCA) to normalize adjacent well logging parameters such as natural gamma, well diameter, natural potential, and others as influencing factors and established a mathematical model for the drillability ratings of hard rock formations. A sensitivity analysis of drilling parameters for the Fencheng Formation rocks revealed that sonic time difference, formation density, natural gamma, and resistivity were closely associated with drillability ratings. Empirical results showed that a multiple logarithmic linear regression approach accurately predicted drillability ratings for hard rock formations with over 95% precision compared to experimental values. The Fencheng Formation strata’s high hardness and abrasion resistance result in poor drillability (with ratings above 7.5), with rotational speed sensitivity found to be higher than drilling pressure sensitivity for expediting drilling in hard formations. For the “turbine + PDC bit” drill tool combination, it is recommended to use a drilling pressure of 40 - 80KN and a rotary table speed of 50 - 70r/min. High rotational speeds and low drilling pressures effectively reduce bit sticking effects, while using PDC bits with turbines reduces bit wear, improving the economic efficiency of drilling operations without sacrificing mechanical drilling speed.

New binary mixtures of fungicides against Macrophomina phaseolina: Machine learning-driven QSAR, read-across prediction, and molecular dynamics simulation

Quantitative Structure-Activity Relationship (QSAR) analysis greatly enhances the development and research of pesticides. This study employed Multiple Linear Regression (MLR), machine learning (ML), and read-across (RA) approaches to investigate the combined effects of binary mixtures of fungicides on Macrophomina phaseolina. Using the Fixed Ratio Ray Design (FRRD) method, 75 binary mixtures of six frequently used fungicides were generated, with many exhibiting additive interactions as indicated by the Concentration Addition (CA) and Independent Action (IA) models. The QSAR analysis revealed that Support Vector Regression (SVR) and Gaussian Process Regression (GPR) models were the most effective, outperforming the Least Squares Kernel (LSK), MLR, and RA methods. SVR achieved an outstanding R2 of 0.95 and Q2LMO of 0.81, whereas GPR demonstrated values of 0.93 and 0.81 for the same metrics. Internal and external validation confirmed the reliability and generalizability of these models, suggesting they could be applied to a wider array of data. Moreover, Molecular Dynamics (MD) simulations showed that the effects of the fungicides are linked to physiological mechanisms rather than intermolecular interactions within their formulations. This study establishes a robust framework for creating potent fungicide combinations that improve disease management efficacy while promoting environmental sustainability and reducing the chemical load to mitigate negative impacts.

Multivariate approaches for assessing dissolution behaviour of spironolactone. Dissolution-driven determination of polymorphic purity limits

Spironolactone (SPN) is an aldosterone antagonist effective for preventing and controlling cardiovascular diseases. Form I and Form II exhibit different behaviour in their dissolution properties, which can compromise the quality of the pharmaceutical formulation. Hence, in the present work, we propose the combination of two multivariate approaches to estimate the polymorphic purity of SPN in commercial tablets and predict the dissolution behaviour of these formulations considering their polymorphic composition. Multiple linear regression (MLR) approach was applied to model the dissolution behaviour of formulations depending on their polymorphic composition, showing excellent agreement among actual vs. predicted dissolution curves. This model was applied to establish a design space were the risks of dissolution failure due to the polymorphic purity was avoided. Next, to evaluate the purity of Form II of SPN in formulated products, two quantitative partial least squares (PLS) models were developed based on near (NIR) and mid (MIR) infrared spectroscopies. Training (n = 14) and validation (n = 8) sets were prepared by mixing both polymorphs and the excipient matrix to simulate commercial tablets. Methodologies showed recoveries non-statistical different from 100 %, but MIR-PLS model showed a higher dispersion (100.1 ± 1.82 % and 99.5 ± 6.94 % for NIR and MIR, respectively). Similar values of Form II content (1.02 ± 0.01 w/w with NIR and 1.03 ± 0.03 w/w with MIR) were obtained during the analysis of commercial and spiked SPN tablets. Finally, NIR-PLS was coupled to MLR model for the prediction of dissolution in entire tablets based on polymorphic composition to act as Quality by Design (QbD) tool. The full coincidence among actual and NIR-MLR predicted curves validated the final purpose of this approach.

PENGARUH SOLVABILITAS, LIKUIDITAS, DAN HARGA KOMODITAS TERHADAP HARGA SAHAM MELALUI PROFITABILITAS SEBAGAI VARIABEL MEDIASI

This study aims to analyze the relationship between financial performance and commodity prices on the share prices of coal mining companies listed on the Indonesian Stock Exchange during the period 2015- 2022. The research method used is quantitative-descriptive with a multiple linear regression approach. This research focuses on Debt to Equity Ratio (DER), Current Ratio (CR), Return on Assets (ROA), and coal prices. Through path analysis, the research indicate that ROA significantly mediates the impact of DER, CR, and Coal Price on the Stock Prices of coal mining companies. These finding contribute to understanding the factors influencing the stock prices of coal mining companies and have implication for investors, providing information for consideration in making investment decisions

The role of wheat flour minor components in predicting water absorption

Water absorption in wheat flour is a crucial parameter for optimizing bread-making processes. The determinants of wheat flour water absorption were investigated through the analysis of 28 compositional and technological properties of 150 wheats grown in France. A multiple linear regression approach was used to predict the water absorption, selecting the best model through successive examination of Bayesian Information Criterion, Variance Inflation Factor and minimizing the total number of variables.A model with protein content, soluble starch, damaged starch and specific viscosity from water extractable arabinoxylans was identified as the best trade-off between the number of variables and the predictive performances among all possible models. Soluble Starch, varying between 1.11 and 6.21 g/100 g flour a new criterion measured alongside water-extractable arabinoxylans content, varying between 0.26 and 0.86 g/100 g flour, shows significant potential to predict water absorption compared to damaged starch.

Remote Prediction of Soybean Yield Using UAV-Based Hyperspectral Imaging and Machine Learning Models

Early soybean yield estimation has become a fundamental tool for market policy and food security. Considering a heterogeneous crop, this study investigates the spatial and spectral variability in soybean canopy reflectance to achieve grain yield estimation. Besides allowing crop mapping, remote sensing data also provide spectral evidence that can be used as a priori knowledge to guide sample collection for prediction models. In this context, this study proposes a sampling design method that distributes sample plots based on the spatial and spectral variability in vegetation spectral indices observed in the field. Random forest (RF) and multiple linear regression (MLR) approaches were applied to a set of spectral bands and six vegetation indices to assess their contributions to the soybean yield estimates. Experiments were conducted with a hyperspectral sensor of 25 contiguous spectral bands, ranging from 500 to 900 nm, carried by an unmanned aerial vehicle (UAV) to collect images during the R5 soybean growth stage. The tests showed that spectral indices specially designed from some bands could be adopted instead of using multiple bands with MLR. However, the best result was obtained with RF using spectral bands and the height attribute extracted from the photogrammetric height model. In this case, Pearson’s correlation coefficient was 0.91. The difference between the grain yield productivity estimated with the RF model and the weight collected at harvest was 1.5%, indicating high accuracy for yield prediction.

The Effect of Financial Performance to Share Returns on Companies IDX 30 Indexed During The Covid Pandemic at The Indonesian Stock Exchange

This research is a replication of several previous on going studies due to previous research results that tried to test the influence of financial performance on stock returns on IDX 30 during the covid 19 pandemic. The study is intended to obtain an explanation of empirical facts on the influence of factors described through financial stability, external pressure, and financial targets together and partial influences financial statement fraud on banking companies during the period of COVID 19 on the Indonesian Stock Exchange. The population in this study is all IDX 30 indexed companies on the Indonesian Stock Exchange, namely as many as 30 companies. With sample count of 22 companies. The type of data used in this study was secondary data with the amount of observation from 2019-2020 during the COVID 19 pandemic period so that it became a sample of observation of 22 X 2 = 44 samples, and the scale used in this study was the scale of ratio and analysis of data used was with the multiple linear regression approach at a significant rate of 5%. Research will be very useful input for investors as an important guideline for looking at the condition of the company and the fundamental aspects to assess the expectations of the return (return shares) that it will acquire and as a decision maker to buynot buy shares of the company. The results of this research are also expected to be used by IDX 30 companies to find out their condition and finances so as to improve the financial performance of the next period that will eventually affect the company’s stock price. In addition, this research can also be used as the basis for advanced research to review financial performance viewed from different perspectives of financial instruments.

Toxicity reference values (TRVs) for force health protection: Gap identification and TRV prediction

The mission of the Force Health Protection (FHP) program of the U.S. Air Force (USAF), sustaining the readiness of warfighters, relies on determinations of acceptable levels of exposure to a wide array of substances that USAF personnel may encounter. In many cases, exposure details are limited or authoritative toxicity reference values (TRVs) are unavailable. To address some of the TRV gaps, we are integrating several approaches to generate health protective exposure guidelines. Descriptions are provided for identification of chemicals of interest for USAF FHP (467 to date), synthesis of multiple TRVs to derive Operational Exposure Limits (OpELs), and strategies for identifying and developing candidate values for provisional OpELs when authoritative TRVs are lacking. Rodent bioassay-derived long-term Derived No Effect Levels (DNELs) for workers were available only for a minority of the substances with occupational TRV gaps (19 of 84). Additional occupational TRV estimation approaches were found to be straightforward to implement: Tier 1 Occupational Exposure Bands, cheminformatics approaches (multiple linear regression and novel nearest-neighbor approaches), and empirical adjustment of short term TRVs. Risk assessors working in similar contexts may benefit from application of the resources referenced and developed in this work.

An Investigation on Causes of the Detected Surface Solar Radiation Brightening in Europe Using Satellite Data

AbstractSurface solar radiation is fundamental for terrestrial life. It provides warmth to make our planet habitable, drives atmospheric circulation, the hydrological cycle and photosynthesis. Europe has experienced an increase in surface solar radiation, termed “brightening,” since the 1980s. This study investigates the causative factors behind this brightening. A novel algorithm from the EUMETSAT satellite application facility on climate monitoring (CM SAF) provides the unique opportunity to simulate surface solar radiation under various atmospheric conditions for clouds (clear‐sky or all‐sky), aerosol optical depth (time‐varying or climatological averages) and water vapor content (with or without its direct influence on surface solar radiation). Through a multiple linear regression approach, the study attributes brightening trends to changes in these atmospheric parameters. Analyzing 61 locations distributed across Europe from 1983 to 2020, aerosols emerge as key driver during 1983–2002, with Southern Europe and high elevations showing subdued effects (0%/decade–1%/decade) versus more pronounced impacts in Northern and Eastern Europe (2%/decade–6%/decade). Cloud effects exhibit spatial variability, inducing a negative effect on surface solar radiation (−3%/decade–−2%/decade) at most investigated locations in the same period. In the period 2001–2020, aerosol effects are much smaller, while cloud effects dominate the observed brightening (2%/decade–5%/decade). This study therefore finds a substantial decrease in the cloud radiative effect over Europe in the first two decades of the 21st century. Water vapor exerts negligible influence in both sub‐periods.

A Novel Multiple Linear Regression Approach for Predicting the Unconfined Compressive Strength of Soil

A Novel Multiple Linear Regression Approach for Predicting the Unconfined Compressive Strength of Soil

THE INFLUENCE OF ACCOUNTING DIGITALISATION TRANSFORMATION AND SUSTAINABLE MANAGEMENT ON ESG PERFORMANCE TO ACHIEVE ENVIRONMENTAL SUSTAINABILITY

This research aims to examine and analyse the influence of accounting digitalisation transformation and sustainable management on ESG (Environmental, Social, and Governance) performance as a supporting capacity for hotel environmental sustainability programs. This research was conducted on a population of 1,010 four and five-star hotels in Indonesia based on data from the Central Statistics Agency. The study employed a quantitative research method with a survey design, utilising both online and offline questionnaires. Data analysis was conducted using a multiple linear regression approach. The empirical findings indicate that the digital transformation of accounting and sustainable management has a notable positive impact on ESG performance, thereby indicating its potential to bolster environmental sustainability within the hotel industry. This suggests that leveraging the digital transformation of sustainable accounting and management as a proxy for legitimacy theory can genuinely enhance ESG performance and contribute to environmental sustainability practices in the hotel sector. The research contribution is aimed at strengthening legitimacy theory in research in the field of sustainability accounting and can practically be used as a management reference in managing hotels towards sustainable hotels oriented towards environmental sustainability and receiving the title of green hotel.

Pengaruh Rasio Likuiditas dan Rasio Aktivitas Terhadap Profitabilitas Pada PT. Surya Toto Indonesia, Tbk

This study aims to analyze the influence of liquidity ratio and activity ratio on profitability in PT. Surya Toto Indonesia, Tbk. Liquidity ratio is measured using Current Ratio (CR) and Quick Ratio (QR), while activity ratio is measured using Total Asset Turnover (TATO) and Inventory Turnover (ITO). The research method used is a quantitative method with a multiple linear regression approach. The data used in this study is secondary data obtained from the financial statements of PT. Surya Toto Indonesia, Tbk during the 2018-2022 period. The results show that the liquidity ratio and activity ratio have a significant influence on profitability as measured by Return on Assets (ROA). Current Ratio (CR) and Total Asset Turnover (TATO) have a positive and significant effect on profitability, while Quick Ratio (QR) and Inventory Turnover (ITO) have a negative but insignificant effect. These findings indicate that companies should pay attention to liquidity management and their operational activities to increase profitability.

Double Literacy, Double Protection?: Minimising Gen Z's Risky Digital Credit Behaviour In Surabaya Through Financial Literacy And Digital Financial Literacy

This study aims to investigate the credit risk issues that Generation Z is facing In Surabaya, particularly digital credit and loans. This study utilises a closed-ended questionnaire to explore Generation Z’s knowledge and awareness of subjective financial literacy, digital financial literacy, and risky credit behaviour, so that the results can reveal the influence of subjective financial literacy and digital financial literacy on risky credit behaviour controlled by gender and financial condition of Generation Z in Surabaya. To the best of the researcher’s knowledge, there are no studies that specifically highlight the influence of subjective financial literacy and digital financial literacy on risky credit behaviour. Based on the results of the multiple linear regression approach, researchers found that subjective financial literacy and digital financial literacy have a significant positive effect on risky credit behaviour, which means that the higher the subjective financial literacy and digital financial literacy, the riskier credit behaviour is minimized, gender has no significant effect on risky credit behaviour, financial condition has a significant negative effect on risky credit behaviour.

Phenolic Acid-β-Cyclodextrin Complexation Study to Mask Bitterness in Wheat Bran: A Machine Learning-Based QSAR Study.

The need to solvate and encapsulate hydro-sensitive molecules drives noticeable trends in the applications of cyclodextrins in the pharmaceutical industry, in foods, polymers, materials, and in agricultural science. Among them, β-cyclodextrin is one of the most used for the entrapment of phenolic acid compounds to mask the bitterness of wheat bran. In this regard, there is still a need for good data and especially for a robust predictive model that assesses the bitterness masking capabilities of β-cyclodextrin for various phenolic compounds. This study uses a dataset of 20 phenolic acids docked into the β-cyclodextrin cavity to generate three different binding constants. The data from the docking study were combined with topological, topographical, and quantum-chemical features from the ligands in a machine learning-based structure-activity relationship study. Three different models for each binding constant were computed using a combination of the genetic algorithm (GA) and multiple linear regression (MLR) approaches. The developed ML/QSAR models showed a very good performance, with high predictive ability and correlation coefficients of 0.969 and 0.984 for the training and test sets, respectively. The models revealed several factors responsible for binding with cyclodextrin, showing positive contributions toward the binding affinity values, including such features as the presence of six-membered rings in the molecule, branching, electronegativity values, and polar surface area.