Multiple Regression Approach Research Articles

Physical Distribution Practices and Marketing Performance of Selected Fast Moving Consumer Goods Companies in South-South Region of Nigeria

This study was designed to investigate the influence of physical distribution practices (transportation management, warehousing management, communication and information systems, and order processing) on the marketing performance of selected fast-moving consumer goods (FMCG) companies in the south-south region of Nigeria. The survey research design approach was adopted using a structured questionnaire to collect data from 247 selected FMCG sales representatives involved in the distribution of FMCG products in the south-south region of Nigeria. Stratified random sampling was employed. The data gathered was analysed using percentages and frequency tables. The formulated hypotheses were tested using a multiple regression approach. Findings from the study revealed a strong positive influence between the independent variables (transportation management, warehouse management, communication and information systems, and order processing management) and the dependent variable (marketing performance). The researchers suggest that FMCG companies should prioritise transportation management to facilitate better and more efficient movement of goods and people, thereby improving their marketing performance. Given the limitations of this study, the researchers presented practical implications and directions for future research.

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

Climate-Related Risks and Agricultural Yield Assessment in the Senegalese Groundnut Basin

Climate change and variability pose significant threats to agricultural production, particularly in regions heavily dependent on rainfed agriculture like Senegal. The problem addressed in this study revolves around the impact of climate-related risks on agricultural yields in the Senegalese Groundnut Basin as a key agricultural region. Daily rainfall, temperatures, and yield over 1991–2020 were used. The data were analyzed using multiple regression, trend analysis, and correlation approaches. The results indicate that the overall seasonal precipitation increases over time (98 mm in the north and 103 mm in the south). However, we found that the south Groundnut Basin has a much slower seasonal precipitation rate than the northern zone. Our results also show that the northern zone exhibits a more consistent and predictable growing season, with onset and offset, in contrast with the southern zone, which shows higher variability. The analysis further reveals that both the northern and southern zones are experiencing a warming trend, with the southern zone showing a more pronounced increase in maximum temperatures (+0.7 °C) than to the northern zone (+0.4 °C). Estimates from the regression analysis revealed that total seasonal precipitation and maximum temperature positively and significantly influence groundnut, millet, and maize yields in the northern and southern zones. All the other weather-related parameters have different influences depending on the zone. These findings highlight the heterogeneous nature of the study area and the significant role climatic factors play in crop yield variability in the Groundnut Basin. Understanding these influences is crucial for developing targeted agricultural strategies and climate adaptation measures to mitigate risks and enhance regional productivity. The study provides valuable insights for policymakers and farmers aiming to improve crop resilience and sustain agricultural outputs amidst changing climatic conditions.

Age at marriage and its determinants in Kerala and Bihar

BackgroundMarriage is a significant milestone in one’s life, and the age at which individuals choose to get married can have various implications. By examining the age at marriage and its determinants in different regions, such as Kerala and Bihar, we can gain insights into Socio-demographic factors that influence this decision.MethodsThe National Family Health Survey (2019–2021) data was used. A total of 41,392 ever married women from Kerala (8, 579) and Bihar (32,813) were analysed using bivariate and multiple binary logistic regression approaches to meet the study aim.ResultThe significant association was found between explanatory factors and age at first marriage in Karala and Bihar. In Bihar, educated women from the poorest families had a higher rate of early marriages. Conversely, in Kerala, uneducated women from non-majority religions and larger households (more than 6 members) were more likely to be married before18. After accounting major effects of background characteristics, the adjusted odds ratio showed that In Kerala Muslim religion women have significantly higher odds of getting married before 18 (OR: 3.09, CI 2.67–3.57) then the Hindu women whereas in Bihar Muslim religion women has the significantly lower chance of getting married before turning 18 (OR: 0.87, CI 0.81–0.93) compared to Hindu. In both the states Kerala and Bihar General social group women had the significantly lower odds of getting married below 18 (OR: 0.73, CI 0.57–0.93 and OR: 0.88, CI 0.81–0.96) with respect to SC/ST social group women’s.ConclusionWomen in Bihar usually marry in younger age due to socioeconomic and traditional reasons, whereas in Kerala reverse is true, because of a greater emphasis on education and a more progressive social outlook. These regional variations reflect different cultural norms and socioeconomic environments, shape different marriage age patterns between the two regions.

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.

Comparable glucagon-stimulated amino acid suppression in individuals with and without hepatic steatosis or steatohepatitis.

Hepatic amino acid (AA) metabolism and glucagon secretion are linked in a feedback cycle in which circulating AAs stimulate glucagon secretion, while glucagon stimulates hepatic AA catabolism. It has been proposed that metabolic dysfunction-associated steatotic liver disease (MASLD) leads to hepatic glucagon resistance, which may result in hyperaminoacidemia and hyperglucagonemia. We tested the glucagon effect on AA metabolism in subjects with obesity; 11 with steatohepatitis (MASH), 10 with steatosis (MAS), and seven subjects (CON) without steatosis. We performed a somatostatin clamp with infusions of insulin and low-dose followed by high-dose glucagon. We measured plasma levels of 17 AAs and assessed hepatic fat content (FF%) and body fat distribution (visceral and subcutaneous adipose tissue mass) by MRI. HighGlucagon suppressed plasma total AA equally in all groups; MASH 13% (SD 9%), MAS 14% (7%), CON 11% (5%), respectively. In univariate regression analyses visceral adipose tissue mass (β = 0.471, P = 0.011) and AA concentration at LowGlucagon (β = ─0.524, P = 0.004), but not FF% (β = ─0.243, P = 0.213), were significant predictors of AA reduction. Using a stepwise backward multiple regression approach revealed similar results. Total and specific AA levels (glutamic acid, tyrosine) were higher in both MASLD groups during the study and FF% was positively correlated to a number of individual AAs. Though finding elevated AA concentrations in subjects with MASLD, we conclude that in MASLD patients that do not have elevated glucagon at baseline, glucagon suppresses circulating AA levels equally in subjects with and without MASLD. NCT04042142.

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.

Response of alpine vegetation function to climate change in the Tibetan Plateau: A perspective from solar-induced chlorophyll fluorescence

Vegetation change in the Tibetan Plateau (TP) is a crucial indicator of climate change in alpine regions. Previous studies have reported an overall greening trend in the vegetation structure across the TP, especially in its northeastern part, in response to a warming climate. However, variations in the vegetation function and the possible drivers remain poorly understood. Considering the optimal temperature for plants in TP is usually higher than the current temperature, our hypothesis is the function and structure of alpine vegetation have changed synchronously over past few decades. To test this hypothesis, we analyzed satellite-observed solar-induced chlorophyll fluorescence (SIF) and leaf area index (LAI) in the Yellow River source (YRS) region in the northeastern TP to quantify the long-term trends in vegetation functional and structural states, respectively. The results suggest that from 1982 to 2018, SIF increased significantly in 77.71 % of the YRS area, resulting in a significant upward trend of 0.52 × 10−3 mW m−2 nm−1 sr−1 yr−1 (p < 0.001) for the regional-mean SIF. This represents a 16.1 % increase in SIF, which is close in magnitude to the increase in LAI over the same period. The synchronous changes between vegetation function and structure suggest that improved greenness corresponds to a similar level of change in carbon uptake across YRS. Additionally, we used a multiple regression approach to quantify the contribution of climatic factors to SIF changes in YRS. Our analyses show that the increases in SIF were primarily driven by rising temperatures. Spatially, temperature dominated SIF changes in most parts of YRS, except for certain dry parts in the northern and western YRS, where precipitation had a greater impact. Our results are crucial for a comprehensive understanding of climate regulations on vegetation structure and function in high-elevation regions.

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.

The Influence of Earning Per Share (EPS) and Price Earnings Ratio (PER) on Stock Prices in the Food and Beverage Sector on the Indonesian Stock Exchange (BEI) for the 2015-2019 Period

The objective of this study is to examine the impact of earnings per Share (EPS) and Price Earnings Ratio (PER) on the stock prices of companies in the food and beverage industry listed on the Indonesia Stock Exchange (BEI) from 2015 to 2019. EPS is utilized as a metric to gauge the profitability of each share, whereas PER characterizes the market's assessment of the earnings generated per share. The research employed a descriptive verification analysis method, utilizing a sample of seven prominent organizations within the researched sector.The data utilized is derived from the yearly financial reports of each company, specifically secondary data. The investigation employed multiple regression approaches to examine the hypothesis regarding the impact of EPS and PER on stock prices. The test utilizes the coefficient of determination to ascertain the extent to which the regression model can account for the fluctuations in stock prices.The research findings indicate a substantial correlation between EPS (Earnings Per Share), PER (Price Earnings Ratio), and firm share prices within the food and beverage industry on the IDX (Indonesia Stock Exchange). The R^2 value of 0.753 suggests that 75.3% of the changes in stock prices can be accounted for by the independent variables, EPS and PER. In this industry, share prices are primarily influenced by the profitability per share and market valuation.In addition, this study emphasizes that additional variables not investigated in this research can significantly impact stock prices, including macroeconomic conditions, market sentiment, government regulations, and other external factors. Stable macroeconomic conditions can lead to more favorable circumstances for increased stock values, whereas government policy or market sentiment alterations can profoundly influence a company's stock performance.The recommendations derived from this study are intended for investors and corporate executives operating in the food and beverage industry on the IDX. Investors are advised to consider the earnings per share (EPS) and price-to-earnings ratio (PER) components of their fundamental analysis to make better-informed investment choices. It is recommended that companies prioritize enhancing their financial performance and ensuring openness in their financial reporting to boost market trust and raise the value of their shares.Subsequent investigations might broaden the range by including supplementary variables like Dividend Yield or Return on Equity (ROE) while doing a more extensive analysis of qualitative elements that impact market perceptions of EPS and PER. This research contributes to understanding the factors that impact share prices in the Indonesian capital market, particularly within the food and beverage industry.