Coefficients Of Variables Research Articles

THE INFLUENCE OF CONSUMER BEHAVIOR ON THE PURCHASE DECISION OF HYDROPONIC VEGETABLES IN PUCCAN HYDROPONIC, BLITAR CITY

The research aims to determine the effect of consumer behavior on purchasing decisions for hydroponic vegetables at Puccan Hydroponics. The results of the analysis show that there are 4 factors considered, namely Product, Price, Place, and Promotion on Purchase Decision using multiple linear regression analysis. The population is hydroponic vegetable consumers at Puccan Hydroponics with a total sample of 30 respondents. The sampling technique in this study was the accidental sampling method. Based on the research results, from the results of the F test it can be seen that the variables Product, Price, Place, and Promotion together have a significant effect on the Purchase Decision variable. From the results of the T test it can be seen that the price variable partially influences purchasing decisions, while the product, place, and Promotion does not partially influence the Purchase Decision. From the test results of the coefficient of determination, Product, Price, Distribution, and Promotion variables contribute 90% to the Purchasing Decision variable. While 10% is influenced by other factors.

Empirical analysis of poverty eradication and its determinants in Rwanda: lessons from China

This paper analyzed empirically the lessons Rwanda learned on poverty reduction strategies used by China. The simulations were made using the Fixed Effect Model where the coefficients of explanatory variables were constant or the same for both countries to reflect how poverty reduction strategies used by both countries result in the same outcomes and relatively the same output. Outcomes from simulations revealed that Adjusted Net Saving, government spending, citizen‟s empowerment, education for all and Foreign Direct Investments strategies used and drawn from Chinese economy by Rwanda are statistically significant; hence reduced poverty in both economies. In the light of the findings, the study recommended to policy makers a strong review of their empowerment strategies since it was the only explanatory variable which was not statistically significant in the model. It also recommended that women empowerment in political, social and economic aspects must be accompanied by youths‟ empowerment in those domains as the main driving forces of both economies.

Labour Demand Analysis in the ICT Sector: EU Countries and Türkiye

This paper is dedicated to specific research on the information and communications technologies (ICT) sector, where the variables determine labour demand in the European Union (EU) and Türkiye. The research aims to clarify the relationships between employment-generating firm growth that represents labour demand and the independent variables identified by the authors. For this purpose, our method analyses the factors affecting labour demand econometrically. In the study, a panel data set of 22 countries, including 21 EU countries and Türkiye, is used for the period of 2014-2019. The results show that there is a positive and significant relationship between employment in the ICT sector and real gross domestic product (GDP) per capita and frequency of internet use. In addition, the results show that the relationship between employment and wage level in the ICT sector is negative and significant. According to the results obtained from the Fixed Effects (FE) model, the elasticity coefficients of the independent variables in the model present for wages (1.53), GDP per capita (3.27) and frequency of internet use (1.60). Finally, we have discussed the results estimated by the Shadow Variable Least Squares (LSDV) method to measure the impact of each country on the overall variability in employment level. As a result of the study, when labour demand is associated with firm employment increase, the countries in the target geography where a significant and positive relationship was found are Belgium, Bulgaria, Croatia, Czechia, Estonia, Finland, Germany, Hungary, Italy, Latvia, Netherlands, Poland, Portugal, Romania, Slovakia, Spain, Sweden, and Türkiye.

Multi-Scale Geographically Weighted Elasticity Regression Model to Explore the Elastic Effects of the Built Environment on Ride-Hailing Ridership

Understanding the relationship between the built environment and the ride-hailing ridership is crucial to the prediction of the demand for ride-hailing and the formulation of the strategy for upgrading the built environment. However, the existing studies on ride-hailing ignore the scale effect and zone effect of the modifiable area unit problem (MAUP), and show a lack of consideration for the elastic relationship with spatial heterogeneity between built environment variables and ride-hailing ridership. Taking Chengdu as an example, this paper selects 12 independent variables based on the “5Ds” (density, diversity, design, destination accessibility and distance to transit) of the built environment, the dependent variables are the density of ride-hailing pick-ups in the morning and evening peak hours, and 11 spatial units are proposed according to different scales and zoning methods for the aggregation of built environment variables and ride-hailing pick-ups. With the goal of global optimal goodness-of-fit, we determined the optimal spatial unit by using the log-linear Ordinary Least-Squares (OLS) model. A multi-scale geographically weighted elastic regression (MGWER) model is formulated to explore the relative effect of the built environment on the ride-hailing ridership and spatial heterogeneity. The average value of positive elastic local regression coefficient of different variables is used to measure the relative positive impact of built environment factors, and the absolute value of the average value of negative elastic local regression coefficient is used to measure the relative negative impact of built environment factors. The results show that: (1) The MGWER model under the community unit division has the best global goodness-of-fit. (2) Different built environment variables have different elastic impacts on the demand for ride-hailing. For the morning peak hours and evening peak hours, the top three built environment factors with positive impacts are ranked as follows: commercial POI density > average house price > population density, and distance to CBD has the highest negative impacts on pick-up ridership. (3) The different local elasticity coefficients of the built environment factors at different stations are discussed, which indicate the spatial heterogeneity of the ride-hailing ridership. The optimal community zoning method can provide a basis for the zoning and scheduling management of ride-hailing. The results of the built environment variables with greater impact are conducive to the formulation of targeted urban renewal strategies in the process of adjusting the ridership of ride-hailing.

Decentralized event-triggered sliding mode scheme for frequency regulation of multi-area power systems under deception attacks

This paper investigates the frequency regulation problem of networked multi-area power systems subject to random deception attacks. Due to the increasing integration of wireless communication devices in power systems, the frequency stability highly relies on the efficiency and security of information transmission. To alleviate the communication burden, a decentralized dynamic event-triggered (ET) scheme is proposed, in which each area transmits the measurement via its own ET rule. Moreover, the coefficients of dynamic variables are designed based on the state variations and the attack probability for different areas. Then, a decentralized ET sliding mode controller is proposed to restore the frequency. Both the mean square exponentially ultimate boundedness of the power systems and reachability of the specified sliding surface are analyzed, and the corresponding conditions are given. Compared with the widely adopted centralized schemes, the proposed decentralized ET control scheme is easier to implement and can ensure more reasonable transmission considering attack probability for different areas; meanwhile, the deception attacks on different areas are considered to occur independently. Finally, simulation results of a three-area power system are provided.

Development of a traffic accident simulation system for main roads in Loei Province, Thailand: Application of a geographic information system and multiple logistic regression with clustering

Traffic accidents are a major and crucial problem worldwide. The development of a traffic accident simulation system applied by using a geographic information system and multiple logistic regression with clustering can provide drivers with safe routes as well as guidelines for assessing the risk points of accidents in each subdistrict. This research is based on case-control study design. The data were collected by using two types of questionnaires: a questionnaire for 35 community leaders and a questionnaire for 580 community residents based on the distance at which main routes pass through the subdistrict area. The data were analysed through multiple logistic regression with clustering, and the standardized coefficient of the selected variables was then added to the equation as a weight in the traffic accident simulation system. The results of the study indicated that 11 variables affected traffic accidents. These factors were evaluated in order to predict traffic accidents (Pseudo R square=0.5906). Standardized coefficient of variables was applied in a geographic information system to simulate traffic accidents on roads. This study was distinctive for its analysis, which examined the clusters of variables that were the subdistrict-level data, including surroundings and road conditions at the riskiest location in each subdistrict. The data were analysed based on their quality as subdistrict data clusters. The analysis results were then applied as the weight of variables used in the GIS to obtain the values appropriate to the data clusters’ quality for the GIS to properly simulate traffic accidents in each area.

Estimating the overall fraction of phenotypic variance attributed to high-dimensional predictors measured with error.

In prospective genomic studies (e.g., DNA methylation, metagenomics, and transcriptomics), it is crucial to estimate the overall fraction of phenotypic variance (OFPV) attributed to the high-dimensional genomic variables, a concept similar to heritability analyses in genome-wide association studies (GWAS). Unlike genetic variants in GWAS, these genomic variables are typically measured with error due to technical limitation and temporal instability. While the existing methods developed for GWAS can be used, ignoring measurement error may severely underestimate OFPV and mislead the design of future studies. Assuming that measurement error variances are distributed similarly between causal and noncausal variables, we show that the asymptotic attenuation factor equals to the average intraclass correlation coefficients of all genomic variables, which can be estimated based on a pilot study with repeated measurements. We illustrate the method by estimating the contribution of microbiome taxa to body mass index and multiple allergy traits in the American Gut Project. Finally, we show that measurement error does not cause meaningful bias when estimating the correlation of effect sizes for two traits.

Rapid, Digital Dietary Assessment in Association with Cardiometabolic Biomarkers.

To examine the associations between dietary intake as assessed by a rapid, image-based digital tool and biomarkers of cardiometabolic health. Retrospective analysis of adults with blood biomarkers performed by Boston Heart Diagnostics (BHD) between December 2020 and March 2022. Outpatient centers serviced by BHD. 546 adults, excluding those taking relevant medications and/or supplements known to affect blood test results. Laboratory assays of blood specimens were performed by Boston Heart Diagnostics. Nutrient intake and diet quality data were obtained using Diet Quality Photo Navigation (DQPN®; US Patent #11,328,810 B2) technique via Diet ID™ tool. Pearson correlation coefficients (for continuous variables) and Spearman coefficients (for ordinal variables) were used to evaluate associations between nutrient intake data and laboratory data for the full study sample. Two-sided P-values < .05 were considered statistically significant. Both continuous and ordinal measures of diet quality correlated significantly with HDL-C and triglycerides (n = 485; P < .0 01); with hs-CRP (n = 441; P < .001); with HgbA1c (n = 345; P < .01); with fasting insulin (n = 372; P < .001); and with HOMA-IR (n = 319; P < .001). Findings affirm that rapid, digital diet quality and composition assessment by pattern recognition rather than recall tracks significantly with key biomarkers of cardiometabolic health.

Travel Matrix Decomposition for Understanding Spatial Long-Distance Travel Structure

Mobile phone location data enable us to obtain accurate and temporally detailed long-distance travel distribution. However, the traditional long-distance travel distribution model cannot normally handle this detailed temporal information. This study proposes an approach for handling temporally detailed information of long-distance travel distribution. Considering this approach, the origin-destination matrix decomposes into two variables (indicators): destination amenity and travel cost. They can be interpreted as composite indicators of several variables that are treated in the travel-destination choice multinomial logit model. Because they are calculated only from the origin destination, we can discuss their detailed temporal variations. In this study, time changes in destination amenities and travel costs of interprefectural travel in Japan are calculated to confirm the value of this approach. These indicators have succeeded in describing the pattern of domestic long-distance travel in Japan. These quantified indicators have facilitated the understanding of the national land structure. They are useful as outcome measures for policy-making. Moreover, these indicators explain the temporal applicability of the destination choice model. Specifically, the results of destination amenities have a large seasonal variation. This indicates that the parameters of the destination amenity model (i.e., the coefficients of the destination variables) are not seasonally stable. Therefore, this must be considered when dealing with destination choice for long-distance travel.

A DSC Test for the Early Detection of Neoplastic Gastric Lesions in a Medium-Risk Gastric Cancer Area.

In this study, we aimed to assess the accuracy of the proposed novel, noninvasive serum DSC test in predicting the risk of gastric cancer before the use of upper endoscopy. To validate the DSC test, we enrolled two series of individuals living in Veneto and Friuli-Venezia Giulia, Italy (n = 53 and n = 113, respectively), who were referred for an endoscopy. The classification used for the DSC test to predict gastric cancer risk combines the coefficient of the patient's age and sex and serum pepsinogen I and II, gastrin 17, and anti-Helicobacter pylori immunoglobulin G concentrations in two equations: Y1 and Y2. The coefficient of variables and the Y1 and Y2 cutoff points (>0.385 and >0.294, respectively) were extrapolated using regression analysis and an ROC curve analysis of two retrospective datasets (300 cases for the Y1 equation and 200 cases for the Y2 equation). The first dataset included individuals with autoimmune atrophic gastritis and first-degree relatives with gastric cancer; the second dataset included blood donors. Demographic data were collected; serum pepsinogen, gastrin G17, and anti-Helicobacter pylori IgG concentrations were assayed using an automatic Maglumi system. Gastroscopies were performed by gastroenterologists using an Olympus video endoscope with detailed photographic documentation during examinations. Biopsies were taken at five standardized mucosa sites and were assessed by a pathologist for diagnosis. The accuracy of the DSC test in predicting neoplastic gastric lesions was estimated to be 74.657% (65%CI; 67.333% to 81.079%). The DSC test was found to be a useful, noninvasive, and simple approach to predicting gastric cancer risk in a population with a medium risk of developing gastric cancer.

Corporate governance and banking performance: A comparative study between Islamic and conventional banking sector in the context of Bangladesh

Corporate governance (CG) is a set of principles that should be included into every aspect of the firm in order to ensure accountability and responsibility. The purpose of this research is to look at the state of corporate governance (CG) in banking sector of conventional banks and Islamic banks. Because the rules, regulations, and operating processes of conventional and Islamic banks differ significantly, the corporate governance (CG) practices of these two banking sectors are likewise distinct. In this work, the authors attempt to give a clear comparative assessment of corporate governance (CG) practices in these two banking sectors. This research looked at four dimensions of corporate governance: board size, board diversity, board diversity, and CEO duality. Return on Equity (ROE) and Return on Assets (ROA) have been used to evaluate banking performance. Regression analysis is used to evaluate the banking performance of the said banking sectors. It is found that BS, IND and BD have positive impact on ROE in Islamic banks sample. On the other hand, in terms of the coefficient of independent variables from sample banks of conventional sector are found to be negative influence on ROE in where IND and CEO duality shows significant result. Both conventional and Islamic banks are under pressure to enhance their corporate governance (CG) practices, since both banking sectors are seeing considerable improvements. However, when compared to conventional banks, Islamic banks lag behind in terms of corporate governance (CG). Companies must comprehend the advantages of implementing strong governance techniques and accompanying activities that aid in enhancing financial performance.

Natural History of Patients with Prostate MRI Likert 1-3 and Development of RosCaP: a Multivariate Risk Score for Clinically Significant Cancer.

Clinically significant prostate cancer (csCaP) with Gleason ≥3+4 is found in 10% negative prebiopsy multiparametric (mp) MRI cases and varies widely for equivocal mpMRI cases. The objective of this study was to investigate long-term outcomes of patients with negative and equivocal mpMRIs and to develop a predictive score for csCaP risk stratification in this group. Patients who underwent an upfront mpMRI between May 2015 and March 2018 with an MRI score Likert 1 to 3 were included in the study. Patients had either a CaP diagnosis at MRI-targeted biopsy or were not diagnosed and attended follow-up in the community. Outcomes were analysed through the Kaplan-Meier estimator and Cox Model. Regression coefficients of significant variables were used to develop a Risk of significant Cancer of the Prostate score (RosCaP). At first assessment 281/469 patients had mpMRI only and 188/469 mpMRI and biopsy, 26 csCaP were found at biopsy, including 10/26 in Likert 3 patients. 12/371 patients discharged without CaP after first assessment were diagnosed with csCaP during a median of 34.2 months' follow-up, 11/12 diagnosis occurred in patients omitting initial biopsy. csCaP diagnosis-free survival was 95.7% in the MRI group and 99.1% in the biopsy group. From these outcomes, a continuous RosCaP score was developed: RosCaP=0.083 x Age - 0.202 x (1/PSA Density)+0.786 (if Likert 3), and 4 risk classes were proposed. Limitations include retrospective design and absence of external validation. Age, PSA Density and MRI Likert score were significantly associated to the risk of csCaP and utilised to devise the novel RosCap predictive score focused to support risk assessment in patients with negative or equivocal mpMRI results.

Substantial decline in energy storage and stomach fullness in Antarctic minke whales (Balaenoptera bonaerensis) during the 1990s

A substantial decline in energy storage in the Antarctic minke whale during the 18-year Japanese JARPA research programme (austral summers 1987/88–2004/05) was reported in 2008 (Konishi et al., 2008). The statistical method used in the study was simple multiple linear regression. The results have since been thoroughly discussed by the Scientific Committee of the International Whaling Commission because of the potential importance of the findings. Some had suggested that the sampling heterogeneity in the JARPA data was so substantial that generalised linear models (GLMs) with interaction terms and random-effects terms should be explored. For the present article, five response variables related to energy storage and the variable ‘stomach content weight’ are systematically analysed using GLMs. For all five energy storage variables, the results show declines in the interval 3% to 9% over the JARPA period, all significantly different from zero at the 5% level, but no later decline. The weight of sieved stomach contents declined by 25% over the same period. The coefficients of the decline and the coefficients for most other independent variables were similar to values obtained by simple linear regression, but in some cases the standard errors were larger. The results indicate that important changes took place in the Antarctic ecosystem during the 1990s. It is hypothesised that the most important cause of the changes was the simultaneous increase in numbers of other krill feeders, especially humpback whales.

Determination of Direct and Indirect Effects on Milk Yield of Anatolian Buffaloes Using Path Analysis

This study aimed to determine the direct and indirect effects of the independent variables presented by the lactation lenght (LL), age of calving (CAGE), and daily milk yield (DMY) on the dependent variable of lactation milk yield (LMY) in Anatolian buffaloes. In this study, a total of 3761 LMY records of the 834 Anatolian buffaloes calving between 2012 and 2017 in Tokat province and around were used as the research material. In the study, the simple correlation coefficients between the dependent variable of LMY and independent variables were determined positive and significant (P&lt;0.001). The research specified the direct determination coefficients of the independent variables of the LL, CAGE, and DMY on the dependent variable of LMY as 0.378, 0.004, and 0.350, respectively. The path coefficients related to LL, CAGE, and DMY were determined as 0.615, 0.021, and 0.592, respectively. Since the DMY and LL examined in the study had a high direct impact on the lactation milk yield, therefore it could be suggested that using these data as criteria in breeding programs conducted in the herds evaluated in the study will increase the success in the selections applied.

The Utility of Oncology Information Systems for Prognostic Modelling in Head and Neck Cancer

Cancer centres rely on electronic information in oncology information systems (OIS) to guide patient care. We investigated the completeness and accuracy of routinely collected head and neck cancer (HNC) data sourced from an OIS for suitability in prognostic modelling and other research. Three hundred and fifty-three adults diagnosed from 2000 to 2017 with head and neck squamous cell carcinoma, treated with radiotherapy, were eligible. Thirteen clinically relevant variables in HNC prognosis were extracted from a single-centre OIS and compared to that compiled separately in a research dataset. These two datasets were compared for agreement using Cohen’s kappa coefficient for categorical variables, and intraclass correlation coefficients for continuous variables. Research data was 96% complete compared to 84% for OIS data. Agreement was perfect for gender (κ = 1.000), high for age (κ = 0.993), site (κ = 0.992), T (κ = 0.851) and N (κ = 0.812) stage, radiotherapy dose (κ = 0.889), fractions (κ = 0.856), and duration (κ = 0.818), and chemotherapy treatment (κ = 0.871), substantial for overall stage (κ = 0.791) and vital status (κ = 0.689), moderate for grade (κ = 0.547), and poor for performance status (κ = 0.110). Thirty-one other variables were poorly captured and could not be statistically compared. Documentation of clinical information within the OIS for HNC patients is routine practice; however, OIS data was less correct and complete than data collected for research purposes. Substandard collection of routine data may hinder advancements in patient care. Improved data entry, integration with clinical activities and workflows, system usability, data dictionaries, and training are necessary for OIS data to generate robust research. Data mining from clinical documents may supplement structured data collection.

The research on Fama-French 5-factor model in the medicine industry during COVID-19

During COVID-19, the diease has changed various aspects on the stock market so dramatically that the stock prices in different industry fields. While Fama-French 5-factor model is one of the most effective stock prices assessment models, the worldwide change during these several years could have the unexpected influence on this professional method. In this research, attempting to find the relationship between medicine policy and the stock price, in order to explain the huge change on the economics progress with this model, the linear regression is adopted to cope with this issue. Results indicate that most of the regression coefficients of the explanatory variables under the five factors have the same significance, with only the difference in the size of the coefficient values. Besides, the larger the value of the policy, the higher the risk premiums of pharmaceutical companies will be, additionally, small minus big, high minus low, robust minus weak, and conservative minus aggressive average returns will decrease, and vice versa. In summary, this paper investigates the effectiveness of Fama-French 5-factor model with the new variable, the medicine policy, based on linear regression and other mathematics methods. In the future, it is of great importance to investigate stock prices under such situation and take other factors into seriously account while doing research. Overall, these results shed light on guiding future policy making and different perspective on medicine industry development.

Application of LASSO and its extended method in variable selection of regression analysis

Multicollinearity is an important issue affecting the results of regression analysis. LASSO developed in recent years has great advantages in selecting explanatory variables, processing high-dimensional data, and solving multicollinearity problems. This method adds a penalty term to the model estimation, which can compress the regression coefficients of some unnecessary variables to zero and then remove them from the model to achieve the purpose of variable screening. This paper focuses on the LASSO method and compares it with optimal subsets, ridge regression, adaptive LASSO, and elastic net results. It is found that both LASSO and adaptive LASSO have good performance in solving independent variable multicollinearity problems and enhancing model interpretation and prediction accuracy.

Investigating the association among CO2 emissions, renewable and non-renewable energy consumption in Uzbekistan: an ARDL approach.

This paper examines for the first time the relationship between CO2 emissions and the consumption of renewable and non-renewable energy in Uzbekistan, spanning the period 1985-2020. The analysis uses the Autoregressive Distributed Lags (ARDL) model to estimate the long-run dynamic multipliers and short-run elasticity coefficients of energy consumption variables. Economic factors, such as GDP, are excluded in the analysis as they may cause multicollinearity problems. The empirical results document that in the short- and long-run, hydropower (renewable) energy consumption negatively impacts CO2 emissions per capita, showing a unidirectional causal effect. As regards with non-renewable energy consumption, natural gas and oil energy consumption have a positive impact on CO2 emissions per capita both in the short and long run. Coal consumption positively impacts CO2 emissions in the short run, while it is negative in the long run. Policy measures to enhance the collection of energy from additional renewable energy sources, in particular hydropower, should be taken into account in order to increase the share of renewable energy, and thus, to compensate non-renewable energy consumption which is the main contributor to CO2 emissions. Moreover, solar and wind energy should be explicitly taken into consideration as an additional renewable energy source, which has the lack of attention by policymakers. Furthermore, policy actions, such as the involvement of the private sector into renewable energy projects and the implementation of effective carbon tax policies, could be further options to reduce CO2 emissions.

What are the key factors influencing nursing students' Willingness to serve older adults? An Educational Technology Perspective.

Virtual learning environments (VLEs) employ a virtual environment to support learning activities. VLEs are commonly used to overcome the temporal and spatial restrictions of learning activities held in conventional face-to-face classrooms. In VLEs, students can participate in learning activities using the internet and teachers can provide assistive learning tools during the process. The purpose of this study was to investigate the relationships among nursing students' mental load, cognitive load, and affective learning outcomes in terms of their service willingness toward older adults in an interaction-based educational virtual reality (VR) learning environment. This study employed a cross-sectional method. A total of 130 students participated in interaction-based VR learning and completed related questionnaires. The descriptive and inferential statistics and stepwise regression for data analysis were used. The research results revealed that in the dimension of willingness to use VR learning materials, perceived usefulness received the highest score (mean = 4.42 ± .45). In the dimension of nursing ability, students scored the highest in information management and application ability to care for case patients (mean = 4.35 ± .54). Correlation analysis revealed that cognitive load during learning and willingness to serve older adults were negatively correlated, whereas willingness to use VR learning materials was positively correlated with nursing ability and willingness to serve older adults. Analyzing the regression coefficients of predictor variables revealed that willingness to use VR learning materials (β = .23, t = 2.89, p = .005 < .05) and cognitive load during learning (β = -.35, t = -.4.30, p = .000 < .05) were predictive factors of nursing students' willingness to serve older adults. This study demonstrated that students' willingness to use VR learning materials and their cognitive load during learning affected their willingness to care for older adults. Therefore, the components of mental or cognitive load generate inconsistent predictive effects on affective variables and service willingness toward older adults.

A Model for Product Mix Planning Using Multi-objective Optimization Approach with Sensitivity Analysis

The paper proposes a model for allocation problems in business organizations considering multiple objectives. It concerns the distribution of resources among options in order to achieve the best value for the goals of the business. Thus, the paper develops a model for a product mix plan using the multi-objective optimization (MOO) approach. The objectives set by the paper are to minimize production costs and waste. The inclusion of the production wastes was motivated by United Nations Sustainable Development Goal number 13, which states, “Take immediate action to combat climate change and its impacts.” The developed model was then tested in a manufacturing company and resulted in Pareto-optimal solutions. Such solutions determine the quantity of products to be produced in several production facilities at a particular period of time and will be set as the product mix plan of the case study company. A sensitivity analysis was also performed by varying the weights of the objective functions, which resulted in different decisions about the best product mix plan. It was found that solution 2 is more stable than solution 1. Moreover, the paper also analyzed the permissible ranges for the coefficients of the decision variables and the coefficients for the constraints, where the optimal solutions remain unchanged. The proposed mathematical model thus demonstrates its viability and usefulness in achieving the company's aims.