Linear Regression Method Research Articles

MODIS Evapotranspiration Forecasting Using ARIMA and ANN Approach at a Water-Stressed Irrigation Scheme in South Africa

The forecasting of evapotranspiration (ET) in some water-stressed regions remains a major challenge due to the lack of reliable and sufficient historical datasets. For efficient water balance, ET remains the major component and its proper forecasting and quantifying is of the utmost importance. This study utilises the 18-year (2001 to 2018) MODIS ET obtained from a drought-affected irrigation scheme in the Eastern Cape Province of South Africa. This study conducts a teleconnection evaluation between the satellite-derived evapotranspiration (ET) time series and other related remotely sensed parameters such as the Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI), Normalised Difference Drought Index (NDDI), and precipitation (P). This comparative analysis was performed by adopting the Mann–Kendall (MK) test, Sequential Mann–Kendall (SQ-MK) test, and Multiple Linear Regression methods. Additionally, the ET detailed time-series analysis with the Keiskamma River streamflow (SF) and monthly volumes of the Sandile Dam, which are water supply sources close to the study area, was performed using the Wavelet Analysis, Breaks for Additive Seasonal and Trend (BFAST), Theil–Sen statistic, and Correlation statistics. The MODIS-obtained ET was then forecasted using the Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANNs) for a period of 5 years and four modelling performance evaluations such as the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and the Pearson Correlation Coefficient (R) were used to evaluate the model performances. The results of this study proved that ET could be forecasted using these two time-series modeling tools; however, the ARIMA modelling technique achieved lesser values according to the four statistical modelling techniques employed with the RMSE for the ARIMA = 37.58, over the ANN = 44.18; the MAE for the ARIMA = 32.37, over the ANN = 35.88; the MAPE for the ARIMA = 17.26, over the ANN = 24.26; and for the R ARIMA = 0.94 with the ANN = 0.86. These results are interesting as they give hope to water managers at the irrigation scheme and equally serve as a tool to effectively manage the irrigation scheme.

Investigation of the relationship between socioeconomic status and literacy in PISA Türkiye data

Previous researchers have identified socioeconomic status as a significant predictor of achievement/literacy. However, it is important to recognize that the influence of socioeconomic status on literacy may vary at different levels of socioeconomic status. Thus, this study analyzes the relationship between socioeconomic status and literacy scores for all domains in PISA Türkiye data from 2003 to 2022 through the Classification and Regression Trees and linear regression methods. Upon examining the results, separate investigations carried out for the lower and upper socioeconomic status groups indicate that R2 values were found to be equal to or greater than .80 in 37 out of the 42 analyses. From 2003 to 2009, the R2 values in both groups were considerably high; however, there has been a notable decline in subsequent periods. The year 2009 demonstrated particularly high R2 values by ESCS in all domains for both upper and lower groups. Consequently, socioeconomic status exhibited a greater predictive power on literacy scores across all domains in the lower socioeconomic group than upper socioeconomic group.

Recurrence Multilinear Regression Technique for Improving Accuracy of Energy Prediction in Power Systems

This paper demonstrates how artificial intelligence can be implemented in order to predict the energy needs of daily households using both multilinear regression (MLR) and single linear regression (SLR) methods. As a basic implementation, the SLR makes use of one input variable, which is the total amount of energy generated as an input. The MLR implementation involves multiple input variables being taken from various energy sources, including gas, coal, geothermal, wind, water, biomass, oil, etc. All of these variables are derived from detailed energy production data from the various energy sources. The purpose of this paper is to demonstrate that it is possible to analyze energy demand and supply directly together as a way to produce a more in-depth analysis. By analyzing energy production data from previous periods of time, a prediction of energy demand can be made. Compared to the SLR implementation, the MLR implementation is found to perform better because it is able to achieve a smaller error value. Furthermore, the forecasting pattern is carried out sequentially based on a periodic pattern, so this paper calls this method the recurrence multilinear regression (RMLR) method. This paper also creates a pre-clustering using the K-Means algorithm before the energy prediction to improve accuracy. Other models such as exponential GPR, sequential XGBoost, and seq2seq LSTM are used for comparison. The prediction results are evaluated by calculating the MAE, RMSE, MAPE, MAPA, and time execution for all models. The simulation results show that the fastest and best model that obtains the smallest error (3.4%) is the RMLR clustered using a weekly pattern period.

Online application of the periodic linear regression method for daily monitoring of photovoltaic systems performance

This paper presents an analysis of the performance of photovoltaic (PV) systems using daily applications of the periodic linear regression method to the performance indicators Final Yield (YF) and Reference Yield (YR). The research was conducted on PV systems installed on rooftops and ground at the Federal Institute of Education, Science, and Technology of Goiás (IFG), Itumbiara Campus, from July 2022 to August 2023. The Energy Monitoring and Management Software (SMGE) was developed to calculate the performance indicators and perform linear regression to analyze the performance of the systems. The results demonstrate that the efficiency of the systems can be monitored by analyzing variations in the slopes of the linear regression lines. An increase in energy conversion efficiency was observed following the cleaning of the PV panels, as reflected in significant changes in the regression slopes. Additionally, natural events, such as rainfall, positively impacted system efficiency. These findings highlight the importance of proper maintenance of PV panels, whether through scheduled cleaning or natural events like rain.

Compressive strength of nano concrete materials under elevated temperatures using machine learning

In this study, four Artificial intelligence (AI) - based machine learning models were developed to estimate the Residual compressive strength (RCS) value of concrete supported with nano additives of Nanocarbon tubes (NCTs) and Nano alumina (NAl), after exposure to elevated temperatures ranging from 200 to 800 degrees. These models were developed via adapting meta- heuristic models including the Water cycle algorithm (WCA), Genetic algorithm (GA), and classical AI models of Artificial neural networks (ANNs), Fuzzy logic models (FLM), in addition to the statistical method of Multiple linear regression (MLR). 156 post heating experimental results available as a literature data (represents four input parameters of temperature change, heat exposure duration, nanomaterial type, and replacement proportion) are used to achieve the study’s objective. Results of the developed models demonstrated that ANN and FLM have strong potential in predicting RCS. However, it is often infeasible to generate practical equations that relate input and output variables from these models. Upon analysing the results of the WCA and GA, it was found that WCA yielded the most accurate predictions based on all performance indicators. Furthermore, RCS prediction equations with superior accuracy were derived utilizing the meta-heuristic AI models of WCA and GA, with Mean absolute errors (MAEs) of 3.09 kg/cm² and 3.53 kg/cm² for the training, 1.91 kg/cm² and 2.72 kg/cm² for the validation, and 1.91 kg/cm² and 2.72 kg/cm² for the testing data sets, respectively. Additionally, sensitivity analysis via neural networks weights and SHAP investigation were performed to reveals the impact and relationship of the input variables with the output variables. Both techniques reveal that temperature degree and time of exposure had the highest positive impact on RCS value, followed by NAl and NCTs, in order.

Data Mining Predicts the Number of Scout Enthusiasts in Binjai City Using the Linear Regression Method

This study aims to predict the number of scout enthusiasts in Binjai City using a linear regression method. The data used includes the number of active scout members from the Alert, Fundraiser, and Enforcer levels during the 2018-2024 school year. The linear regression method was chosen because of its ability to predict future trends based on historical data. This research implements a linear regression algorithm through the Python programming language for the development of prediction systems. The problem in this study is that scout education in Binjai City has many impacts that can affect the number of scout enthusiasts in an educational institution. Some of the factors that can affect the quality of the teacher can be a determining factor in the interest in participating in scouting. With the existence of qualified scout teaching staff and creativity, students will be more in demand to participate in scouting.and the achievement of providing facilities provided from the School Level to the Branch Committee can also be a factor in attracting interest in participating in scouting and the support of school principals and the surrounding government can also be a determining factor in the development of scouting in the region so that activities from the school level to the international level are carried out which will be more in demand by scouting students. This finding is expected to help the Branch of the Binjai City Scout Movement in preparing and optimizing the number of scouting students in the future. This value means that the relationship between the free variable / predictor X and the bound variable / response Y is a low increase, the percentage is 93%. With the overall interest of 3191 scout members active scout members in the coming year with the number of Standby (1756 people), Fundraisers (662 people), Enforcers (773 people) So, the interest of scouts in the city of Binjai is influenced by the number of active Scout members this year.

Linear Regression Algorithm the Effect of Game Time on Students' Reading Interest

Education plays an important role in shaping an individual's character and abilities, with students' interest in reading as the main foundation for intellectual development and critical thinking skills. However, in today's digital age, there has been an alarming decline in students' interest in reading, who are more interested in spending their free time playing games than reading. The development of information technology and digital entertainment such as smartphones, tablets, and computers has changed students' habits in utilizing their free time. Playing games is the main choice, while reading interest is marginalized, which has a negative impact on students' literacy skills. The decline in reading interest has serious implications, especially in Indonesia, where the Human Development Index (HDI) in the field of education is still low compared to neighboring Malaysia. This low interest in reading is influenced by the lack of reading habits from an early age and unequal access to education. Good reading skills not only affect academic achievement but also the development of critical, analytical, and creative thinking skills. This study aims to understand the factors that affect students' interest in reading, especially the influence of game time. The Simple Linear Regression method was used to analyze the relationship between game play time and students' reading interest, which provided valuable insights for educators and parents in designing effective educational strategies. The study focused on SMP Negeri 7 Binjai and used a linear regression method to analyze data on students' reading and gaming habits. The results of the study show that excessive game playing time, an average of 9 to 10 hours per day, has an impact on increasing and decreasing students' interest in reading. This data was analyzed using the RapidMiner application, which showed a correlation between playing games and students' reading interest.

Application of Linear Regression in Predicting Education Level and Income of Residents (Case Study: Desa Padang Cermin)

Linear regression methods were used to predict education and income levels in Padang Cermin Desa. Padang Cermin Desa has twelve hamlets and a total of 13,055 residents, with 6,135 men and 6,920 women, and 2545 households. The aim of this study is to raise government and community awareness of the importance of education for welfare and a better life in the future. Using existing data, this study can provide a clear picture of the relationship between education levels and income as well as relevant recommendations to improve the quality of life in Padang Cermin Desa. This research uses a quantitative case study design with secondary data collected through hamlet heads and semi-structured interviews. The linear regression equation Y 36900147.57 + 2516320.971X is based on the MAPE value with a result of 28.25% and an accuracy rate of 71.75%. In the case study of applying linear regression to predict the education level and income of residents, the following are some conclusions: The prediction results show that people with elementary school education are estimated to have an income of 421,897,256.1, while people with secondary school education are estimated to have an income of 311,179,133.4. People who completed a senior high school education are estimated to have the highest income of IDR 457,125,749.7, showing the importance of secondary education. Higher education and vocational education still have the potential to be improved, although the income of the population with education is estimated at IDR 64,579,678.25 for Diploma I/II/III and IDR 79,677,604.08 for Diploma IV/Strata I.

Application of Data Mining to Measure the Level of Satisfaction with Public Facilities and Services at STMIK Kaputama Binjai Using Linear Regression Method

This study aims to analyze the level of satisfaction of STMIK Kaputama Binjai students with physical facilities (classrooms, laboratories, prayer rooms, wifi) and general services (administration, academic guidance, library, security, campus cleanliness) using multiple linear regression methods. Data were collected through questionnaires from students in the 2022/2023 academic year. The results showed that both variables have a significant effect on student satisfaction, with a regression coefficient of physical facilities of 0.40 and general services of 0.59, indicating that general services have a greater impact. Prediction of student satisfaction reached an accuracy level of 98% with a Mean Absolute Percentage Error (MAPE) value of 2%. Laboratory facilities and internet access (wifi) are the dominant factors affecting satisfaction. Based on these findings, improvements in both aspects are recommended to increase student satisfaction and institutional competitiveness.

Analysis of Village Residents Receiving Social Assistance Using Linear Regression Method

This study aims to analyze the recipients of social assistance in Banyumas Village using the simple linear regression method. The research examines how household income affects the amount of social assistance received. Data was collected from the Banyumas Village Office, including information on income and the amount of social assistance received by residents. The results show a negative relationship between household income and the amount of assistance received, where higher income leads to smaller assistance. The model also demonstrates good accuracy with an average prediction error (MAPE) of 9.38%. Additionally, an R² value of 0.999972 indicates that the model can explain almost all variations in the data. This study provides valuable insights into the effectiveness of the social assistance program in Banyumas Village and to help improve the program in the future.

Optimizing the Preparation of Work Plans Using Linear Regression

Preparing effective and efficient work plans to achieve regional government strategic goals is often faced with budget management challenges, especially in the face of increasing indicative ceilings. This research optimizes this process at the Langkat Regency Regional Secretariat using the linear regression method. This method evaluates previous work plans and adapts them to regional financial capabilities for more efficient budget use. The research results show that linear regression provides accurate predictions of budget needs, with an error rate (MAPE) of 0.08% and an accuracy rate of 99.92%. It is hoped that this optimization can support regional development goals in a structured and measurable manner.

Metal single-atom interaction with graphitic C3N4 surface based on density functional theory calculations and linear regression analysis

This paper discusses the adsorption of metal single-atoms (SAs) on graphitic C3N4 (gCN) multilayered surface models based on density functional theory (DFT) calculations. We systematically searched stable adsorption sites on gCN and showed that the cavity site is preferred for metal SAs. We also discussed the usefulness of several descriptors, such as the distance between the SA and gCN, charge donation (electron transfer), and atomic radius, for evaluating the SA adsorption energy, based on the linear regression method. In addition, the most stable adsorption energy for fifth- and sixth-row metals can be predicted using only the “group” descriptor of the periodic table. The group descriptor is closely related to the atomic radius, and hence the stability of fifth- and sixth-row metals is determined from the fit between the SA radius and the gCN cavity size. The study findings facilitate the development of various metal-dopant techniques for gCN-based applications.

Association between risk factors and bone mineral density and the development of a self-assessment tool for early osteoporosis screening in postmenopausal women with type 2 diabetes.

Both diabetes and osteoporosis have developed into major global public health problems due to the increasing aging population. It is crucial to screen populations at higher risk of developing osteoporosis for disease prevention and management in postmenopausal women with type 2 diabetes (T2D). This study aims to quantitatively investigate the association between risk factors and bone mineral density (BMD) and develop a self-assessment tool for early osteoporosis screening in postmenopausal women with T2D. We retrospectively enrolled 1,309 postmenopausal women with T2D. Linear regression methods were used to assess the association between risk factors and BMD. Additionally, a multivariate logistic regression analysis was performed to identify independent risk factors associated with osteoporosis. Utilizing the logistic regression machine learning algorithm, we developed an osteoporosis screening tool that categorizes the population into three risk regions based on age and body mass index (BMI), indicating low, moderate, and high prevalence of osteoporosis in the age-BMI plane. Older age and lower BMI were independently associated with decreased BMD. The BMD at the total hip, femur neck, and lumbar spine differed by 12.9, 10.9, and 15.5 mg/cm2 for each 1 unit increase in BMI, respectively. Both age and BMI were identified as independent predictors of osteoporosis. The osteoporosis screening tool was developed by using two straight lines with equations of BMI = 0.56 * age-4.12 and BMI = 0.56 * age-10.88; there were no significant differences in the prevalence of osteoporosis among the training, internal test, and external test datasets in the low-, moderate-, and high-risk regions. We have successfully developed and validated a self-assessment tool for early osteoporosis screening in postmenopausal women with T2D for the first time. BMI was identified as a significant modifiable risk factor. Our study may improve awareness of osteoporosis and is valuable for disease prevention and management for postmenopausal women with T2D.

Ecotoxicological risk assessment of active pharmaceutical ingredients (APIs) against different aquatic species leveraging intelligent consensus prediction and i-QSTTR modeling

The increasing presence of active pharmaceutical ingredients (APIs) in aquatic ecosystems, driven by widespread human use, poses significant risks, including acute and chronic toxicity to aquatic species. However, the scarcity of experimental toxicity data on APIs and related compounds due to the high costs, time requirements, and ethical concerns associated with animal testing hinders comprehensive risk assessment. In response, we developed quantitative structure-toxicity relationship (QSTR) and interspecies quantitative structure toxicity-toxicity relationship (i-QSTTR) models for three key aquatic species: zebrafish, water fleas, and green algae, using NOEC as an endpoint, following OECD guidelines. Algae, daphnia, and fish, recognized as standard organisms in toxicity testing, are crucial bio-indicators due to their size, transparency, adaptability, and regulatory acceptance. We used partial least squares (PLS) and multiple linear regression (MLR) methods for model development alongside machine learning techniques such as Random Forest (RF), Support Vector Machines (SVM), K-nearest Neighbor (kNN), and Neural Networks (NN) to enhance the predictivity. Lipophilicity, electronegativity, unsaturation, a molecular cyclized degree in molecular structure, large fragments, aliphatic secondary C(sp2), and R–CR–R groups were identified as critical biomarkers for API toxicity. Screening of the PPDB (pesticide properties databases) and DrugBank validated the practical application of these models, offering valuable tools for regulatory decisions, safer API design, and the preservation of aquatic biodiversity.

Transient electromagnetic data denoising based on cluster analysis and locally weighted linear regression

AbstractIn transient electromagnetic surveys, the collected data inevitably contain noise originating from both natural and cultural sources. This noise has the potential to mask transient electromagnetic responses linked to geological features, thereby posing challenges in accurately interpreting subsurface structures. Hence, the implementation of effective noise reduction techniques is crucial in ensuring the accuracy and reliability of inversion outcomes in transient electromagnetic surveys. This study introduces a novel approach that merges k‐means clustering with locally weighted linear regression to denoise transient electromagnetic data. The results from synthetic examples illustrate that the k‐means locally weighted linear regression method can predict transient electromagnetic data closely resembling true values, similar to the long short‐term memory autoencoder. Occam's inversion results derived from denoised data using both the k‐means locally weighted linear regression and long short‐term memory–autoencoder methods can well reflect the true model. Notably, a key advantage of the k‐means locally weighted linear regression method is its independence from labelled data as the sample set. The k‐means locally weighted linear regression method was applied to field data collected at the Narenbaolige coalfield in Inner Mongolia, China. Occam's inversion models generated from the denoised field data delineate the boundary between the basaltic body and sedimentary rocks, aligning with drilling data. The inversion models derived from the noisy field data also can capture this boundary, but deep section views reveal the presence of numerous intricate high‐resistivity anomalous bodies. These observations highlight the effectiveness of the k‐means locally weighted linear regression method in denoising transient electromagnetic data.

Optimization method of parameters inverse identification for hot deformation constitutive model of 2Cr13 martensitic stainless steel using genetic algorithm

Constitutive models are considered a key prerequisite to investigate the thermal deformation behavior of materials. Accurate identification of their parameters is crucial for enhancing the predictive precision of the models. A novel optimization method for accurate inverse identification of parameters using the genetic algorithm (GA) in the Modified Zerilli-Armstrong (MZA) model was proposed in this work. Under conditions of temperatures ranging from 1223 K to 1473 K at intervals of 50 K and strain rates of 0.01, 0.1, 1, and 5 s−1, uniaxial isothermal hot compression tests were performed on 2Cr13 martensitic stainless steel (MSS) employing a Gleeble-1500D thermal simulation test machine. Based on the experimental data, the conventional linear regression method was utilized to solve the MZA model of 2Cr13 MSS. Initial values for the material constants I1, S1, C5, and C6 to be optimized were assigned drawing from the calculated results. The GA-based iteratively optimized MZA (G-ZA) model was established by minimizing the mean squared error as an objective function between the experimental and predicted flow stress values. Compared to the MZA model, a significant enhancement in predictive performance was achieved with the G-ZA model, while good generalization was also demonstrated. Both models were successfully integrated into the Forge® finite element analysis software through the secondary development of user subroutines. Numerical simulation results indicated that the G-ZA model demonstrated a better agreement with the experimental data in predicting the load-displacement curve. This validated that a more accurate constitutive model for the hot deformation of 2Cr13 MSS can be effectively constructed using the GA-based parameter inverse identification strategy.

Pengaruh Kualitas Pelayanan dan Fasilitas Wisata terhadap Kepuasan Pengunjung di Objek Wisata Taman rekreasi dan Rumah Susu Lachio Patumbak

The target of this study is to observe the influence of service quality and tourist facilities on visitor satisfaction at the Lachio Patumbak Recreation Park and Milk House tourist attraction. This study is of a quantitative type by including several variables including Service Quality and Tourist Facilities as independent variables and Visitor Satisfaction as the dependent variable. To collect data, a questionnaire was used and a total random sample of 93 respondents. The analysis uses a multiple linear regression method using SPSS version 20 application software. The results of the study show that Service Quality has a significant partial impact on Visitor Satisfaction, because t calculated for Service Quality is above the t table (7.778 > 1.986) and the sign is 0.000 < 0.05. However, the use of tourist facilities has a partial impact on visitor satisfaction with a sign of 0.010 > 0.05 and t calculated above t table (2.643 > 1.986). Then the quality of tourist services & facilities has a significant impact on visitor satisfaction together because f count > f table (84.645 > 3.10) is significantly 0.000 < 0.05.

A Study on the Impact of Traits of Managers on the Financial Performance of Selected Nifty 50 Companies

Purpose: A manager is a key person who has the ability to hasten the company's success. No two managers are the same since everyone has a unique background and set of traits that might have an influence on how the organization functions. This study's primary objective is to demonstrate a link between managerial traits and their significant impact on the financial performance of NIFTY 50-listed firms. These traits are age, gender, educational background, total time spent working for these NIFTY 50 listed firms, and total time spent working for the managers in the particular NIFTY 50 listed firm’s organization. These criteria are seen as independent of the investigation (Sreemathi, S., & Sekhara Rao, K. S., 2021). Design/Method/Answer: The data were collected from the official company websites, where the financial reports of the company have been published for the common people's access, and the traits of the management in terms of age, gender, and educational qualification have been covered from the Nifty 50 company’s websites. Findings: Regression analysis in terms of the multiple linear regression method has been applied to highlight the different managerial traits, i.e., gender as a trait of management personnel, that have a significant impact on the financial performance of the Nifty 50 companies, i.e., ROE and ROA (Kamath, G. B., 2022). Originality/Value: This study provides meaningful insights in terms of the different human traits that are prevailing in the management officials, which have a significant impact on the management decisions on the various parameters and have a significant impact on the financial performance of the companies in the Nifty 50. There are other human traits that might also have an impact on the decision-making of the management, which leads to the performance of the company

Toward Cross-Species Crop Se Content Prediction Using Random Forest Modeling

Selenium is an indispensable trace element in the human body that plays an important role in maintaining life activities. The consumption of Se-rich crops provides a practical and effective way for the body to supplement Se. However, the Se content in crops is affected by the soil Se content and the interactions between other elements in the soil. In this study, the Tibetan Plateau of China was chosen as the study area. The random forest algorithm was applied to select four key indicators—selenium (Se), bioavailable phosphorus (P), cadmium (Cd), and bioavailable copper (Cu)—from 29 soil variables to predict the Se content in rapeseed, wheat, potato, pasture, and chrysanthemum crops. The results showed that, despite the rich soil Se resources in the Tibetan Plateau, only 20% of the crop samples met the national Se enrichment standard (>0.07 mg kg−1). Compared with the traditional multiple linear regression method, the random forest model is more accurate, efficient, and reliable in predicting the Se content of crops. In cross-species crop prediction, which refers to the simultaneous cultivation and analysis of multiple distinct crop species within the same agricultural setting, the random forest model demonstrated superior performance, marking a significant breakthrough in cross-species crop research. This approach effectively eliminates the tedious process of conducting repetitive individual evaluations for different crop types in the same region, highlighting its innovative significance. Meanwhile, the Tibetan Plateau, known as the “Roof of the World”, is also of great research value. These results provide valuable references for the planning and management of Se-enriched farmlands, which will help improve the yield and quality of Se-enriched crops and promote the growth of farmers’ interests.

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.