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The paper is devoted to the research of the two-criteria estimation method for linear regressions. The first criterion corresponds to the least absolute deviations, the second – to the non-strict ordinary least squares. The implementation of the method requires solving a two-criteria linear programming problem, the solution of which involves the formation of a Pareto set. The main goal of the article was to research how the normalization of the initial variables affects the formation of the Pareto set. For this, two samples were used. The first was created artificially and contains an outlier. The second was formed on the basis of real economic data. In both cases, when normalizing the variables, the Pareto set turned out to be more representative than when working with non-normalized indicators. The example with an outlier illustrates the robustness of the least absolute deviations and the anti-robust ness of the ordinary least squares. It is shown how, based on the predicted values of the explained variable, it is possible to choose the optimal Pareto vertex.

In order to solve the problem of the traditional remote sensing drought index focuses on the monitoring of a single response factor and lacks a complete analysis of drought.In this paper， we selected TVDI， RVI， PDI， and GVMI daily products estimated from remote sensing data as independent variables， and MCI calculated from meteorological data at the adjacent moments of satellite transit as dependent variables， and uses the Random Forest Regression （RFR） model to construct a integrated remote sensing drought monitoring model.The results show that the accuracy of RFR model is better than that of the Ordinary Least Squares （OLS） model in bothtraining data and test data.The R value of the RFR training data is 0.97， the RMSE is 0.33， the R value of the RFR test data is 0.90， and the RMSE is 0.53.The R value of the OLS training data is 0.78， the RMSE value is 0.73， the R value of the OLS test data is 0.76， and the RMSE value is 0.79.The comparisons of RFR and OLS model in R and RMSE show that the RFR model is superior than the OLS model in the characterization of regional drought.In the application of drought monitoring in Southwest China in 2022， the RFR results are consistent with the spatiotemporal distribution of the MCI index， which can better characterize the spatial and temporal dynamics of the regional drought， reflecting the practicality of the RFR model in the actual drought monitoring process.However， the accuracy of RFR model is related to the number of regional stations and the spatial distribution of stations， and the accuracy of the RFR model is higher in areas with a large number of stations and uniform distribution of stations.

Pork consumption has risen significantly in many emerging nations, with producers using various systems to meet demand. However, the profitability of these systems remains largely unexplored. Therefore, the drivers of profitability of pig production systems in Northern Uganda were examined. Data were collected using a pretested structured questionnaire through a cross-sectional survey of 240 randomly selected pig farmers. Data were analyzed using descriptive statistics, gross margin analysis, and ordinary least squares model. Results revealed that the cost of initial stock (p < 0.1), cost of feed (p < 0.05), cost of vaccines (p < 0.01), output (p < 0.05), and quantity of feed (p < 0.05) were drivers of profitability in the farrow-to-finish pig production system. Further, profitability in the farrow-to-weaner pig production system was influenced by access to credit (p < 0.1), household size (p < 0.1), access to extension service (p < 0.01), and cost of initial stock (p < 0.05). In the weaner-to-slaughter pig production system, drivers of profitability included access to extension service (p < 0.1), cost of feed (p < 0.1), cost of vaccines (p < 0.05), and cost of initial stock (p < 0.05). Researchers recommend that the government arrange sufficient capacity-building initiatives and training, particularly on the farrow-to-weaner pig production system to increase the output and profitability of this production system. Further, the government and non-governmental organizations should make inputs such as vaccines, drugs, and breeding stock available to pig farmers at competitive market prices to enable farmers to make price-responsive decisions.

East Asia, particularly China, Japan, and South Korea has experienced rapid economic growth in recent decades, partly through active participation in global value chains (GVCs). Foreign direct investment (FDI) has played a crucial role in this process, enabling the transfer of technology and skills and access to international markets. This paper aims to empirically analyse the effects of FDI on GVCs in East Asian countries over the last two decades.

East Asia, particularly China, Japan, and South Korea has experienced rapid economic growth in recent decades, partly through active participation in global value chains (GVCs). Foreign direct investment (FDI) has played a crucial role in this process, enabling the transfer of technology and skills and access to international markets. This paper aims to empirically analyse the effects of FDI on GVCs in East Asian countries over the last two decades. The paper covers six East Asian countries from 2000 to 2022 and explores the relationship between FDI and GVCs using the Driscoll and Kraay (1998) estimator. The estimation techniques are ordinary least squares (OLS) and feasible generalized least squares (FGLS). The results reveal that FDI inflows into East Asia positively and significantly affect the countries participation in GVCs, both upstream and downstream. These results suggest the need to support FDI in Asian countries to improve their participation in GVCs. [ABSTRACT FROM AUTHOR]

Simultaneous causality occurs when two variables mutually influence each other, creating empirical contexts where cause and effect are not clearly unidirectional. Crime and policing often appear in urban studies presenting the following characteristic: sometimes, increased police patrols can reduce criminal activities, and other times, higher crime rates can prompt law enforcement administrations to increase patrols in affected areas. This study aims to explore the relationships between patrol dynamics and crime locations using spatial regression to support public policies. We identify spatial patterns and the potential impact of crime on policing and vice versa. Data on crimes and patrol locations were collected from the database provided by the Planning and Management Secretariat and the Social Defense Secretariat of Pernambuco, Brazil. The study employed Ordinary Least Squares (OLS) to create a spatial simultaneous regression model for integrated security zones within the Brazilian geography. This approach provides a holistic visualization, enhancing our understanding and predictive capabilities regarding the intricate relationship between police presence and crime. The results report a significant relationship, with crime locations explaining police patrols (varying in geographic domain and type of crime). No statistically significant results from most geographic locations point to the inverse relation. The quantitative analysis segregated by typology presents a potential for effective public decision support by identifying the categories that most influence the patrol security time. [ABSTRACT FROM AUTHOR]

International trade is a vital driver of economic development for nations worldwide, and Bangladesh, as a developing country in South Asia, strategically utilizes trade as a key element in its economic growth agenda. This paper examines the impact of internal factors on Bangladesh's international trade, employing Ordinary Least Squares (OLS) regression analysis to analyze data spanning from 2000 to 2022. The study focuses on factors such as GDP, inflation, real interest rates, unemployment rates, government expenditure, population growth, remittance inflows, government expenditure in education, macroeconomic management, and tariff rates. The results reveal a strong statistical significance between the predictors and Bangladesh's international trade volume. Notably, personal remittances, government expenditure in education, and macroeconomic management index exhibit significant influence on international trade. The findings provide valuable insights for policymakers and stakeholders to enhance Bangladesh's trade performance, contributing to its sustainable economic development.

International trade is a vital driver of economic development for nations worldwide, and Bangladesh, as a developing country in South Asia, strategically utilizes trade as a key element in its economic growth agenda. This paper examines the impact of internal factors on Bangladesh's international trade, employing Ordinary Least Squares (OLS) regression analysis to analyze data spanning from 2000 to 2022. The study focuses on factors such as GDP, inflation, real interest rates, unemployment rates, government expenditure, population growth, remittance inflows, government expenditure in education, macroeconomic management, and tariff rates. The results reveal a strong statistical significance between the predictors and Bangladesh's international trade volume. Notably, personal remittances, government expenditure in education, and macroeconomic management index exhibit significant influence on international trade. The findings provide valuable insights for policymakers and stakeholders to enhance Bangladesh's trade performance, contributing to its sustainable economic development. [ABSTRACT FROM AUTHOR]

IntroductionConsidering the current process of destruction of natural resources in the country and the problems faced by the present and future generations, the measures taken in the field of conservation and restoration and development of natural resources do not seem to be enough. Although, to solve these problems, the role of the government as a planner and supporter of natural resources projects is clear and important (Arayesh & Farajilah Hosseini, 2010). However, the projects designed to conserve natural resources are implemented based on people's participation. In general, people's participation in projects in the field of natural resources protection can take place in various fields. The most important of these issues have been considered in the behavioral examples of the Helpers of Nature project which can be including planting seedlings, people's participation in times of crisis such as fire, protection of forests, contributing to environmental protection associations, joining environmental associations, waste management in terms of separation and volume, joining the natural resources projects through the organization's systems, introducing people to the natural resources organization in the form of nature's helper, paying the green tax and etc (Natural resources & watershed management organization-I.R of IRAN, 2023). However it is the question as how to attract people's participation in the mentioned issues requires tools that can influence human behavior. Behavioral economics, as a new scientific field of economic sciences, can measure and analyze the impact of intentions, beliefs, and motivations on human behavior and decisions, and based on this, it can also provide policy tools (Asgari et al., 2021). For this reason, it can influence people's behavior to participate in the conservation of natural resources using behavioral economics approach. Therefore, to realize the goal of attracting people's participation, the current study considers to examine nudges and the effectiveness of nudges on people's behavior using behavioral economics approach Materials and MethodsThe sampling method of the research is convenience sampling. The number of samples is 213 people using the online questionnaire in two separate groups. This study is a quasi-experimental design and its type is a comparison between two groups. The number of the control group includes 108 people and the number of the treatment group includes 105 people. The control and treatment groups were independent. Each of the groups had completely common questions and response criteria. In this study, the control and treatment groups did not receive any training, but only the treatment group was given additional information about cognitive errors along with related questions. First, the Mann-Whitney-Wilcoxon test is used to check and compare the ratings regarding supplementary information as well as the answers of people in the two groups. The ordered probit regression is used to analyze the relationship between ordinal or ranked dependent variables related to natural resource protection behavior and independent treatment (nudge) variables and other variables. For the relative dependent variables (the time of registration of cooperation request and the number of people introduced as a nature helper), the ordinary least square regression is used to analyze the effect of the treatment binary variable on the people's behavior in the field of natural resources conservation Results and DiscussionThis study results showed that out of 19 nudges, 14 nudges includes; Normative default, time limit, anchor and exemplify, carrot and stick, personalization, decoy effect, authority confirmation bias, bandwagon effect, present bias, automatic recommendation, halo effect and ownership effect (1) and (2) and (3) had a significant impact on these people's behavior, respectively, the number of seedlings, the time of registering a request for cooperation, participation in firefighting, waste production, the number of members introduced as a nature’s helper, choosing tasks, membership in associations, membership in a special association, recycling, people's action preferences for forest protection, the percentage of perceived success for projects, willingness to spend taxes to beautify one's neighborhood, willingness to spend taxes to protect forests in one's area, and applying zoning to protect forests. The direction of influence in all nudges (except for the normative default and time limit) on people's behavior has been positive and significant. The marginal effects also showed that all nudges had the positive effect (with ordinal or ranked dependent variable) on the selection of the target option(s) in the treatment group compared to the control group. The carrot and stick policy had no significant effect on the ordinal variable of waste production, but the effect of this nudge on dummy variable of waste production was significant, This means that this nudge has had a positive and significant effect on maintaining the existing situation (garbage collection every day of the week) and reducing the amount of garbage (choosing 20 kg of garbage and less per week). The normative default and the time limit had a negative and significant effect (respectively) on the number of seedlings and the registration time of cooperation requests for planting seedlings. These negative effects have also confirmed the positive effect of nudging on people's behavior. Although the normative default resulted in fewer seedlings being planted by individuals, this occurred because the default was set at a minimal level and individuals were significantly more inclined to follow the default. The nudge of the time limit also led to a reduction in the time to register cooperation requests by individuals, so that people tended to register their request faster. ConclusionAccording to the results of this research, to attract the people's participation in the conservation of natural resources, these following should be considered: defaults, low-cost anchors, clear examples, incentives and punishments, highlight individual performance through personalization, using existing privileges for more cooperation, confirming people's sovereignty, presenting reports during performance, immediate rewards, making SMS and telephone systems available to compensate for people's lack of action, considering time limits for registering people in programs and projects, providing success reports to join people in an action, applying people's ownership of the green tax to further encourage them to pay taxes, allocating each zone to an environmental association for forests protection.

Abstract The OLS model is built on the assumption of normality in the distribution of error terms. However, this assumption can be easily violated, especially when there are outliers in the data. A single outlier can disrupt the normality assumption of error terms, making the OLS model less effective. In such situations, M-estimators (MEs) come into play to obtain reliable estimates. We introduce a redescending M-estimators (RME) for robust regression to handle datasets with outliers. The proposed RME produces more robust estimates by effectively managing the influence of outliers, even at lower values of the tuning constant. We compared the performance of this estimator with existing RMEs using real-life data examples and an extensive simulation study. The results show that our suggested RME is more efficient than the compared ME in various situations.

Introduction Considering the current process of destruction of natural resources in the country and the problems faced by the present and future generations, the measures taken in the field of conservation and restoration and development of natural resources do not seem to be enough. Although, to solve these problems, the role of the government as a planner and supporter of natural resources projects is clear and important (Arayesh & Farajilah Hosseini, 2010). However, the projects designed to conserve natural resources are implemented based on people's participation. In general, people's participation in projects in the field of natural resources protection can take place in various fields. The most important of these issues have been considered in the behavioral examples of the Helpers of Nature project which can be including planting seedlings, people's participation in times of crisis such as fire, protection of forests, contributing to environmental protection associations, joining environmental associations, waste management in terms of separation and volume, joining the natural resources projects through the organization's systems, introducing people to the natural resources organization in the form of nature's helper, paying the green tax and etc (Natural resources & watershed management organization-I.R of IRAN, 2023). However it is the question as how to attract people's participation in the mentioned issues requires tools that can influence human behavior. Behavioral economics, as a new scientific field of economic sciences, can measure and analyze the impact of intentions, beliefs, and motivations on human behavior and decisions, and based on this, it can also provide policy tools (Asgari et al., 2021). For this reason, it can influence people's behavior to participate in the conservation of natural resources using behavioral economics approach. Therefore, to realize the goal of attracting people's participation, the current study considers to examine nudges and the effectiveness of nudges on people's behavior using behavioral economics approach Materials and Methods The sampling method of the research is convenience sampling. The number of samples is 213 people using the online questionnaire in two separate groups. This study is a quasi-experimental design and its type is a comparison between two groups. The number of the control group includes 108 people and the number of the treatment group includes 105 people. The control and treatment groups were independent. Each of the groups had completely common questions and response criteria. In this study, the control and treatment groups did not receive any training, but only the treatment group was given additional information about cognitive errors along with related questions. First, the Mann-Whitney-Wilcoxon test is used to check and compare the ratings regarding supplementary information as well as the answers of people in the two groups. The ordered probit regression is used to analyze the relationship between ordinal or ranked dependent variables related to natural resource protection behavior and independent treatment (nudge) variables and other variables. For the relative dependent variables (the time of registration of cooperation request and the number of people introduced as a nature helper), the ordinary least square regression is used to analyze the effect of the treatment binary variable on the people's behavior in the field of natural resources conservation Results and Discussion This study results showed that out of 19 nudges, 14 nudges includes; Normative default, time limit, anchor and exemplify, carrot and stick, personalization, decoy effect, authority confirmation bias, bandwagon effect, present bias, automatic recommendation, halo effect and ownership effect (1) and (2) and (3) had a significant impact on these people's behavior, respectively, the number of seedlings, the time of registering a request for cooperation, participation in firefighting, waste production, the number of members introduced as a nature's helper, choosing tasks, membership in associations, membership in a special association, recycling, people's action preferences for forest protection, the percentage of perceived success for projects, willingness to spend taxes to beautify one's neighborhood, willingness to spend taxes to protect forests in one's area, and applying zoning to protect forests. The direction of influence in all nudges (except for the normative default and time limit) on people's behavior has been positive and significant. The marginal effects also showed that all nudges had the positive effect (with ordinal or ranked dependent variable) on the selection of the target option(s) in the treatment group compared to the control group. The carrot and stick policy had no significant effect on the ordinal variable of waste production, but the effect of this nudge on dummy variable of waste production was significant, This means that this nudge has had a positive and significant effect on maintaining the existing situation (garbage collection every day of the week) and reducing the amount of garbage (choosing 20 kg of garbage and less per week). The normative default and the time limit had a negative and significant effect (respectively) on the number of seedlings and the registration time of cooperation requests for planting seedlings. These negative effects have also confirmed the positive effect of nudging on people's behavior. Although the normative default resulted in fewer seedlings being planted by individuals, this occurred because the default was set at a minimal level and individuals were significantly more inclined to follow the default. The nudge of the time limit also led to a reduction in the time to register cooperation requests by individuals, so that people tended to register their request faster. Conclusion According to the results of this research, to attract the people's participation in the conservation of natural resources, these following should be considered: defaults, low-cost anchors, clear examples, incentives and punishments, highlight individual performance through personalization, using existing privileges for more cooperation, confirming people's sovereignty, presenting reports during performance, immediate rewards, making SMS and telephone systems available to compensate for people's lack of action, considering time limits for registering people in programs and projects, providing success reports to join people in an action, applying people's ownership of the green tax to further encourage them to pay taxes, allocating each zone to an environmental association for forests protection. [ABSTRACT FROM AUTHOR]

The OLS model is built on the assumption of normality in the distribution of error terms. However, this assumption can be easily violated, especially when there are outliers in the data. A single outlier can disrupt the normality assumption of error terms, making the OLS model less effective. In such situations, M-estimators (MEs) come into play to obtain reliable estimates. We introduce a redescending M-estimators (RME) for robust regression to handle datasets with outliers. The proposed RME produces more robust estimates by effectively managing the influence of outliers, even at lower values of the tuning constant. We compared the performance of this estimator with existing RMEs using real-life data examples and an extensive simulation study. The results show that our suggested RME is more efficient than the compared ME in various situations. [ABSTRACT FROM AUTHOR]

For chemical measurements, calibration is typically conducted by regression analysis. In many cases, generalized approaches are required to account for a complex-structured variance–covariance matrix of (in)dependent variables. However, in the particular case of highly correlated independent variables, the ordinary least squares (OLS) method can play a rational role with an approximated propagation of uncertainties of the correlated independent variables into that of a calibrated value for a particular case in which standard deviation of fit residuals are close to the uncertainties along the ordinate of calibration data. This proposed method aids in bypassing an iterative solver for the minimization of the implicit form of the squared residuals. This further allows us to derive the explicit expression of budgeted uncertainties corresponding to a regression uncertainty, the measurement uncertainty of the calibration target, and correlated independent variables. Explicit analytical expressions for the calibrated value and associated uncertainties are given for straight-line and second-order polynomial fit models for the highly correlated independent variables. [ABSTRACT FROM AUTHOR]

The detection of nonstationarities in partial duration time series (PDS) depends on several factors, including the length of the time series, the selected statistical test, and the heaviness of the tail of the distribution. Because of the more limited attention received in the literature when compared to the trend detection on block maxima variables, we perform a Monte Carlo simulation study to evaluate the performance of different approaches: Spearman's rho, Mann–Kendall, ordinary least squares (OLS), Sen's slope estimator (SEN), and the nonstationary generalized Pareto distribution fit to identify the presence of trends in PDS records characterized by different sample sizes (n), shape parameter (ξ) and degrees of nonstationarity. The results point to a power gain for all tests by increasing n and the degree of nonstationarity and by reducing ξ. The use of a nonparametric test is recommended in samples with a high positive skew. Furthermore, the use of sampling rates greater than one to increase the PDS sample size is encouraged, especially when dealing with small records. The use of SEN to estimate the magnitude of a trend is preferable over OLS due to its slightly smaller probability of occurrence of type S error when ξ is positive. [ABSTRACT FROM AUTHOR]

Brazil has experienced a significant escalation in crime rates, resulting in many municipalities ascending to top positions in international rankings of the most violent cities. Various socioeconomic factors contribute to this surge in crime rates, prompting public policies to address not only policing but also elements such as education and social inequality. This study aims to conduct a regression analysis on education, inequality, and crime indicators in the municipalities of the state of Pernambuco based on the Ordinary Least Squares (OLS) method. The findings provide insights into the interplay between these factors and guide the formulation of more effective, multifaceted public policies, supporting policymakers on the critical importance of integrating educational and social inequality measures into crime reduction strategies. Additionally, by highlighting specific socioeconomic drivers of crime, the research may lead to more targeted and sustainable interventions in the most affected municipalities.

This paper aims to examine the role of economic diplomacy in attracting foreign capital to emerging countries, by developing a composite index measuring diplomatic activity. We focus on what extent does economic diplomacy influence the inflow of foreign capital to emerging countries. Then, we used data from fifty-five (55) developing economies in 2018. The composite index for diplomatic activity is constructed using principal component analysis. Further, we investigated the effect of this index on foreign capital inflows using linear regression based on the ordinary least squares method. The results indicate that the increase in the number of embassies alone does not significantly influence the evolution of diplomatic action. However, diplomacy plays a non-negligible role in attracting foreign capital. Our results demonstrate a positive and significant link between diplomacy and foreign funding, highlighting the importance of this tool for attracting investment and supporting growth in these countries. The findings of this work are going to serve both scientific and practitioners’ communities as it sheds light on the larger debate around the growing role of economic diplomacy in emerging countries in the context of globalization. Moreover, it provides a useful tool for measuring the effectiveness of foreign policies and their impact on economic expansion.

The electricity industry plays a pivotal role in a country's economic growth and development. Therefore, it is imperative to assess its performance and identify the strengths and weaknesses of its different sectors, such as production, transmission, and distribution, to enhance economic growth in diverse areas. Given the significance of the transmission sector, this research focuses on analyzing and evaluating the performance of 16 regional electricity companies in Iran from 1390 to 1398, with the aim of comprehending the impact of contextual variables on efficiency. To achieve this, the study will utilize two techniques - Data Envelopment Analysis (DEA) and Ordinary Least Squares (OLS) - to determine the efficiency score and estimate the effect of contextual variables on efficiency, respectively. In the first stage, the DEA technique is employed to calculate the technical efficiency of each company, considering their specific inputs and outputs. In the second stage, the logarithm of the efficiency scores obtained is regressed on contextual variables to establish their effect on efficiency. The residual derived from the regression is referred to as managerial ability. Finally, the companies are ranked based on their modified efficiency after removing the impact of contextual variables. Introduction The electricity industry comprises three key sectors: production, transmission, and distribution. It stands as one of the most crucial economic infrastructures in the country, exerting significant influence on industrial, agricultural, service, and other sectors. Undoubtedly, the growth of the electricity industry drives the nation's economic development and progress, contributing to the prosperity and comfort of its citizens (Tavassoli et al., 2020). Consequently, analyzing and examining the growth trajectory of each sector across different years becomes pivotal in mitigating adverse effects and fostering progress within this domain. In recent years, numerous researchers have conducted studies in this field. Some have independently evaluated each production, transmission, and distribution sector, while others have adopted a comprehensive approach by considering the integrated three-stage network structure. The research background highlights that the transmission sector has received less attention from researchers than other sectors. This is noteworthy because, following electricity production, the transmission process and energy accessibility to consumers are paramount. The absence of proper energy transfer can result in consumer dissatisfaction, financial losses, and stagnation within the competitive economic market. Therefore, identifying the strengths and weaknesses of the transmission sector's performance and comparing regional electricity transmission companies can effectively help enhance the performance level of each. One technique that has captured researchers' attention for evaluating the electricity industry's performance is the data envelopment analysis (DEA) technique. DEA is a non-parametric method used to assess the performance of homogeneous units, considering multiple inputs and outputs. It was initially introduced in 1978 by Charnes et al. The initial model was built upon the assumption of constant returns to scale. Subsequently, Banker et al. (1984) extended it by presenting a model under the assumption of returns to a variable scale. Importantly, traditional DEA models evaluate a system's performance based on specific inputs and outputs consumed and produced by the unit. However, various factors, such as contextual variables, managerial ability, and skill, can significantly influence performance and productivity. A crucial point to consider is that managerial abilities are not always overtly visible. This lack of direct visibility can impede accurate measurement. Hence, recognizing these variables among the existing indicators and assessing their influence on the performance and efficiency of each unit holds particular significance. This procedure enhances the precision of evaluation and opens avenues for delivering enhanced solutions aimed at improving the system's overall performance. Methodology The objective of this study is to analyze and evaluate the performance of Iran's regional electricity transmission sector while considering contextual variables and establishing a ranking methodology based on managerial ability. This perspective enables the identification of strengths and weaknesses in the system's structure from various angles and offers appropriate solutions for enhancement. To accomplish this, the first step involves identifying all variables within the transmission section, encompassing inputs, outputs, and contextual factors. Subsequently, we determine the technical efficiency of each regional power transmission company, taking into account specific inputs and outputs, using meta-frontier technology. The concept of meta-frontier in DEA measures the gap or distance between decision-making units (DMUs) across different boundaries. This approach assumes a unified boundary for all subgroups, enabling efficiency estimation based on a single boundary (Battese, 2004; O'Donnell, 2008). Its primary advantage lies in resolving the challenge of evaluating efficiency at varying levels. As a result, meta-frontier technology enhances the precision of evaluating regional power companies over multiple periods. After assessing the efficiency of each regional electricity transmission company, we employ the linear regression method to estimate the impact of contextual variables on efficiency, subsequently yielding a measure of managerial ability. Ultimately, we introduce a method for ranking each company based on managerial ability. The advantage of the proposed method is that, in addition to reviewing and analyzing the technical efficiency of each of the companies in the regional electricity transmission sector during different periods, it will be possible to evaluate the managerial ability of each of the companies. Such a perspective allows for companies to be compared from different dimensions. Moreover, providing a new ranking criterion based on managerial ability also facilitates a better and more accurate comparison. Results In this study, the performance of Iran's regional power companies was analyzed and evaluated from two systems and management perspectives during the years 1390-1398. Additionally, a new rating criterion based on managerial ability was presented to compare the performance of companies during 9 time periods. In this regard, firstly, the technical efficiency of 16 regional electricity companies during 9 time periods was calculated based on the inputs of the number of employees and receiving energy from neighboring companies and the outputs of sending energy to neighboring companies and delivering energy to distribution companies, using meta-frontier technology and the DEA approach. Then, the effect of contextual variables, such as line length, transformer capacity, and loss magnitude, on the efficiency score of each company was estimated using the ordinary least squares method (OLS). Furthermore, the managerial ability of each company was determined during different periods. Ultimately, a ranking criterion was established based on the results of technical efficiency after removing the effect of contextual variables. Conclusion The results of efficiency measurements over 9 time periods indicate that the highest and lowest average efficiencies were observed in the years 1390 and 1398, respectively. Furthermore, it's evident that, in general, the performance of Iran's 16 regional electricity companies exhibited a consistent upward trend from 1390 to 1398. Among the 16 evaluated companies, the Guilan regional electricity company consistently achieved the highest level of efficiency across all 9 time periods, reflecting its strong performance. Conversely, the Fars regional electricity company consistently had the lowest efficiency, indicating its weaker performance compared to other companies. When analyzing the companies' performance by year, it's noteworthy that the Tehran regional electricity company secured the highest rank in 1390, 1391, and 1394, while the Fars regional electricity company held the top spot in the remaining years. In contrast, the Sistan regional electricity company consistently displayed the lowest performance throughout all periods. The assessment of management performance over the 9 time periods indicates that the Kerman regional electricity company demonstrated superior performance from 1390 to 1393, whereas the Guilan regional electricity company excelled from 1394 to 1398, outperforming other companies. Conversely, the Gharb regional electricity company exhibited weaker performance compared to its counterparts. Additionally, the results of the regression analysis highlight a positive relationship between the efficiency score and two variables: line length and transformer capacity. Conversely, the relationship with loss magnitude is observed to be inversely correlated.

Oil is Ecuador's main export product and its financial income an important part of the General State Budget. Being Ecuador a country dependent on crude oil is the reason why this investigation is carried out. For this purpose, an investigation with a quantitative approach was used, with a descriptive and explanatory scope strengthened in the Solow model, for which an econometric model with time series data was estimated using the Ordinary Least Squares (OLS) method. The results show that oil barrel exports have a positive effect on economic growth. Likewise, it is concluded that oil imports, the price of oil, will have a greater dynamism in economic growth, since there is a strong causality with the aforementioned variables.

Exploring the spatial non-stationary relationships between land surface temperature (LST) and their driving environmental factors is important for selecting appropriate strategies to mitigate and regulate the thermal environment of watersheds. To examine the influence of various biophysical factors on LST in the Upper Awash Basin (UAB) of Ethiopia, the study used two models: Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models. As a global model, the OLS model was initially used to capture the overall relationship between LST and some biophysical factors. And then the GWR, a local spatial modeling approach, was used to examine the spatial non-stationary relationships between LST and its influencing biophysical factors. Landsat 8 OLI/TIRS image and Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) were used to generate the LST and its influencing biophysical factors. Biophysical parameters such as enhanced vegetation index (EVI), modified normalized difference water index (MNDWI), normalized difference built-up index (NDBI), normalized difference bareness index (NDBaI), albedo and elevation were used as potential driving environmental factors of LST. The result showed that the GWR model, with a higher coefficient of determination (R2) (GWR: 0.98; OLS: 0.52) and a smaller Akaike Information Criterion (AIC) (GWR: 12354; OLS: 65412), provides a better prediction than the traditional OLS model, reflecting the spatial non-stationarity relationships. The results also showed that increased LST was significantly affected by NDBI, NDBaI, and albedo, with NDBI having the greatest effect. Conversely, EVI, MNDVI, and DEM showed a negative correlation with LST, with EVI having the greatest impact. These findings highlighted the importance of considering the spatial non-stationarity relationships between LST and pertinent driving factors, and they also offer recommendations for mitigating measures to control the thermal environment of a river basin.

Abstract The detection of nonstationarities in partial duration time series (PDS) depends on several factors, including the length of the time series, the selected statistical test, and the heaviness of the tail of the distribution. Because of the more limited attention received in the literature when compared to the trend detection on block maxima variables, we perform a Monte Carlo simulation study to evaluate the performance of different approaches: Spearman's rho, Mann–Kendall, ordinary least squares (OLS), Sen's slope estimator (SEN), and the nonstationary generalized Pareto distribution fit to identify the presence of trends in PDS records characterized by different sample sizes (n), shape parameter (ξ) and degrees of nonstationarity. The results point to a power gain for all tests by increasing n and the degree of nonstationarity and by reducing ξ. The use of a nonparametric test is recommended in samples with a high positive skew. Furthermore, the use of sampling rates greater than one to increase the PDS sample size is encouraged, especially when dealing with small records. The use of SEN to estimate the magnitude of a trend is preferable over OLS due to its slightly smaller probability of occurrence of type S error when ξ is positive.

1,4-dihydropyridines (1,4-DHPs) are widely recognized as highly effective L-type calcium channel blockers with significant therapeutic benefits in the treatment of cardiovascular disorders. 1,4-DHPs can also target T-type calcium channels, making them promising drug candidates for neurological conditions. When exposed to light, all 1,4-DHPs tend to easily degrade, leading to an oxidation product derived from the aromatization of the dihydropyridine ring. Herein, the elaboration of a quantitative structure–property relationships (QSPR) model was carried out by correlating the light sensitivity of structurally different 1,4-DHPs with theoretical molecular descriptors. Photodegradation experiments were performed by exposing the drugs to a Xenon lamp following the ICH rules. The degradation was monitored by spectrophotometry, and experimental data were elaborated by Multivariate Curve Resolution (MCR) methodologies to assess the kinetic rates. The results were confirmed by the HPLC-DAD method. PaDEL-Descriptor software was used to calculate molecular descriptors and fingerprints related to the chemical structures. Seventeen of the 1875 molecular descriptors were selected and correlated to the photodegradation rate by means of the Ordinary Least Squares (OLS) algorithm. The chemometric model is useful to predict the photosensitivity of other 1,4-DHP derivatives with a very low relative error percentage of 5.03% and represents an effective tool to design new analogs characterized by higher photostability. [ABSTRACT FROM AUTHOR]

Body temperature is universally recognized as a dominant driver of biological performance. Although the critical distinction between the temperature of an organism and its surrounding habitat has long been recognized, it remains common practice to assume that trends in air temperature—collected via remote sensing or weather stations—are diagnostic of trends in animal temperature and thus of spatiotemporal patterns of physiological stress and mortality risk. Here, by analysing long-term trends recorded by biomimetic temperature sensors designed to emulate intertidal mussel temperature across the US Pacific Coast, we show that trends in maximal organismal temperature ('organismal climatologies') during aerial exposure can differ substantially from those exhibited by co-located environmental data products. Specifically, using linear regression to compare maximal organismal and environmental (air temperature) climatologies, we show that not only are the magnitudes of body and air temperature markedly different, as expected, but so are their temporal trends at both local and biogeographic scales, with some sites showing significant decadal-scale increases in organismal temperature despite reductions in air temperature, or vice versa. The idiosyncratic relationship between the spatiotemporal patterns of organismal and air temperatures suggests that environmental climatology cannot be statistically corrected to serve as an accurate proxy for organismal climatology. Finally, using quantile regression, we show that spatiotemporal trends vary across the distribution of organismal temperature, with extremes shifting in different directions and at different rates than average metrics. Overall, our results highlight the importance of quantifying changes in the entire distribution of temperature to better predict biological performance and dispel the notion that raw or 'corrected' environmental (and specially air temperature) climatologies can be used to predict organismal temperature trends. Hence, despite their widespread coverage and availability, the severe limitations of environmental climatologies suggest that their role in conservation and management policy should be carefully considered. [ABSTRACT FROM AUTHOR]

Interpolation of scalar data, in the 2D space, is an important topic in many fields of environmental and geoscience studies, and uncertainty assessment is as important as interpolation itself. An example of this is the Kriging method, which is well-established in geostatistics and enables the automatic evaluation of uncertainties by solving a linear equation, taking into account the bivariate spatial continuity of the data. The Sibson interpolation method (natural neighbour) has the important property of providing unambiguous and reproducible results. However, since it is fundamentally a deterministic method, it does not have qualitative and/or quantitative control of the uncertainty based on the sampling spatial distribution geometry. In this paper, we show the different steps leading to an analytical approach to evaluate the uncertainties of the Sibson method. After a series of tests with a synthetic data set and a surface with a known differentiable function, we show an example using the data set of accelerometric data from the M 6.5 Norcia earthquake of 30 October, 2016. [ABSTRACT FROM AUTHOR]

Music cities leverage live music as a tool for urban revitalization. Identifying influential industries in U.S. urban areas that have shaped the country music landscape can provide valuable insights into the role of the music industry in urban development. The 'big data' of country music concerts obtained from Spotify were examined to discern the relative importance of food and transportation services in explaining the spatial distribution of country music concerts from 2009 to 2019. Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) analyses show that both food and transportation services have a positive relationship with country music concerts. The analysis also reveals that the majority of country music concerts occurred in urbanized areas. Although country music has successfully spread throughout the entire country, there are distinctive regional clusters in large cities such as Nashville, Dallas, New York City, and Austin. The result also indicates the strength of GWR in improving and sustaining the explanatory power of models. The GWR was implemented to execute four models separately considering different explanatory variables and a comparative analysis of the model performance then suggested that food service appears to perform best, whilst bus service performs better than train service and air service. These findings highlight the roles of food and transportation service facilities that have made country music — a form of Southern culture visible in the urban landscape. This study encourages music cities to harness the potential of big data's power to foster vibrant industrial ecosystems in urban environments. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to evaluate poverty reduction possibilities determining the effect of average wage and unemployment on poverty, identifying the factor that is a more important predictor of poverty in Lithuania. Based on scientific literature analysis, we identify the factors that determine the phenomenon under study. We perform a statistical analysis of the collected data in the period of 2008–2021 to identify trends and patterns in the factors under consideration. The ordinary least squares method allows to estimate the impact of the selected factors on poverty. The results show that unemployment has a statistically significant positive effect (based on the sign of the estimated parameter of the regression model) on poverty, while average wage has a statistically significant negative effect on poverty. Based on the obtained results, we present possible solutions to company managers on how they could contribute to reducing poverty in the country. We conclude that company managers could apply more sustainable development goals in their businesses, reduce gender inequality, increase wages, hire unskilled workers, and help them to improve. In this way, company managers could contribute to reducing poverty in the country.

Dalam pemilihan instrumen investasi saham, para investor dapat mengacu pada saham yang tergabung dalam indeks IDX30. IDX30 adalah indeks yang mengukur kinerja harga dari 30 saham yang memiliki likuiditas tinggi dan kapitalisasi pasar besar serta didukung oleh fundamental perusahaan yang baik. Sampel dalam penelitian ini adalah perusahaan yang tercatat dalam indeks IDX30 dari tahun 2018 sampai dengan 2021 sejumlah 49 perusahaan dengan 133 observasi. Persamaan regresi linier berganda metode ordinary least squares, dan quantile regression digunakan, serta dipilih berdasarkan kriteria Hannan-Quinn. Tujuan Penelitian ini adalah untuk mengetahui pengaruh struktur modal, kebijakan dividen, dan Kepemilikan Institusi terhadap valuasi pasar perusahaan yang tergabung dalam IDX30. Metode ordinary least square tidak digunakan dalam inferensi karena p-value dari uji F sebesar 0,2517 (lebih besar dari 0,05). Model yang terpilih berdasarkan kriteria Hannan-Quinn adalah model quantile regression dengan nilai t = 0,1. Struktur modal tidak berpengaruh terhadap valuasi pasar perusahaan IDX30 dengan tingkat signifikansi 0,05. Hal ini dapat disebabkan perusahaan yang tergabung dalam IDX30 telah diseleksi sesuai dengan kriteria yang telah ditetapkan oleh otoritas bursa dari seluruh perusahaan yang tercatat di Bursa Efek Indonesia, sehingga memiliki karakteristik dalam kategori yang telah ditetapkan. Kebijakan dividen berpengaruh positif terhadap valuasi pasar perusahaan IDX30 dengan tingkat signifikansi 0,01. Hal ini sesuai dengan pola perilaku mental accounting dari para investor yang memisahkan keuntungan modal dengan pendapatan dividen. Pendapatan dividen dapat dianggap sebagai hal yang positif ketika terjadi penurunan harga saham. Kepemilikan institusional berpengaruh positif terhadap valuasi pasar perusahaan IDX30 dengan tingkat signifikansi 0,01. Pengendalian perusahaan oleh pemilik yang memiliki jumlah proporsi kepemilikan yang besar merupakan hal yang penting di luar negara yang menganut sistem hukum common law. Para investor secara eksplisit menghubungkan struktur kepemilikan yang dimiliki oleh institusi dengan valuasi yang dilakukan.

Simultaneous causality occurs when two variables mutually influence each other, creating empirical contexts where cause and effect are not clearly unidirectional. Crime and policing often appear in urban studies presenting the following characteristic: sometimes, increased police patrols can reduce criminal activities, and other times, higher crime rates can prompt law enforcement administrations to increase patrols in affected areas. This study aims to explore the relationships between patrol dynamics and crime locations using spatial regression to support public policies. We identify spatial patterns and the potential impact of crime on policing and vice versa. Data on crimes and patrol locations were collected from the database provided by the Planning and Management Secretariat and the Social Defense Secretariat of Pernambuco, Brazil. The study employed Ordinary Least Squares (OLS) to create a spatial simultaneous regression model for integrated security zones within the Brazilian geography. This approach provides a holistic visualization, enhancing our understanding and predictive capabilities regarding the intricate relationship between police presence and crime. The results report a significant relationship, with crime locations explaining police patrols (varying in geographic domain and type of crime). No statistically significant results from most geographic locations point to the inverse relation. The quantitative analysis segregated by typology presents a potential for effective public decision support by identifying the categories that most influence the patrol security time.

Understanding attendance at football stadiums holds great significance for sports economists and football clubs. Consequently, extensive research has been conducted to analyze the factors influencing football stadium attendance. However, much of this research has been confined to short-term analyses or focused solely on European countries. This study seeks to broaden the scope by examining long-term trends in the Netherlands and exploring the dynamics in Brazil. In the Netherlands, factors such as unemployment and overall interest in football emerge as significant determinants of stadium attendance. Surprisingly, hooliganism does not appear to have a notable impact, and the influence of leisure time is unclear. In the Brazilian context, stadium capacity and goal difference do not show significant effects on attendance, and the impact of the club's division is ambiguous.

In this paper, we first establish a locally converged bioinformatics dataset based on gradient sampling and design an optimal data mining control model to improve the accuracy of bioinformatics big data feature mining. The performance of the Compressive Tracking algorithm and Online Bosting algorithm is compared with the mining error as a test index. At the same time, we propose a social media information dissemination algorithm applicable to large-scale social network datasets, taking the degree value of each node as the node’s full influence and comparing and analyzing the dissemination influence of BP-IM, RAND and MC-CELF algorithms. Finally, taking public health big data as the research object, the least squares regression method was used to analyze the influence of the amount of public attention to bioinformatics scientific knowledge on their scientific literacy in different media. The results showed that there was a significant positive correlation between scientific literacy and willingness to engage in science participation behavior on social media when the amount of public attention to scientific information was β =0225, p

The Brazilian housing program, Minha Casa, Minha Vida (MCMV) (My House, My Life), was launched in 2009 to address the housing deficit issue, with the goal of distributing contracted housing units throughout the Brazilian territory. However, the program faces criticisms regarding the distribution of these units. Thus, this paper aims to analyze the distribution heterogeneity of these contracted housing units (CHUs). Two analytical approaches were employed: temporal and spatial (states). To achieve this objective, inferential methods such as Ordinary Least Squares (OLS), Spatial Autoregressive Model (SAR), and panel data regressions were employed. The findings indicate that, from a temporal perspective, there is a positive relationship between the urban housing deficit (UHD) and CHUs. However, the relationship is negative from a spatial perspective, characterizing such heterogeneity among the states. In addition, bordering regions are subject to mutual spatial influences in terms of contracted units, thereby reinforcing this heterogeneity over time. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we solve the well-known symbolic regression problem that has been intensively studied and has a wide range of applications. To solve it, we propose an efficient metaheuristic-based approach, called RILS-ROLS. RILS-ROLS is based on the following two elements: (i) iterated local search, which is the method backbone, mainly solving combinatorial and some continuous aspects of the problem; (ii) ordinary least squares method, which focuses on the continuous aspect of the search space—it efficiently determines the best—fitting coefficients of linear combinations within solution equations. In addition, we introduce a novel fitness function that combines important model quality measures: $$R^2$$ R 2 score, RMSE score, size of the model (or model complexity), and carefully designed local search, which allows systematic search in proximity to candidate solution. Experiments are conducted on the two well-known ground-truth benchmark sets from literature: Feynman and Strogatz. RILS-ROLS was compared to 14 other competitors from the literature. Our method outperformed all 14 competitors with respect to the symbolic solution rate under varying levels of noise. We observed the robustness of the method with respect to noise, as the symbolic solution rate decreases relatively slowly with increasing noise. Statistical analysis of the obtained experimental results confirmed that RILS-ROLS is a new state-of-the-art method for solving the problem of symbolic regression on datasets whose target variable is modelled as a closed-form equation with allowed operators. In addition to evaluation on known ground-truth datasets, we introduced a new randomly generated set of problem instances. The goal of this set of instances was to test the sensitivity of our method with respect to incremental equation sizes under different levels of noise. We have also proposed a parallelized extension of RILS-ROLS that has proven adequate in solving several very large instances with 1 million records and up to 15 input variables.

Introduction/Purpose: Inclusive economic growth is a concept that has taken on importance in recent years globally; however, it has scarcely been studied in Peru. This research aims to know if there exists a positive relationship between inclusive economic growth and international trade in Peru in the period 2000-2021. Methodology: Inclusive economic growth was measured using the pillars of growth and development (per capita GDP, labour productivity, employment, and life expectancy) and inclusion (income concentration and poverty) of the Inclusive Development Index (IDI) proposed by the World Economic Forum. The Ordinary Least Squares (OLS) method was used to perform the regressions. Findings: The results show that the growth of International Trade in Peru has a positive relationship with two of the indicators of inclusive economic growth analysed: real GDP per capita and Vulnerable Employment. In contrast, it has a negative relationship with Labour Productivity. There is no statistical significance for the Poverty variable. Furthermore, there is no cointegration between Peruvian International Trade and Life Expectancy at Birth or Income Distribution. Conclusions: Therefore, it is concluded that inclusive economic growth has a positive relationship with Peruvian International Trade. The study focuses on four development pillars and two inclusion pillars, so the analysis will serve to propose policies that promote inclusive economic growth in Peru. RESUMEN Introducción/Objetivo: el crecimiento económico inclusivo es un concepto de vital importancia recientemente a escala mundial; sin embargo, apenas se ha estudiado en el Perú. El objetivo es conocer la relación del crecimiento económico inclusivo y el comercio internacional del Perú en el período 2000-2021. Metodología: el crecimiento económico inclusivo se midió utilizando los pilares de crecimiento y desarrollo (PIB per cápita, productividad laboral, empleo y esperanza de vida) e inclusión (concentración del ingreso y pobreza) del índice de desarrollo inclusivo (IDI) propuesto por el Foro Económico Mundial. Para realizar las regresiones se utilizó el método de mínimos cuadrados ordinarios (MCO). Resultados: los resultados evidencian que el crecimiento del comercio internacional del Perú tiene una relación positiva con dos de los siete indicadores del crecimiento económico inclusivo analizados, que son, PIB per cápita real, y empleo vulnerable. En contraparte, posee una relación negativa con la productividad laboral. No se encuentra significancia estadística para la variable pobreza. No existe cointegración entre el comercio internacional peruano y la esperanza de vida al nacer ni la distribución de los ingresos. Conclusiones: por tanto, finalmente concluimos que el crecimiento económico inclusivo es afectado por el crecimiento del comercio internacional peruano. El estudio se enfoca en cuatro pilares de desarrollo y dos pilares de inclusión, por lo que el análisis servirá como apoyo para plantear políticas que fomenten el crecimiento económico inclusivo en el Perú.

The main goal of this article is an applied investigation of one of the types of biases caused by overconfidence, under the heading of bias in expected relative wage (or individual overplacement) and its relationship with time preferences (in the form of a proxy of people's patience) based on the Friehe & Pannenberg (2020) method. The data gathering tool of this investigation has been a two-stage questionnaire. 204 staff and faculty members of Ilam university completed the questions related to the questionnaire in two stages. Based on the ordinary least squares and semi-parametric model, the relationship between bias in wage and time preferences was examined in four stages. The results of research models in four stages showed that there is a negative and significant correlation between bias in expected relative wage (or bias in the distribution of the relative wage of people of the same age-peers) and time preferences. This means that people who are more patient, will have less bias (overplacement) on average. Examining the impact of current relative wage on bias showed that there is a positive and significant correlation between bias and current relative wage; This means that the current relative wage of individuals is not effective in reducing bias, and the higher the individual's current relative wage, the individual's bias will be greater. Also, the results showed that there is a positive and significant correlation between bias and extraversion, a negative and significant correlation between bias and neuroticism and a negative and significant correlation between bias and agreeableness.

Essential proteins are vital for maintaining life activities and play a crucial role in biological processes. Identifying essential proteins is of utmost importance as it helps in understanding the minimal requirements for cell life, discovering pathogenic genes and drug targets, diagnosing diseases, and comprehending the mechanism of biological evolution. The latest research suggests that integrating protein–protein interaction (PPI) networks and relevant biological sequence features can enhance the accuracy and robustness of essential protein identification. In this paper, a deep neural network (DNN) method was used to identify a yeast essential protein, which was named IYEPDNN. The method combines gene expression profiles, PPI networks, and orthology as input features to improve the accuracy of DNN while reducing computational complexity. To enhance the robustness of the yeast dataset, the common least squares method is used to supplement absenting data. The correctness and effectiveness of the IYEPDNN method are verified using the DIP and GAVIN databases. Our experimental results demonstrate that IYEPDNN achieves an accuracy of 84%, and it outperforms state-of-the-art methods (WDC, PeC, OGN, ETBUPPI, RWAMVL, etc.) in terms of the number of essential proteins identified. The findings of this study demonstrate that the correlation between features plays a crucial role in enhancing the accuracy of essential protein prediction. Additionally, selecting the appropriate training data can effectively address the issue of imbalanced training data in essential protein identification. [ABSTRACT FROM AUTHOR]

Existing research in differential privacy, whose applications have exploded across functional areas in the last few years, describes an intrinsic trade-off between the privacy of a dataset and its utility for analytics. Resolving this trade-off critically impacts potential applications of differential privacy to protect privacy in datasets even while enabling analytics using them. In contrast to the existing literature, this paper shows how differential privacy can be employed to precisely—not approximately—retrieve the analytics on the original dataset. We examine, conceptually and empirically, the impact of noise addition on the quality of data analytics. We show that the accuracy of analytics following noise addition increases with the privacy budget and the variance of the independent variable. Also, the accuracy of analytics following noise addition increases disproportionately with an increase in the privacy budget when the variance of the independent variable is greater. Using actual data to which we add Laplace noise, we provide evidence supporting these two predictions. We then demonstrate our central thesis that, once the privacy budget employed for differential privacy is declared and certain conditions for noise addition are satisfied, the slope parameters in the original dataset can be accurately retrieved using the estimates in the modified dataset of the variance of the independent variable and the slope parameter. Thus, differential privacy can enable robust privacy as well as precise data analytics. [ABSTRACT FROM AUTHOR]

The present study aims to verify the influence of spending related to intellectual capital and its components (human capital, customer capital and structural capital) on the performance of education in educational institutions, specifically in Federal Institutes, in the years 2017 to 2019, based on the National High School Exam (ENEM). The Ordinary Squared Methods (OLS) model was used. The results showed that intellectual capital and human capital were positively related to ENEM scores. [ABSTRACT FROM AUTHOR]

This study estimates the factors influencing micro and small enterprise financial performance in the non-farm sector of Ghana. Data was sourced from Ghana ECG/ISSER Socio-Economic Panel Survey in 2010. The study is underpinned by the resource-based view theory of firm performance. Ordinary least squares were used to determine the factors affecting financial performance and quantile regression used to analyse the variation of financial performance among enterprises. Many variables including; gender of the enterprise owner, enterprise owner's age, technical education, enterprise years of operation, enterprise location, enterprise sub-sector, number of casual, hired labour, and enterprise value of assets significantly influenced enterprise financial performance. Enterprise resources dominated industry and sector characteristics in shaping enterprise financial performance. Inter-quantile regression results indicate that gender variable was statistically significant across six inter-quantiles emphasizing the importance of gender. Enterprises in the services sub-sector were less profitable relative to those from the manufacturing, trade and restaurant sub-sectors. The results from the quantile regressions dismiss the argument that a joint set of factors influence the financial performance of enterprises, and that those factors do not vary irrespective of whether the enterprise is performing well or not. Technical education should be promoted in order to improve enterprise performance. [ABSTRACT FROM AUTHOR]

Objective. The aim of this article is to develop a program for approximate estimation of regression models specified on the basis of the Leontief production function (non-elementary regressions with two variables) and use it for modeling the unemployment rate in the Irkutsk region.Method. Estimation of non-elementary regressions is carried out using ordinary least squares method. To find approximate estimates, we used a previously developed algorithm that involves solving a very laborious computational problem.Result. Based on this algorithm, a special program was developed in the Delphi programming environment. The program provides for work in manual and automatic modes. In manual mode, according to the specified criteria, the estimates of the model parameters, the residual sum of squares, the coefficient of determination, the Student's criterion, Durbin-Watson's criterion and, for each variable, the number of the binary operation components triggerings on the sample, are determined. In automatic mode, the best estimates of non-elementary regression are determined according to the criteria: residual sum of squares, coefficient of determination, the Student’s criterion and Durbin-Watson’s criterion. At the same time, graphs of all the main characteristics are plotted depending on the key parameter of the model. With the help of the developed program, a model of the unemployment rate in the Irkutsk region was construct.Conclusion. The model construct using the developed program turned out to be better than the traditional model of multiple linear regression. The program is universal and can be used to solve specific applied problems of data analysis.

AbstractThis study estimates the factors influencing micro and small enterprise financial performance in the non-farm sector of Ghana. Data was sourced from Ghana ECG/ISSER Socio-Economic Panel Survey in 2010. The study is underpinned by the resource-based view theory of firm performance. Ordinary least squares were used to determine the factors affecting financial performance and quantile regression used to analyse the variation of financial performance among enterprises. Many variables including; gender of the enterprise owner, enterprise owner’s age, technical education, enterprise years of operation, enterprise location, enterprise sub-sector, number of casual, hired labour, and enterprise value of assets significantly influenced enterprise financial performance. Enterprise resources dominated industry and sector characteristics in shaping enterprise financial performance. Inter-quantile regression results indicate that gender variable was statistically significant across six inter-quantiles emphasizing the importance of gender. Enterprises in the services sub-sector were less profitable relative to those from the manufacturing, trade and restaurant sub-sectors. The results from the quantile regressions dismiss the argument that a joint set of factors influence the financial performance of enterprises, and that those factors do not vary irrespective of whether the enterprise is performing well or not. Technical education should be promoted in order to improve enterprise performance.

In this paper, we solve the well-known symbolic regression problem that has been intensively studied and has a wide range of applications. To solve it, we propose an efficient metaheuristic-based approach, called RILS-ROLS. RILS-ROLS is based on the following two elements: (i) iterated local search, which is the method backbone, mainly solving combinatorial and some continuous aspects of the problem; (ii) ordinary least squares method, which focuses on the continuous aspect of the search space—it efficiently determines the best—fitting coefficients of linear combinations within solution equations. In addition, we introduce a novel fitness function that combines important model quality measures: R 2 score, RMSE score, size of the model (or model complexity), and carefully designed local search, which allows systematic search in proximity to candidate solution. Experiments are conducted on the two well-known ground-truth benchmark sets from literature: Feynman and Strogatz. RILS-ROLS was compared to 14 other competitors from the literature. Our method outperformed all 14 competitors with respect to the symbolic solution rate under varying levels of noise. We observed the robustness of the method with respect to noise, as the symbolic solution rate decreases relatively slowly with increasing noise. Statistical analysis of the obtained experimental results confirmed that RILS-ROLS is a new state-of-the-art method for solving the problem of symbolic regression on datasets whose target variable is modelled as a closed-form equation with allowed operators. In addition to evaluation on known ground-truth datasets, we introduced a new randomly generated set of problem instances. The goal of this set of instances was to test the sensitivity of our method with respect to incremental equation sizes under different levels of noise. We have also proposed a parallelized extension of RILS-ROLS that has proven adequate in solving several very large instances with 1 million records and up to 15 input variables. [ABSTRACT FROM AUTHOR]

Overfitting in linear regression is broken down into two main causes. First, the formula for the estimator includes "forbidden knowledge" about training observations' residuals, and it loses this advantage when deployed out-of-sample. Second, the estimator has "specialized training" that makes it particularly capable of explaining movements in the predictors that are idiosyncratic to the training sample. An out-of-sample counterpart is introduced to the popular "leverage" measure of training observations' importance. A new method is proposed to forecast out-of-sample fit at the time of deployment, when the values for the predictors are known but the true outcome variable is not. In Monte Carlo simulations and in an empirical application using MRI brain scans, the proposed estimator performs comparably to Predicted Residual Error Sum of Squares (PRESS) for the average out-of-sample case and unlike PRESS, also performs consistently across different test samples, even those that differ substantially from the training set. [ABSTRACT FROM AUTHOR]

Cultivation of bell peppers by farmers in Pasirlangu Village, Cisarua District, West Bandung Regency, tends to reach less optimum production, indicated by the average production per crop. The purpose of this study is to evaluate three aspects: (1) factors that affect productivity, (2) allocative efficiency, and (3) income of paprika farming in Pasirlangu Village. The Ordinary Least Squares method was used to analyze factors affecting productivity, the Marginal Product Value method to assess allocative efficiency, and the R/C ratio to calculate cultivation profits. The results showed that labor and seed inputs are inputs that have a significant effect on the productivity of agricultural businesses. In the efficiency analysis, seed input and husk charcoal are not efficient inputs, so these inputs need to be added. The income analysis shows that the paprika farming business in Pasirlangu Village is still profitable even though it is not optimal, as indicated by the ratio of profits and costs, which is > 1. [ABSTRACT FROM AUTHOR]

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In this article, we investigate the effect of different energy variables on economic growth of several oil-importing EU member states. Three periods from 2000 to 2020 were investigated. Three different types of regression models were constructed via the gretl software. Namely, the OLS, FE, and SE approaches to panel data analysis were investigated. The FE approach was chosen as the final one. The results suggest the importance of the consumption of both oil and renewable energy on economic growth. Crises of certain periods also had a noteworthy effect as well. [ABSTRACT FROM AUTHOR]

The prediction of crop yield, particularly paddy production is a challenging task and researchers are familiar with forecasting the paddy yield using statistical methods, but they have struggled to do so with greater accuracy for a variety of factors. Therefore, machine learning methods such as Elastic Net, Ridge Regression, Lasso and Polynomial Regression are demonstrated to predict and forecast the wheat yield accurately for all India-level data. Assessment metrics such as coefficient of determination ($R^{2}$ ), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) are used to evaluate the performance of each developed model. Finally, while evaluating the prediction accuracy using evaluation metrics, the performance of the Polynomial Regression model is shown to be high when compared to other models that are already accessible from various research in the literature.