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Judo is a skills-led sport, which has higher requirements on the athletes’ skills, physical ability, intelligence and psychology, but the athletes’ physical characteristics are still the basis of competitive ability. In this article, 107 male judo athletes were selected as the investigation object, their body shape indexes and some physical quality indexes were measured, the independent sample multiple regression method and T-test were applied to compare and analyze the elite and non-elite athletes, the youth and adult athletes, and the standardized typical discriminant function of athletes’ sports grade was established by stepwise discriminant analysis. Thus, we have a comprehensive understanding of the physical characteristics of the men's judo athletes and provide a reliable basis for the training and selection of basic coaches.

To verify the effect of the interactive teaching model in English teaching, the author proposes the application of the multiple regression model in the interactive teaching of English classrooms. Authors, by collecting data on each relevant factor, a multiple regression system with regression simulation computing capability was designed and implemented, and the model was solved and analyzed based on Matlab software. Experimental results show that: According to the multiple regression model, the average grade is 1.3732, which shows that the interactive teaching effect is obvious. Conclusion: The application of the multiple regression model in English classroom interactive teaching is of great significance to the reform of college English teaching.

With the continuous transformation and upgrading of the economy and society and the continuous improvement of people’s income levels, the importance of green consumption in the economy is gradually emerging. In this paper, the article takes the sharing tourism app as an experimental object. First of all, using a multiple regression model to put forward relevant assumptions for research, and with the characteristics of the sharing economy, the characteristics of the sharing tourism APP variables, on the basis of which the theoretical model framework is constructed. The four variables of performance expectation, effort expectation, social factors, and facilitating factors are measured simultaneously across different dimensions. The data obtained from the questionnaire distributed to users using the shared tourism app were modeled and tested using SPSS to ensure the reasonableness of the questionnaire and the validity of the scale data. Finally, the correlation analysis of the obtained data was carried out, and the multiple regression model was used for analysis and robustness test, which concluded that the effort expectation, the willingness to use, and the social influence would affect the behavior of users using the shared tourism APP. The regression coefficients for behavioral influence are 0.216, 0.212, and 0.185, respectively. The results of the study show that in the green sharing economy, users’ behavior will be affected by multiple factors, and there will be instability.

Practice teaching is an important part of the teaching system and plays a pivotal role in improving the quality of talent training. The study outlines the mechanism by which practical teaching affects students’ employability in vocational education and proposes a practical teaching model for vocational education. We analyze the implementation of teaching practices in vocational education from the perspectives of teachers and students, using a vocational undergraduate college in GX City as the object of investigation. On this basis, relevant components of practice teaching are selected, research hypotheses about practice teaching and students’ employability enhancement are designed, and after collecting data, correlation analysis and multiple regression analysis are carried out to explore the role of practice teaching in influencing students’ employability enhancement. Students generally believe that the current practical teaching is insufficient in internship and practice (59.12%) and cannot mobilize learning interest (48.72%) and that the effect of practical teaching needs to be improved through the adjustment of teaching mode. All variables of practical teaching had a significant positive effect on the improvement of students’ employability (P

With the continuous development of China’s market economy and the gradual transformation of market competition to brand competition, the brand has an increasingly important role for enterprises. Based on the HIROSE model, this study constructs the M-HIROSE model, which is more suitable for the evaluation of the value of the National Tide brand by adjusting the brand revenue, brand expansion power, and period. To explore the relationship between the loyalty of the new consumer class and national brands, multiple regression is used for analysis. By empirically analyzing the basic financial data of China’s five national tide brand operators in the past three years, the results show that the ranking of the final brand value of the enterprises and the ranking order of the loyalty drivers are consistent, and the loyalty drivers basically determine the final brand value of the enterprises. The direct effect of new consumer class loyalty on the National Tide brand is significant (R2=0.235, p

Innovation and entrepreneurship are essential for higher vocational students to escape low-end employment and are vital for vocational education to provide diverse products and services, fulfilling the people’s demand for a better life. This study builds on the theory of innovation and entrepreneurship education, integrating information technology to devise the “Four-Drive-One-Guide” model for vocational colleges. Utilizing Province A as a case study, a survey was conducted to gather data on the effectiveness of this education model, analyzed using ANOVA, Pearson’s correlation coefficient, and multiple linear regression. Furthermore, the operational efficiency of innovation and entrepreneurship education across 30 Chinese regions was assessed using the CCR model within the DEA framework. Findings reveal that the “Four-Drive-One-Guide” model significantly boosts students’ innovative and entrepreneurial capabilities, with an average rating of 4.06. The most influential factor in its effectiveness is students’ attitudes, with a coefficient of 0.238. Moreover, vocational colleges in the eastern region exhibit the highest comprehensive efficiency at 0.875. The study suggests that integrating industry teaching and optimizing the curriculum can further enhance students’ innovative and entrepreneurial thinking.

With the development of the information age, international teaching cooperation and exchanges have become increasingly frequent, and educational sovereignty and cultural security in higher education international exchanges have gradually become the focus of attention. This paper constructs a multiple regression model of educational sovereignty related to international exchanges, and designs a cultural security risk assessment model in international exchanges by combining fault tree and Bayesian network to prevent cultural invasion. Empirical Analysis is carried out on this basis to explore the factors and preventive measures affecting educational sovereignty and cultural security in higher education international exchanges. In the regression results, the standard regression coefficient of national comprehensive strength is the highest at 0.272, in which the significance of intellectual property rights, educational legislation, educational administration, state authority and national total strength are all less than 0.001. The accuracy rate is more than 90% for identifying cultural security risks. In the research of this paper, the cultural security risk problems can be effectively identified, and the cultural security problems in the international exchange of university education can be improved with practical guarantee.

In the context of the “Internet Plus” era, the reform of children’s literature curriculum content is fundamental. This study explores the impact of such reform on the quality of children’s psychological aesthetic education, aiming to provide theoretical guidance for future educational practice. The research method uses quantitative and qualitative methods, constructs word lists and word lists for teaching children’s literature courses by analyzing the texts in language textbooks at the compulsory stage, and explores the data using multiple regression models. The results show that the reform of teaching children’s literature significantly improves students’ psycho-aesthetic qualities, among which the improvement of teaching methods has the most significant impact on psycho-aesthetic education, with a path coefficient of 0.2906 (P

This paper focuses on how the cultural industry affects economic growth through the theory and model of cultural economics. Based on the development of the economic growth model endogenized by the cultural industry, it explains how the arts and entertainment sector and the digital economy play a crucial role in the long-term economic growth after examining the logic and mechanism of the cultural industry impacting economic growth through induction and theoretical derivation. To create a quantitative analytical model of the digital economy and cultural industry and investigate the relationship between them, multiple regression analysis is employed. The impact of individual aspects of each condition variable on the economic performance composition of the cultural industry is evaluated using group effects. The results of the NCA test demonstrate that the digital economy, the scale of the cultural industry, public participation, and resources are all necessary for the sector to perform economically. The effect sizes of these factors are (0.525, 0.540), (0.362, 0.347), (0.315, 0.363), and (0.271, 0.188). The digital economy’s regression coefficients, which are usually positive, are0.470, 0.461, 0.428, 0.422, 0.383, and 0.381, according to the regression study. According to this study, the cultural sector’s growth at a high standard is positively impacted by the digital economy.

The stress distribution function in the surface layer is created as a result of using stress measurements on the surfaces of C45 steel samples after shot peening. Stresses were measured by X-ray diffraction with the use of the PSF-3M device from the Rigaku Company. For measuring residual stresses, subsequent layers of the top surface of the material were used as a basis, and these were obtained through electrochemical etching. The test results i.e. distance into the material, sample hardness, shot type, stress) were entered into the stepwise multiple regression program. A record of residual stresses was obtained in the form of the second-degree regression function of three independent variables with interactions. The obtained analytical form of the residual stress function was used in the FUNVAL3.EXE program to calculate the tabular values of stresses permeating into the material. For the analytical description of the stress distribution, the REGPOLY.EXE regression program was used, which creates a polynomial functional form of the residual stress distribution. The plot form of the residual stress distribution was obtained using the EXCEL Microsoft Office 2000 program. [ABSTRACT FROM AUTHOR]

In order to reduce the default rate of corporate bond market, the author proposes to use digital signal processing and multiple regression analysis to study the prediction system of financial distressed companies. First, design the research method, Logistic regression model is the most commonly used multivariate statistical method when modeling binary dependent variables, it can solve the problem of nonlinear classification, it has no specific requirements for the distribution of variables, and the accuracy of judgment is high. The author selects 32 financial ratios from the perspectives of solvency, operating ability, profitability, development ability, per share index, and risk level. Taking special treatment (ST) due to abnormal financial status as a sign of financial distress in listed companies, when selecting samples, the matching principle is adopted to select non-ST companies as matching samples. Two methods of logistic regression and support vector machine are used for empirical testing, and both in-sample testing and out-of-sample prediction are performed. The results show that when using the logistic regression method, the propensity to default indicator (TTD) reflected in the text content, it can indeed improve the out-of-sample prediction accuracy of the financial distress prediction model, and it is consistent with the in-sample test, this is mainly reflected in the reduction of the first type of error, that is, the probability of misjudging a financially distressed company as a normal company. Changes in the proportions have little effect on the relative importance of financial ratio variables when modeling with support vector machines, the propensity to default indicator (TTD) entered the top ten important variables in both ratios, and ranked fourth among all indicators when the ratio was 1:2, importance has increased significantly. From this it can be seen that, when using support vector machine to build a financial distress prediction model, the propensity to default indicator (TTD) has played an important role. In the case of using the support vector machine method, adding the default tendency indicator (TTD) reflected by the text information can also improve the accuracy of the financial distress prediction model.

Groundwater modelling needs a large number of piezometer well data, which is unfortunately not always available as earlier registered data series are not always continuous. The reconstruction of data series with gaps was previously examined on a small riverside pilot area with the help of multiple regression, this method has now been tested at a 50-times larger environment. The results show that the reconstruction of the data series with this method works with average monthly groundwater levels and that applying multiple regression with the independent variables being one of the wells and the river optimizes the accuracy of the calculated data series, even if the relationships between the river and the wells are weak. The effect of the multiple regression on the accuracy is greater if the data series is sparser.

In Costa Rica, a land tax valuation model provided by the Technical Standardization Body (ONT) of the Ministry of Finance is used. This model considers homogeneous areas that include properties with similar characteristics and review factors that have not been recently updated. The objective of this article is to determine the effect of the distance to the train station and mobile coverage on the value of land in Oreamuno, Costa Rica, as a proposal to be included in the mass valuation model of the ONT. A multiple regression econometric model was used to establish the functional relationship between the value of the property and its characteristics, through the Ordinary Least Squares (OLS) method, using 10 variables applied to 200 references initially. The results allow demonstrating that other variables such as “Distance to the train station” and “Mobile coverage” are representative for determining the value of properties, in addition to those commonly used by the ONT model. The research allows concluding that the land valuation models should be updated periodically and should include variables related to improvements in technology and access roads, since these increase the market value of properties and improve the social development of localities in study.

In the development of urban construction, regional economic differences have always been the focus of the government, society and academia. As an eastern coastal province in China, although Jiangsu has a relatively high level of economic development from an overall point of view, there is still a serious problem of regional economic disparity within it. Therefore, based on the development of local economic construction and the understanding of the phenomenon of industrial transfer, this paper uses a multiple regression model to study the regional economic coordination and thus puts forward appropriate solutions.

Soil bulk density is a required variable for quantifying stocks of elements in soils and is therefore instrumental for the evaluation of land-use related climate change mitigation measures. Our motivation was to derive a set of pedotransfer functions for soil bulk densities usable to accommodate different levels of data availabilities. We derived sets of linear equations for bulk density that are appropriate for different forms of land-use. After introducing uncertainty factors for measured parameters, we ran the linear models repeatedly in a Monte Carlo simulation in order to test the impact of inaccuracy. The reliability of the models was evaluated by a cross-validation. The single best predictor of soil bulk density is the content of soil organic carbon, yielding estimates with an adjusted R2 of approximately 0.5. A slight improvement of the estimate is possible when additionally, soil texture and soil depth are known. Residual analysis advocated the derivation of land-use specific models. Using transformed variables and assessing land-use specific pedotransfer functions, the determination coefficient (adjusted R2) of the multiple linear models ranged from 0.43 in cropland up to 0.65 for grassland soils. Compared to pedotransfer function, from the literature, the performance of the linear modes were similar but more accurate. Taking into account the likely inaccuracies when measuring soil organic carbon, the soil bulk density can be estimated with an accuracy of +/− 9 to 25% depending on land-use. We recommend measuring soil bulk density by standardized sampling of undisturbed soil cores, followed by post-processing of the samples in the lab by internationally harmonized protocols. Our pedotransfer functions are accurately and transparently presented, and derived from well-documented and high-quality soil data sets. We therefore consider them particularly useful in Austria, where the measured values for soil bulk densities are not available.

To develop an advanced control of thermal energy supply for domestic heating, a number of new challenges need to be solved, such as the emerging need to plan operation in accordance with an energy market-based environment. However, to move towards this goal, it is necessary to develop forecasting tools for short- and long-term planning, taking into account data about the operation of existing heating systems. The paper considers the real operational parameters of five different heating networks in Latvia over a period of five years. The application of regression analysis for heating load dependency on ambient temperature results in the formulation of normalized slope for the regression curves of the studied systems. The value of this parameter, the normalized slope, allows describing the performance of particular heating systems. Moreover, a heat load forecasting approach is presented by an application of multiple regression methods. This short-term (day-ahead) forecasting tool is tested on data from a relatively small district heating system with an average load of 20 MW at ambient temperature of 0 °C. The deviations of the actual heat load demand from the one forecasted with various training data set sizes and polynomial orders are evaluated for two testing periods in January of 2018. Forecast accuracy is assessed by two parameters – mean absolute percentage error and normalized mean bias error. [ABSTRACT FROM AUTHOR]

The paper deals with the possibilities of company goodwill valuation and verification. The value of company goodwill is still an actual issue for the scientific community. Goodwill as an economic phenomenon has attracted the attention of economic experts since the nineteenth century. Nowadays, there are many approaches to goodwill valuation. However, its identification and quantification are still a challenge. The paper aim is to identify significant sources of company goodwill creation and their verification on the sample of 2 European countries with the similar business environment, political stability and regulatory platform - Slovak and Czech companies. The sample for the identification of significant sources of company goodwill creation consists of the financial statements of Slovak companies in 2015. The sample for data verification consists of the financial statements of Slovak and Czech companies in 2016. The paper identifies the determinants of goodwill creation by multiple regression analysis. The paper also verifies the total explanatory power of these determinants by matrixes of changes. Volatility and deviation of the results are captured by descriptive statistical methods. The paper’s results point to a necessity to identify the key determinants of goodwill creation. They bring the construction of an econometric model for company goodwill valuation. It could be used to compute the value of company goodwill of the individual companies in the Slovak economic conditions.

In the paper, the results of fatigue investigations on smooth and flame yarns are presented. The investigations were performed for yarns of the following types 25, 30, 40 and 50 tex, i.e. for thin yarns as well as for relatively thick ones made in spinning mills. An assessment of fatigue life was made based upon statistically analysed Wöhler curves. Aiming for a more complex presentation of changes over time taking place due to variable loading, we performed an assessment of the simultaneous influence of linear mass changes for smooth and flame cotton yarns as well as coefficients of the real loading cycle on the fatigue life of adequate yarns (represented by the number of loading/fatigue cycles). A statistical approach to the identification of characteristics of fatigue life was utilised. Experimental data were approximated via linear-square polynomials. Additionally, factor analysis was performed, which has not been considered till now, in relation to the fatigue phenomena modelling. Due to the wide and comprehensive plan of experiments as well as the number of trails and specimens utilised, such investigations are rarely described in references. Within the framework of the experiment program, a consecutive series of planned tests: 4 x 8 based upon 32 variants for 20 tests were taken into consideration for smooth yarns of every variant as well as an adequate number of tests performed separately for cotton flame yarns. This allowed us to make versatile statistical analyses and reliable identification of needed quantities and parameters. Utilisation of a pre-designed (planned) experiment allows for assessment of the average behaviour of a product in a real technological process, while methods considering characteristics in dependence on one variable are too simple for showing the ongoing changes. They could not fully represent the behaviour of an artifact/product in service conditions. [ABSTRACT FROM AUTHOR]

Though smallholder goat enterprise has been a major source of livelihood in most African communities for ages, yet little efforts exist to explore its full potential through commercialization. While much has been done to improve agricultural production in the small-scale sector, little is known and documented about the current state of goat production and marketing in most parts of Nigeria. Using proportionate sampling, data were collected from 160 smallholder goat producers and 220 consumers making a total of 380 respondents for the study. The data were analysed using descriptive statistics, budgetary analyses, multiple regression and Likert Scale Technique. The study showed that smallholder goat enterprise was female-dominated with majority aged between 51 - 60 years. The average cost, average revenue and gross margin per production season were ₦12,471.78, ₦16,755.51 and ₦4,904.61, respectively. The farmers realized ₦430.20, ₦450.80, ₦252.00 and ₦363.50 on a kilogram of buck, doe, male and female kids, respectively. Smallholder goat enterprise was profitable in the study area because the Benefit-Cost Ratio (BCR) showed that every ₦100 invested per goat yielded ₦34.00 more and above the money invested. The multiple regression model analysis showed that management practice (P < 0.01), gender (P < 0.1), age of cost of medication (P < 0.01) and flock size (P < 0.05) significantly influence the profitability of smallholder goat production in the study area. The result of Likert’s scale indicated that 66.8 % of the consumers preferred goat meat to other meats because of its availability (56.8 %) and taste (22.3 %). Therefore, it is recommended that efforts should be put into commercializing goat enterprise in the tropics using the intensive system.

The research process aimed at building regression models, which helps to valuate residential real estate, is presented in the following article. Two widely used computational tools i.e. the classical multiple regression and regression models of artificial neural networks were used in order to build models. An attempt to define the utilitarian usefulness of the above-mentioned tools and comparative analysis of them is the aim of the conducted research. Data used for conducting analyses refers to the secondary transactional residential real estate market.

This study examined 36 accessions of melon grown originally in dry and warm zones managed in the gene bank in the Institute of Seed and Plant Improvement, Karaj. To characterize and compare results of the field experiment statistical procedures, hierarchical cluster analysis, principal component analysis (PCA), and multiple linear regressions were applied. The results indicated a high degree of variability for single plant yield, fruit weight, fruit length, and cavity diameter. In addition, the variables of fruit size displayed a high differentiating power. Positive and negative correlation coefficients were identified among the studied traits as well. The highest significant positive correlation was found between the plant yield and fruit weight (0.88). Also, the highest significant negative correlation was observed between relative water content and canopy temperature (−0.58). Melon accessions were classified into three main clusters, indicating that the characterized melon collection has a high potential for particular breeding goals. The usefulness of the identified correlations among traits is valuable for the potential use in breeding projects directed for fruit size and yielding in dry condition. Multiple linear regressions were done and R2 (Coefficient of determination) and RMSE (Root Mean Squared Error) were 0.97 and 229, respectively.

The career of business students is nowadays definitely influenced by their involvement in different internships and volunteering activities. The internship programmes help them understand the context of business organisations and decide what field of activity is most suitable for their professional life. However, sometimes internship programmes are not very well organised and influence in a negative manner students′ final perception of a certain domain. Our research identified and analysed the main factors that might influence the quality of an internship programme from students′ perspective. The quantitative analysis relies on a questionnaire based survey among over 450 students from one of the most prestigious universities in Romania in the field of economics and business administration. By running factor analysis, we identified five factors which mainly determine the quality of internships: Job arrangements, Mentorship and employability benefits, Learning content, Academic supervision, Bureaucracy and accessibility. Subsequently we measured through multiple regression the way the identified factors influence the quality of the internships. We consider that the results of the study are relevant not only for academics but also for students and business organisations that have the power and instruments to improve internship programmes and the entire experience for all stakeholders involved.

The paper deals with the possibilities of company goodwill valuation and verification. The value of company goodwill is still an actual issue for the scientific community. Goodwill as an economic phenomenon has attracted the attention of economic experts since the nineteenth century. Nowadays, there are many approaches to goodwill valuation. However, its identification and quantification are still a challenge. The paper aim is to identify significant sources of company goodwill creation and their verification on the sample of 2 European countries with the similar business environment, political stability and regulatory platform - Slovak and Czech companies. The sample for the identification of significant sources of company goodwill creation consists of the financial statements of Slovak companies in 2015. The sample for data verification consists of the financial statements of Slovak and Czech companies in 2016. The paper identifies the determinants of goodwill creation by multiple regression analysis. The paper also verifies the total explanatory power of these determinants by matrixes of changes. Volatility and deviation of the results are captured by descriptive statistical methods. The paper's results point to a necessity to identify the key determinants of goodwill creation. They bring the construction of an econometric model for company goodwill valuation. It could be used to compute the value of company goodwill of the individual companies in the Slovak economic conditions. [ABSTRACT FROM AUTHOR]

The paper presents mathematical relationships that allow us to forecast the estimated main engine power of new container ships, based on data concerning vessels built in 2005-2015. The presented approximations allow us to estimate the engine power based on the length between perpendiculars and the number of containers the ship will carry. The approximations were developed using simple linear regression and multivariate linear regression analysis. The presented relations have practical application for estimation of container ship engine power needed in preliminary parametric design of the ship. It follows from the above that the use of multiple linear regression to predict the main engine power of a container ship brings more accurate solutions than simple linear regression.

The regularities and relations between real estate prices and the factors that shape them may be presented in the form of statistical models, thanks to which the diagnosis and prediction of prices is possible. A formal description of empirical observation presented in the form of regressive models also offers a possibility for creating certain phenomena in a virtual dimension. Market phenomena cannot be fully described with the use of determinist models, which clarify only a part of price variation. The predicted price is, in this situation, a special case of implementing a random function. Assuming that other implementations are also possible, regressive models may constitute a basis for simulation, which results in the procurement of a future image of the market.

In the late of 1990’s the central bank independence concept has emerged in the economic literature. Beyond the Great Inflation of the 1960’s and 1980’s, an independent central bank in compliance with the free lunch hypothesis has become lately the panacea of modern monetary policy. Moreover, central bank transparency and central bank accountability and responsibility had become the surgery tools for tracking a good record of central bank governance around the world. In this article, we provide a qualitative and quantitative overview regarding modeling central bank governance and inflation having at background the historical evolution of the main macroeconomic indicators in two Central and Eastern European Countries: Romania and Czech Republic. These two countries register several common elements regarding central banking and monetary policy, but register also some distinguished particularities regarding the economical, political and social aspects starting from 1990’s and until present.

In this paper, based on the analysis of official FAOSTAT and EUROSTAT data on poultry meat for 38 European countries for years 2007 and 2009, two hypotheses were examined. Firstly, considering four clustering variables on poultry meat, i.e. production, export and import in kg/capita, as well as the producer price in US $/t, using descriptive exploratory and cluster analysis, the hypothesis that the clusters of countries may be recognized was confirmed. As a result six clusters of similar countries were distinguished. Secondly, based on multiple regression analysis, this paper proofs that there exists the statistically significant relationship of poultry meat production on export and import of that kind of meat, all measured in kg/capita. There is also a high correlation between production, as a dependent, and each of two independent variables.

The paper represents the research of a road landscape from hedonomic point of view, which is a necessary and important part of landscape sustainability. The proposed research method consists of preparative inquiry of the roadscape using Kansei engineering and SD (semantic differential) technique, and the main inquiry using multiple linear regression analysis. The application of the method to the landscape of Lithuanian highways let us identify the factors which influence the hedonomics of a road landscape. [ABSTRACT FROM AUTHOR]

The verification of models based on multiple regress servants for the assessment of the ring spinning process of classic as well as compact cotton yarns was carried out. The analysis of results obtained showed that the statistical models proposed were very useful in making a qualitative and productive comparison of the yarns considered. Decreasing the metric coefficient of the twist - αm and percentage noils - pw during the production of compact yarn makes possible the obtainment of significant productive effects and the lowering of the costs of producing the yarn without an excessive worsening of quality. [ABSTRACT FROM AUTHOR]

The analysis of the parameters of the spinning process was conducted of the quality of the yarn and the efficiency of the production, and it was confirmed that the percentage of noils and the metric coefficient of the twist were important factors. The analysis of the simultaneous influence of both factors on the chosen properties of classic cotton yarns, and of the nominal linear density 20 tex of compact yarn, was conducted by means of statistical models based on multiple regression, after conducting two total experiments of the type 5 x 5. The verification of proposed models will be presented in the second part of the article. [ABSTRACT FROM AUTHOR]