Linear regression and its variants have been widely used for classification problems. However, they usually predefine a strict binary label matrix which has no freedom to fit the samples. In addition, they cannot deal with complex real-world applications such as the case of face recognition where samples may not be linearly separable owing to varying poses, expressions, and illumination conditions. Therefore, in this paper, we propose the kernel negative ε dragging linear regression method for robust classification on noised (...) and nonlinear data. First, a technique called negative ε dragging is introduced for relaxing class labels and is integrated into the LR model for classification to properly treat the class margin of conventional linear regressions for obtaining robust result. Then, the data is implicitly mapped into a high dimensional kernel space by using the nonlinear mapping determined by a kernel function to make the data more linearly separable. Finally, our obtained KNDLR method is able to partially alleviate the problem of overfitting and can perform classification well for noised and deformable data. Experimental results show that the KNDLR classification algorithm obtains greater generalization performance and leads to better robust classification decision. (shrink)

With the widespread application of information technology and the continuous deepening of my country’s education informatization construction, the educational field has increasingly higher requirements for teachers’ informatization quality. The advancement of the process of education informatization depends to a large extent on the level of teachers’ own level of informatization and has gradually become an important indicator to measure the informatization of education. The main body of education and the decisive factor are teachers. The scientific and reasonable evaluation of informatization (...) education is an important content of today’s education informatization. This paper uses regression analysis to evaluate the teacher education information system and uses mathematical statistics to establish a linear regression model for the city and analyze and calculate it. Through the quantitative analysis of the evaluation index system of the education informatization level, the multiple linear regression analysis method was used to establish the linear regression mathematical model of the school education informatization level on each secondary index, and the influence of various indicators on the school education informatization level was analyzed to establish a scientific evaluation system to evaluate the literacy of teachers’ informatization education and to give suggestions for improving the level of school education informatization. The design of the evaluation system is more scientific and reasonable, the regression error is small, the results obtained are more accurate, and the impact of nonlinear errors is basically eliminated. (shrink)

Kruja is a middle-range city located in the center of Albania. The city of Kruja dates back to its existence from the V-VI century and extends to the city around the VI and IX centuries. It becomes the first capital of Albania in the XI-th century, specifically in 1190. This paper is going to deal with only two groups of buildings that are an integral part of the historical city of Kruja, the historical dwellings (XVIII century) and the socialist ones (...) (1960-1985). This paper aims to analyze through linear regressions the relation between two parameters: moisture content and temperature of the indoor walls and floors of the historic and socialist buildings in the city of Kruja. Three types of historic dwelling and three types of socialist apartments have been analyzed, relatively uniformly oriented. The measurements with specific instruments are done on the same day, for three hours. The degree of reactivity is different for both groups of buildings. The historical buildings are more sensitive to temperature rise. They respond invers to it (as the temperature rise, the level of the moisture content decreases), with a higher coefficient than socialist ones, and appear to be more efficient in reducing the level of the moisture, despite the average moisture content being higher. This paper takes into consideration also the analysis of the materials of the construction for both categories. If the level of moisture is reduced, the buildings will be improved and the quality of life indicators of the inhabitance will be improved too. (shrink)

This analysis demonstrates the application of a data duplication technique in linear regression with censored observations of the waiting time to third pregnancy ending in two outcome types, using data from Malaysia. The linear model not only confirmed the results obtained by the Cox proportional hazards model, but also identified two additional significant factors. The method provides a useful alternative when Cox proportionality assumption of the hazards is violated.

Wheat is the most imperative crop for man feeding and is planted in numerous countries under rainfed conditions in semiarid zones. It is necessary for decision-makers and governments to predict the yield of rainfed wheat before harvest and to determine the effect of the major factors on it. Different methods have been suggested for forecasting yield with various levels of accuracy. One of these approaches is the statistical regression model, which is simple and applicable for regions with scarce data (...) available. Since the weather is the most important factor affecting the production of wheat, particularly in rainfed cultivation, regression models using weather parameters are very common. However, the coefficients of these models are location based and should be determined locally. Therefore, in this research, backward multiple linear regression technique based on relative importance metrics was used to determine the most important effective weather parameters on rainfed wheat productions in Fars Province, south of Iran, during 2006–2013. The influence of each parameter in the final model was analyzed using the values of LMG relative importance metric. The result indicated that sunshine hours had the biggest LMG and, therefore, was the most effective parameter. Also, among the other considered parameters, rainy days, minimum relative humidity, and average relative humidity with LMG values of 21.97%, 21.69%, and 21.62%, respectively, had the most effects on rainfed wheat yield in the studied area. All parameters except for the sunshine hours positively affected rainfed wheat yield. The most important reason for the significance of these parameters can be the prevailing dry and semidry climate in the southern areas of Iran. The proposed model for determination of weather parameters effects on rainfed wheat could be a great guidance and aid for different stakeholders such as farmers, decision-makers, and governments. (shrink)

Economic burden of private supplementary tutoring is increasing around the world. Demand for private supplementary tutoring, contributing factors, and their effects are investigated in the study. For this purpose, data were collected through questionnaire, and multistage non-probability technique was used. Hurder model was used to find the factors affecting demand for private supplementary tutoring at the higher secondary level. Linear regression model was used to find the economic burden of family and factors affecting the demand for private tutoring. The (...) findings showed that several factors have a significant effect on the demand for private supplementary tutoring, and some of them significantly impact on the economic burden. The study recommends regulating the market of shadow education because of differentiated tuition fee in different tuition centers to cut down the burden of private tutoring for households. (shrink)

The regression model is generally utilized in several fields of study because of its applications. Regression is an extremely incredible approach; it builds up a connection between dependent and independent variables. We have addressed a powerful computational model by utilizing dengue information joined with fuzzy multiple linear regression. Information is accumulated on dengue fever through the survey. This paper is centered on the comparison of the crisp method with fuzzy multiple linear regression, and then, the utilization (...) of a fuzzy multiple regression method is explained after the comparison. We have used multiple regression and then converted the said technique into three fuzzy cases. The effectiveness of the fuzzy multiple regression model is measured by numerical computation and comparison of both techniques. 2020 Mathematics Subject Classification. Primary 30C45; 30C50; 30C80; Secondary 11B65, 47B38. (shrink)

The main aim of this paper is to analyze the electronic banking services in Kosovo’s banking net profit during the period 2013 - 2017. The paper discusses the role that these services have played in the development of the banking industry, modernization of e-commerce, and economic growth. These services provide higher security, faster and easier access by reducing transaction costs, access to more distant markets, and access to many products and services. The main paper results have shown that E-banking services (...) have the highest positive impact in banking net profit, while automated teller machine services have shown a slight positive impact. The highest positive correlation results have been seen in point-of-sale services, whereas automated teller machine services showed a negative correlation in banking net profit in Kosovo during the research period. The constant increase of demand for these services has enabled banks to increase their profits in the banking market of Kosovo. Therefore, the paper finds that commercial banks in Kosovo should increase their focus on the advancement and development of electronic banking services (particularly, E-Banking services which are very practical and can be easily used anywhere and anytime). (shrink)

Proper linear models are those in which predictor variables are given weights such that the resulting linear composite optimally predicts some criterion of interest; examples of proper linear models are standard regression analysis, discriminant function analysis, and ridge regression analysis. Research summarized in P. Meehl's book on clinical vs statistical prediction and research stimulated in part by that book indicate that when a numerical criterion variable is to be predicted from numerical predictor variables, proper linear models outperform clinical (...) intuition. Improper linear models are those in which the weights of the predictor variables are obtained by some nonoptimal method. The present article presents evidence that even such improper linear models are superior to clinical intuition when predicting a numerical criterion from numerical predictors. In fact, unit weighting is quite robust for making such predictions. The application of unit weights to decide what bullet the Denver Police Department should use is described; some technical, psychological, and ethical resistances to using linear models in making social decisions are considered; and arguments that could weaken these resistances are presented. (shrink)

Log-linear analysis is a practical tool for examining relationships, successfully applied in many fields of science. This paper discusses the topic of estimation of the chance of getting pregnant in couples that underwent ART insemination. The authors focus on finding significant interactions between variables, on the basis of which statistical models are built. With the use of results of log-linear analysis, a model predicting the chances of achieving a clinical pregnancy that contained interactions was successfully built. Moreover, it was more (...) complete than the model obtained with the use of logistic regression alone. (shrink)

The liver is the human body’s mandatory organ, but detecting liver disease at an early stage is very difficult due to the hiddenness of symptoms. Liver diseases may cause loss of energy or weakness when some irregularities in the working of the liver get visible. Cancer is one of the most common diseases of the liver and also the most fatal of all. Uncontrolled growth of harmful cells is developed inside the liver. If diagnosed late, it may cause death. Treatment (...) of liver diseases at an early stage is, therefore, an important issue as is designing a model to diagnose early disease. Firstly, an appropriate feature should be identified which plays a more significant part in the detection of liver cancer at an early stage. Therefore, it is essential to extract some essential features from thousands of unwanted features. So, these features will be mined using data mining and soft computing techniques. These techniques give optimized results that will be helpful in disease diagnosis at an early stage. In these techniques, we use feature selection methods to reduce the dataset’s feature, which include Filter, Wrapper, and Embedded methods. Different Regression algorithms are then applied to these methods individually to evaluate the result. Regression algorithms include Linear Regression, Ridge Regression, LASSO Regression, Support Vector Regression, Decision Tree Regression, Multilayer Perceptron Regression, and Random Forest Regression. Based on the accuracy and error rates generated by these Regression algorithms, we have evaluated our results. The result shows that Random Forest Regression with the Wrapper Method from all the deployed Regression techniques is the best and gives the highest R2-Score of 0.8923 and lowest MSE of 0.0618. (shrink)

We study Compositional Models based on Dirichlet Regression where, given a (vector) covariate x, one considers the response variable, y, to be a positive vector with a conditional Dirichlet distribution, y | X We introduce a new method for estimating the parameters of the Dirichlet Covariate Model given a linear model on X, and also propose a Bayesian model selection approach. We present some numerical results which suggest that our proposals are more stable and robust than traditional approaches.

The present study explored heterogeneity in the association between engaged living and problematic Internet use. This study included 641 adolescents from four junior-senior high schools of Guangzhou, China. Besides the standard linear regression analysis, mixture regression analysis was conducted to detect certain subgroups of adolescents, based on their divergent association between engaged living and PIU. Sex, age, and psychological need were further compared among the latent subgroups. The results showed that a mixture regression model could account for (...) more variance of PIU than a traditional linear regression model, and identified three subgroups based on their class-specific regression of PIU to engaged living. For the High-PIU class, lower social integration and higher absorption were associated with increased PIU; for the Medium-PIU class, only high social integration was linked with the increase of PIU. For the Low-PIU class, no relation between engaged living and PIU were found. Additionally, being male or having a lower level of satisfied psychological needs increased the link between engaged living and PIU. The results indicated a heterogeneous relationship between engaged living and PIU among adolescents, and prevention or intervention programs should be tailored specifically to subgroups with moderate or high levels of PIU and to those with lower levels of psychological needs’ satisfaction, as identified by the mixture regression model. (shrink)

A result from a standard linear model course is that the variance of the ordinary least squares (OLS) coefficient of a variable will never decrease when including additional covariates into the regression. The variance inflation factor (VIF) measures the increase of the variance. Another result from a standard linear model or experimental design course is that including additional covariates in a linear model of the outcome on the treatment indicator will never increase the variance of the OLS coefficient of (...) the treatment at least asymptotically. This technique is called the analysis of covariance (ANCOVA), which is often used to improve the efficiency of treatment effect estimation. So we have two paradoxical results: adding covariates never decreases the variance in the first result but never increases the variance in the second result. In fact, these two results are derived under different assumptions. More precisely, the VIF result conditions on the treatment indicators but the ANCOVA result averages over them. Comparing the estimators with and without adjusting for additional covariates in a completely randomized experiment, I show that the former has smaller variance averaging over the treatment indicators, and the latter has smaller variance at the cost of a larger bias conditioning on the treatment indicators. Therefore, there is no real paradox. (shrink)

The aim of this study was to examine whether the relationship between the ratio of job-related positive to negative emotions and job burnout is best described as linear or curvilinear. Participants were 89 police officers and 86 firefighters. The positivity ratio was evaluated using the Job-related Affective Wellbeing Scale. Exhaustion and disengagement, two components of job burnout, were measured using the Oldenburg Burnout Inventory. The results of regression analysis revealed that curvilinear relationships between the positivity ratio and two components (...) of job burnout appeared to better fit the data than linear relationships. The relationship between the positivity ratio and exhaustion was curvilinear with a curve point at around 2.1. A similar curvilinear relationship, but with a lower curve point, i.e., around 1.8, was observed for disengagement. It seems that beyond certain values there may be hidden costs of maintaining positive emotions at work. Also, the unequal curve points for subscales suggest that different dimensions of work-related functioning are variously prone to such costs. (shrink)

The authenticity of the company’s accounting information is an important guarantee for the effective operation of the capital market. Accounting fraud is the tampering and distortion of the company’s public disclosure information. The continuous outbreak of fraud cases has dealt a heavy blow to the confidence of investors, shaken the credit foundation of the capital market, and hindered the healthy and stable development of the capital market. Therefore, it is of great theoretical and practical significance to carry out the research (...) on the identification and governance of accounting fraud. Traditionally, accounting fraud identification is mostly based on linear thinking to build the fraud identification model. However, more and more studies show that fraud has typical nonlinear characteristics, and the multiobjective of fraud means also determines the limitations of using the linear model for identification. Considering that the traditional identification methods may have the defects of model setting error and insufficient information extraction, this paper constructs the support vector machine and logistic regression model to identify accounting fraud. The support vector machine is used to improve the learning ability and generalization ability of unknown phenomena, and the explanatory power of each variable to the whole model is identified by the logistic regression model. This paper breaks through the linear constraint hypothesis and explores the model setting form which is more suitable for the law of corporate fraud behaviour to extract the fraud identification information more fully and provide more powerful support for investors to effectively identify fraud. (shrink)

Water quality control helps in the estimation of water bodies and detects the span of pollutants and their effect on the neighboring environment. This is why the water quality of the northern part of Lake Manzala has been studied here from January to March, 2016. This study aims to model and create a program for linear and nonlinear regression of the water elements in Lake Manzala to assess and predict the water quality. Water samples have been extracted from various (...) depths, and physio-chemical properties and heavy metal concentrations have been evaluated. This study has proposed a new algorithm for predicting water quality called “Algorithm of Estimation Regression Model”. On the contrary, in renewable energy applications, statistical modeling and forecasting the solar radiation remains a significant issue with detect to reinforce power management. A new proposed method for forecasting the average Monthly Global Solar Energy in Queensland, Australia, is called, Converting Data Set into Markov Model. It was used to obtain Markov transition probability matrices for three and six states of the solar energy. The proposed forecasting method yielded accurate results with minimal error. (shrink)

This paper argues that while Chambers' method of corresponding regressions offers an intriguing way of analyzing empirical data much remains to be done to make the mathematical, and thus, the statistical meaning of the procedure clear and intuitive. Chambers' theoretical justification of the method of the claim that it can in some sense validate formal cause explanations as alternatives to efficient cause, mechanistic ones is rejected. Chambers has misattributed the mechanistic cast of most contemporary psychological explanations to linear temporality rather (...) than to necessity, and has preserved such necessity in the quality of asymmetry. The paper seeks to distinguish and clarify temporality, causality, and necessity in order to be more clear about the central theoretical problem Chambers identifies. It is further argued that the current theoretical issues facing the discipline likely cannot be resolved by methodological advances. (shrink)

We describe a unification of old and recent ideas for formulating graphical models to explain time series data, including Granger causality, semi-automated search procedures for graphical causal models, modeling of contemporaneous influences in times series, and heuristic generalized additive model corrections to linear models. We illustrate the procedures by finding a structure of exogenous variables and mediating variables among time series of remote geospatial indices of ocean surface temperatures and pressures. The analysis agrees with known exogenous drivers of the indices, (...) not assumed in the analysis. Automated search applied to the residuals after regressing each series on its lags and the lags of its Granger causes yields a graphical model of “comtemporaneous” causal relations identical with the qualitative graphical relations from the time series. A similar analysis produces reasonable results when applied to candidate climate indices obtained by a clustering method from sea surface temperature and sea level pressure data. (shrink)

We describe a unification of old and recent ideas for formulating graphical models to explain time series data, including Granger causality, semi-automated search procedures for graphical causal models, modeling of contemporaneous influences in times series, and heuristic generalized additive model corrections to linear models. We illustrate the procedures by finding a structure of exogenous variables and mediating variables among time series of remote geospatial indices of ocean surface temperatures and pressures. The analysis agrees with known exogenous drivers of the indices, (...) not assumed in the analysis. Automated search applied to the residuals after regressing each series on its lags and the lags of its Granger causes yields a graphical model of “comtemporaneous” causal relations identical with the qualitative graphical relations from the time series. A similar analysis produces reasonable results when applied to candidate climate indices obtained by a clustering method from sea surface temperature and sea level pressure data. (shrink)

The purpose of the study is to determine the impact of entrepreneurial initiative on the development of innovation activity of country enterprises. The theory of entrepreneurship by clarifying the essence of entrepreneurial initiative got further elaboration in the article. The methodical approach to the analysis and evaluation of the entrepreneurial initiative and its impact on the innovation activity of the enterprises, the results of which are the basis for further development of proposals to stimulate entrepreneurial initiative and development of their (...) innovation activity is suggested. The estimation of innovation activity of 1758 enterprises of Ukraine by calculating the integral indicator of innovation activity revealed its low level for innovation active enterprises. The method of one-dimensional linear regression has determined that an increase in the number of business structures per 10 thousand people of the country's population at a rate of 1.0 causes an increase in the indicator of innovation activity by 0.172. It is proved in the article that development of infrastructure of enterprenerial environment and creation of centers for entrepreneurship development act as an important mechanism of increasing innovation activity of enterprises of the country. (shrink)

The costs of agricultural inputs added to those of labor represent almost a third of the total cost of Brazilian sugarcane production. This study analyzes the behavior of the price per ton of sugarcane in Brazil, relating it to the main production costs of this cultivation. Twelve price indicators from January 2015 to December 2020 were evaluated. First, the data were adjusted to a multiple linear regression model to identify the significant variables on variation in the price per ton (...) of sugarcane. Then, the Monte Carlo simulation was used to measure the level of certainty of occurrence of these variables, and forecasts were obtained from the adjustment of ARIMA models. The results showed the influence of the costs of diesel oil, two agricultural pesticides, and daily laborers on the price of sugarcane, besides an increasing trend of its, providing relevant short-term projections for decision-making about investments in the agribusiness sector. (shrink)

This paper presents conceptual clustering algorithms using regression analysis. The basic idea is that given data can be classified to the class “existing” and so conceptual clustering is transformed to classification. The algorithms consist of transforming given data to the data with a class, obtaining a function by regression analysis, approximating the function by a Boolean function, and generating a concept hierarchy from the Boolean function. Regression analysis includes linear regression analysis and nonlinear regression analysis (...) by neural networks. The algorithms can perform the multiple classification and generate simple clusters. The algorithms using linear regression analysis and neural networks have been applied to real data. Results show that the algorithm using neural networks works well. (shrink)

The aim of the present study was to investigate the association between work-related moral stress, moral climate and moral sensitivity in mental health nursing. By means of the three scales Hospital Ethical Climate Survey, Moral Sensitivity Questionnaire and Work-Related Moral Stress, 49 participants’ experiences were assessed. The results of linear regression analysis indicated that moral stress was determined to a degree by the work place’s moral climate as well as by two aspects of the mental health staff’s moral sensitivity. (...) The nurses’ experience of ‘moral burden’ or ‘moral support’ increased or decreased their experience of moral stress. Their work-related moral stress was determined by the job-associated moral climate and two aspects of moral sensitivity. Our findings showed an association between three concepts: moral sensitivity, moral climate and moral stress. Despite being a small study, the findings seem relevant for future research leading to theory development and conceptual clarity. We suggest that more attention be given to methodological issues and developing designs that allow for comparative research in other disciplines, as well as in-depth knowledge of moral agency. Copyright © 2011 by SAGE Publications. (shrink)

The annual volume of ethics consultations (ECs) has been a topic of interest in the bioethics literature, in part because of its presumed relationship to quality. To better understand factors associated with EC volume, we used multiple linear regression to model the number of case consultations performed in the last year based on a national survey. We found that hospital bed size, academic affiliation, and urban/rural location were all associated with EC volume, but were not the primary drivers. Instead, (...) these variables affected EC volume through three other variables, all of which relate to ethics staffing: the percentage of case consultations performed by a single individual; whether the survey respondent’s main role at the hospital contained the word “ethics”; and especially, the number of full-time ethics program staff. We conclude that EC is an example of supply-sensitive care, and further research is needed to determine the optimal level of EC activity. (shrink)

BackgroundMoral distress (MD) is the psychological damage caused when people are forced to witness or carry out actions which go against their fundamental moral values. The main objective was to evaluate the prevalence and predictive factors associated with MD among health professionals during the pandemic and to determine its causes.MethodsA regional, observational and cross-sectional study in a sample of 566 professionals from the Public Health Service of Andalusia (68.7% female; 66.9% physicians) who completed the MMD-HP-SPA scale to determine the level (...) of MD (0-432 points). Five dimensions were used: i) Health care; ii) Therapeutic obstinacy-futility, iii) Interpersonal relations of the Healthcare Team, iv) External pressure; v) Covering up of medical malpractice.ResultsThe mean level of MD was 127.3 (SD=66.7; 95% CI 121.8-132.8), being higher in female (135 vs. 110.3; p<0.01), in nursing professionals (137.8 vs. 122; p<0.01) and in the community setting (136.2 vs. 118.3; p<0.001), with these variables showing statistical significance in the multiple linear regression model (p<0.001; r2=0.052). With similar results, the multiple logistic regression model showed being female was a higher risk factor (OR=2.27; 95% CI 1.5-3.4; p<0.001). 70% of the sources of MD belonged to the dimension "Health Care" and the cause "Having to attend to more patients than I can safely attend to" obtained the highest average value (Mean=9.8; SD=4.9).ConclusionsFemale, nursing professionals, and those from the community setting presented a higher risk of MD. The healthcare model needs to implement an ethical approach to public health issues to alleviate MD among its professionals. (shrink)

The present study examined the factors influencing job satisfactionin the context of factory workers in Malaysia. The major purpose of thestudy was to identify the main problems faced by those factory workers. Thestudy also aimed to examine the influence of demographic variables on jobsatisfaction and the relationships of family income, individual well-being, andperceptions of the working environment with overall job satisfaction amongthose factory workers. A survey was distributed among 551 factory workers working in the Klang Valley area. Results from the (...) survey indicated that thetwo main problems faced by the factory workers are, fear of the influx offoreign workers that may threaten their jobs, and the relatively low pay, takinginto account the high cost of living in the Klang Valley. Findings also indicatedthat while gender and race did not significantly influence perceptions of theworking environment and job satisfaction, job positions did. Those working assecurity and logistics staff were significantly more satisfied with their workingenvironment than those holding administrative or technical/production posts.Job satisfaction also varied significantly across job positions; those working insecurity and logistic positions were significantly more satisfied with their jobscompared to the other employees. Finally, using hierarchical linear regression, with familyincome as the control variable, the findings suggested that both individualwell-being and perceptions of the working environment emerged as positive.Both of these are significant predictors of perceived job satisfaction amongfactory workers. The implication of these findings will be discussed within theframework of organizational satisfaction and individual well-being. (shrink)

We address Northcott’s criticism of Pearson’s correlation coefficient ‘r’ in measuring causal strength by replacing Pearson’s linear regressions by nonparametric nonlinear kernel regressions. Although new proof shows that Suppes’ intuitive causality condition is neither necessary nor sufficient, we resurrect Suppes’ probabilistic causality theory by using nonlinear tools. We use asymmetric generalized partial correlation coefficients from Vinod [2014] as our third criterion in addition to two more criteria. We aggregate the three criteria into one unanimity index, UI in [-100; 100], quantifying (...) causal strengths associated with causal paths: Xi -> Xj, Xj -> Xi, and Xi Xj. (shrink)

In this paper we address the problem of short-term electric energy prediction using a time series forecasting approach applied to data generated by a Paraguayan electricity distribution provider. The dataset used in this work contains data collected over a three-year period. This is the first time that these data have been used; therefore, a preprocessing phase of the data was also performed. In particular, we propose a comparative study of various machine learning and statistical strategies with the objective of predicting (...) the electric energy consumption for a given prediction horizon, in our case seven days, using historical data. In this paper we have tested the effectiveness of the techniques with different historical window sizes. Specifically, we considered two ensemble strategies, a neural network, a deep learning technique and linear regression. Moreover, in this study, we tested whether the inclusion of meteorological data can help achieve better predictions. In particular, we considered data regarding temperature, humidity, wind speed and atmospheric pressure registered during the three-year period of data collection. The results show that, in general, the deep learning approach obtains the best results and that such results are obtained when meteorological data are also considered. Moreover, when meteorological data is used, a smaller historical window size is required to obtain precise predictions. (shrink)

In families, mothers and fathers may hold the same or different levels of theories of intelligence. This congruence and discrepancy may influence parental involvement in children’s education. The current study examined how both parents’ theories of intelligence and the direction and degree of the discrepancy of parents’ intelligence theories influence maternal and paternal involvement separately. We measured 1,694 matched pairs of parents’ theories of intelligence and educational involvement, and examined the relationships using linear regressions and polynomial regressions with response surface (...) analysis. The results showed that 1) the mother’s intelligence theory positively related to both paternal involvement and maternal involvement, but the father’s intelligence theory only positively related to paternal involvement; 2) when the parents’ theories of intelligence reached congruence, the parents’ theories of intelligence are positively related to both maternal and paternal involvement; 3) when the parents’ theories of intelligence have discrepancy, the maternal involvement is higher while the mother’s intelligence theory’s level is more incremental than father’s; and 4) when the parents’ theories of intelligence have discrepancy, more discrepancy of parents’ theories of intelligence is related to more paternal involvement. This study revealed the significance of mother’s role in education, highlighted the importance of parents’ congruence and discrepancies in beliefs, examined how parents’ beliefs impact their own behavior and their couple’s behavior. (shrink)

As Active Mining is a new concept among data mining and/or knowledge discovery in databases communities, in order to validate the effectiveness, it is important to carry out empirical studies using practical data. Based on the concept of Active User Reaction, this paper develops a causal model from liver function test data in a medical domain. To develop the model, we have set a problem to predict the values of ICG test from given observation data and experts' background knowledge. We (...) therefore employ a framework of meta-learning and structural equation modeling. In this paper meta-learning means learning about mined results from multiple data-mining techniques. Structural equation modeling enables us to describe flexible models from background knowledge. The construction of the causal model contains two phases: meta-learning and the model building. The meta-learning phase utilizes both the linear regression and the neural network as data mining techniques, then examines the predictability on the given data set. Mining models are n-folded learned from the training data set. Each of the prediction accuracy of the mining models is compared using with the testing data. On the model building phase, we use structural equation modeling to develop a causal model based on results of meta-learning and background knowledge. We again compare the accuracy of the causal model with each of the mining models. Consequently we have developed the causal model, which is comprehensible and have good predictive performance, via the meta-learning phase. Through the empirical study, we have got the conclusion that the framework of meta-learning is effective in data mining in a difficult medical domain. (shrink)

The COVID-19 pandemic has presented a global threat to physical and mental health worldwide. Research has highlighted adverse impacts of COVID-19 on wellbeing but has yet to offer insights as to how wellbeing may be protected. Inspired by developments in wellbeing science and guided by our own theoretical framework, we examined the role of various potentially protective factors in a sample of 138 participants from the United Kingdom. Protective factors included physical activity, tragic optimism, gratitude, social support, and nature connectedness. (...) Initial analysis involved the application of one-sample t-tests, which confirmed that wellbeing in the current sample was significantly lower compared to previous samples. Protective factors were observed to account for up to 50% of variance in wellbeing in a hierarchical linear regression that controlled for a range of sociostructural factors including age, gender, and subjective social status, which impact on wellbeing but lie beyond individual control. Gratitude and tragic optimism emerged as significant contributors to the model. Our results identify key psychological attributes that may be harnessed through various positive psychology strategies to mitigate the adverse impacts of hardship and suffering, consistent with an existential positive psychology of suffering. (shrink)

The purpose of this research is to identify the contribution of self control toward impulsive buying on early adolescence. Impulsive buying is an unplanned purchasing when consumer experiences a sudden, often powerful and persistent urge to buy something immediately. Self control is the feeling that someone can make decisions and take effective action to achieve desirable outcomes and avoid undesirable ones. Respondents of this research consists of 200 early adolescence with 78 male and 122 female. Those sampling obtained by non-probability (...) sampling techniques with convenience sampling method. The statistical measurement uses linear regression analysis method with SPSS version 11.5. The results implicates that self control significantly contributes to impulsive buying, refer to β score = -0.532, p< 0.05. Self control has 28.3% contribution to impulsive buying on early adolescence  . (shrink)

We present a nonparametric approach to prediction of the n-back n \in {1, 2} task as a proxy measure of mental workload using Near Infrared Spectroscopy (NIRS) data. In particular, we focus on measuring the mental workload through hemodynamic responses in the brain induced by these tasks, thereby realizing the potential that they can offer for their detection in real world scenarios (e.g., difficulty of a conversation). Our approach takes advantage of intrinsic linearity that is inherent in the components of (...) the NIRS time series to adopt a one step regression strategy. We demonstrate the correctness of our approach through its mathematical analysis. Furthermore, we study the performance of our model in an inter-subject setting in contrast with state-of-the-art techniques in the literature to show a significant improvement on prediction of these tasks (82.50% and 86.40% for female and male participants, respectively). Moreover, our empirical analysis suggest a gender difference effect on the performance of the classifiers (with male data exhibiting a higher non-linearity) along with the left-lateralized activation in both genders with higher specificity in females. (shrink)

Objective: Mindfulness disposition is associated with various psychological factors and prevent emotional distress in chronic diseases. In the present study, we analyzed the key role of mindfulness dispositions in protecting the individual against psychological distress consequent to COVID-19 social distancing and quarantining. Methods: An online survey was launched on March 13, 2020, with 6,412 responses by April 6, 2020. Socio-demographic information, exposure to the pandemic, and quarantining were assessed together with psychological distress and mindfulness disposition. Multivariate linear regression analysis (...) was performed to study the influence of predictive factors on psychological distress and quality of life in Italian responders during the early days of lockdown. Pearson correlations were calculated to study the relations between mindfulness and psychiatric symptoms. Results: Multivariate linear regression run on socio-demographics, COVID-19-related variables, and mindfulness disposition as moderators of overall psychological distress showed that mindfulness was the best predictor of psychological distress (β = -0.504; p =.000). High negative correlations were found between mindfulness disposition and overall Global Severity Index (r = -.637; p =.000), while moderate to high associations were found between mindfulness and all SCL-90 sub-scales. Discussion: Findings showed that high dispositional mindfulness enhances well-being and helps in dealing with stressful situations such as the COVID-19 pandemic. Mindfulness-based mental training could represent an effective intervention to stem the post-traumatic psychopathological beginnings and preventing the onset of chronic mental disorders. (shrink)

Job burnout, organizational commitment, and work environment continue to be important areas of research to be studied in the realm of company employment and employee retention. Job burnout is the state of physical and emotional exhaustion and perceiving one’s profession as dull or overwhelming. Meanwhile, organizational commitment refers to the company’s attitude towards the organization and their employees, encompassing loyalty, moral responsibility, and their willingness to work. And lastly, work environment provides opportunities for employees to establish connections, develop skills, and (...) maximize their full potential in performing tasks. For the current study, researchers utilized descriptive-correlational research design to investigate the influence of Work Environment towards the job burnout and organizational commitment of BPO agents in selected companies in Bulacan, Philippines. Hence, Danish Psychosocial Work Environment Questionnaire (DPQ), Maslach Burnout Inventory - General Survey (MBI-GS), and the Three-Dimensional Model of Organizational Commitment Scale (ACS, NCS, CCS) were employed to properly assess the study's objectives. Multiple Linear Regression Analysis was performed to analyze the collected data. Based on the analysis, findings show that work environment has no significant influence on the job burnout and organizational commitment of BPO Agents. Furthermore, the results of this study were thoroughly evaluated, leading to an in-depth discussion. Based on the findings, recommendations were made to benefit the stakeholders of the study and contributeto the existing body of knowledge. (shrink)

Due to the COVID-19 Pandemic, several changes have been forcibly made and observed in various fields and areas of society, one of which include the field of education; the foundation of the formation of intellect and knowledge. After two years of studying indoors and private educational institutions holding virtual classes, the time has finally come for students to be re- adjusted once more to the blended mode of learning; a combination of virtual and in-person classes. Thus, this study aimed to (...) investigate the development of students’ study habits and how these habits could potentially affect the students’ academic achievement or performance. The study utilized a descriptivecorrelational design to examine the impact of study habits to the academic performance of 137 senior high school students. The study made use of the Palsane and Sharma’s Study Habits Inventory, or also known as PSSHI, and using this instrument, the study has been able to procure sufficient results that will answer the present study’s problems. Using linear regression analysis, the present study found that there was a significance between the study habits and academic performance of the senior high school students, thus the study’s null hypothesis was eventually rejected. (shrink)

The linguistic complexity of words has largely been studied on the behavioral level and in experimental settings. Only little is known about the neural processes underlying it in uninstructed, spontaneous conversations. We built up a multimodal neurolinguistic corpus composed of synchronized audio, video, and electrocorticographic (ECoG) recordings from the fronto-temporo-parietal cortex to address this phenomenon based on uninstructed, spontaneous speech production. We performed extensive linguistic annotations of the language material and calculated word complexity using several numeric parameters. We orthogonalized the (...) parameters with the help of a linear regression model. Then, we correlated the spectral components of neural activity with the individual linguistic parameters and with the residuals of the linear regression model, and compared the results. The proportional relation between the number of consonants and vowels, which was the most informative parameter with regard to the neural representation of word complexity, showed effects in two areas: the frontal one was at the junction of the premotor cortex, the prefrontal cortex, and Brodmann area 44. The postcentral one lay directly above the lateral sulcus and comprised the ventral central sulcus, the parietal operculum and the adjacent inferior parietal cortex. Beyond the physiological findings summarized here, our methods may be useful for those interested in ways of studying neural effects related to natural language production and in surmounting the intrinsic problem of collinearity between multiple features of spontaneously spoken material. (shrink)