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)

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)

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.

The use of linear regression is ubiquitous across the social and behavioral sciences, and yet researchers rarely hold that the variables in their target systems are in fact linearly related. This raises the question of how to interpret linear regression coefficients when there is ‘functional misspecification’ and the target system exhibits nonlinearity. Here, recent methodological discussions among practitioners have mirrored philosophical debates over scientific realism. In this paper, I frame the prevailing views in terms of their (...) stance on the scientific realism debate. I then present a novel realist interpretation of linear regression—the ‘secant interpretation’—based on a property I derive about the manner in which linear regression coefficients represent properties of non-linear systems of interest. (shrink)

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)

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)

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 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 (...) class='Hi'>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)

Researchers in the social and behavioral sciences often have clear expectations about the order/direction of the parameters in their statistical model. For example, a researcher might expect that regression coefficient β 1 is larger than β 2 and β 3. The corresponding hypothesis is H : β 1 > {β 2, β 3 } and this is known as an (order) constrained hypothesis. A major advantage of testing such a hypothesis is that power can be gained and inherently a (...) smaller sample size is needed. This article discusses this gain in sample size reduction, when an increasing number of constraints is included into the hypothesis. The main goal is to present sample-size tables for constrained hypotheses. A sample-size table contains the necessary sample-size at a pre-specified power (say, 0.80) for an increasing number of constraints. To obtain sample-size tables, two Monte Carlo simulations were performed, one for ANOVA and one for multiple regression. Three results are salient. First, in an ANOVA the needed sample-size decreases with 30–50% when complete ordering of the parameters is taken into account. Second, small deviations from the imposed order have only a minor impact on the power. Third, at the maximum number of constraints, the linear regression results are comparable with the ANOVA results. However, in the case of fewer constraints, ordering the parameters (e.g., β 1 > β 2 ) results in a higher power than assigning a positive or a negative sign to the parameters (e.g., β 1 > 0). (shrink)

Growth modelling is of interest to scientists in various disciplines. In our article, we will collect 17 models designed for growth modelling, appraise these models and contribute to the discussion of their applicability. The merit of the paper lies in studying the convergence properties of nonlinear regression in selected models. Our studies will be performed mainly concerning the quality of the obtained estimates, which are closely related to the intrinsic curvature of the model according to Bates and Watts. This (...) curvature determines the size of the linearization domains. Only if the initial solution is in this domain, then the convergence of the estimate in nonlinear regression is guaranteed. The primary goal is to design a methodology for selecting a growth model. We will demonstrate fruitfulness of our methodology on the weight measurements of 10 calves under 25 months of age from cowsheds in the village Záluží in the Czech Republic. Estimated parameters of growth curves, sizes of linearization domain, calculated residues and coefficients of determination indices will be the subject of discussion. (shrink)

As one of the low-carbon products, remanufactured products are being paid more attention in more and more countries. But the low willingness of Chinese consumers to pay for them makes it difficult for remanufactured products companies to move forward in the Chinese market. This study explores the factors that affect consumers’ willingness to pay for remanufactured goods, through the hierarchical linear regression method, based on questionnaires. The results show that demographic variables (age, education, occupation, and income), individual subjective (...) variables (environmental awareness, secondhand preferences, and Chinese quality trust) and product perception variables (MP4 quality perceived risk, face risk, and product impact on the environment) have a significant impact on the willingness to pay for remanufactured goods. (shrink)

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)

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)

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 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)

Smooth function is the essence of the ε-smooth support vector regression (ε-SSVR) which is an unconstrained convex quadratic programming. In this paper, Bézier function as a new smooth function is proposed to replace the ε-insensitive loss function of ε-SSVR with tolerating a smaller error in fitting given data sets linearly and nonlinearly. The ε-Bézier function smooth support vector regression (ε-BSSVR) model is presented. Theoretical analyses show that the derived smooth function has improved approximation precision compared with other smooth (...) approximate functions. The Newton-Armijo algorithm is applied to solve the new model. Numerical results and comparisons demonstrate that the proposed method has the better regression performance and learning efficiency compared with other given models. (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 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)

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)

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 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)

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 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)

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)

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)

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)

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)

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)

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)

Facial emotion recognition (FER) deficits interfere with interpretation of social situations and selection of appropriate responses. Attention-Deficit/Hyperactivity Disorder (ADHD) symptoms are independently associated with social difficulties and might exacerbate the influence of deficient FER, because children with ADHD symptoms have fewer compensatory resources in social situations when they misinterpret emotions. Very few studies have tested this hypothesis in a community context, where child ADHD symptoms vary on a continuum. The current study extended this work by utilising a community sample (N (...) = 87) of boys and girls in middle childhood (M = 7.83 years) and testing for moderation of FER effects separately by ADHD symptom type (ADHD-I = inattentive, H = hyperactive/impulsive, C = combined) using linear regression. While lower FER was associated with more social problems, this relationship was qualified by the presence of ADHD symptoms. Specifically, only children with relatively high ADHD symptoms in our community sample showed this inverse relationship, which was clearest among children with elevated ADHD-C or ADHD-I symptoms. No gender differences were observed. These results support our primary hypothesis, extend prior findings to boys and girls in the community, and have implications for understanding how ADHD symptoms and FER influence youth social deficits. (shrink)

Much of scientific inference involves fitting numerical data with a curve, or functional relation. The received view is that the fittest curve is the curve which best balances the conflicting demands of simplicity and accuracy, where simplicity is measured by the number ofparameters in the curve. The problem with this view is that there is no commonly accepted justification for desiring simplicity. This paper presents a measure of the stability of equations. It is argued that the fittest curve is the (...) curve which best balances stability and accuracy. The received view is defended with a proof that simplicity corresponds to stability, for linear regression equations. 1This paper is based on part of my doctoral dissertation. My thanks go to my thesis supervisor Professor Alasdair Urquhart for his encouragement, constructive criticism, and for directing me to several relevant articles: to my advisor Professor Ian Hacking for reminding me to concentrate on results that might have some application in the real world; and to my friend Wendy Brandts for sharing her ideas on a closely related problem. My thanks also to an anonymous referee of The British Journal of the Philosophy of Science for several helpful comments, to my friends and family for unfailing support, and to the Social Sciences and Humanities Research Council (awards 452-86-5885 and 453-87-0513) and the University of Toronto for financial assistance. (shrink)

Although the study of moral judgment has gained in popularity in the field of psychology in recent years, little research has focused on confidentiality dilemmas in the context of moral judgment. Yet clinicians regularly face such dilemmas, which often arise in emotionally charged contexts. This study assesses the role of emotions in responses to these confidentiality dilemmas. A total of 186 participants studying psychology at different universities in Quebec were randomly assigned to one of three groups in an experimental design (...) (dramatic audio modality, neutral audio modality, text modality) and responded to confidentiality dilemmas by choosing between protecting or lifting confidentiality. Also taken into consideration were participants’ positioning on a continuum ranging from deontology to utilitarianism, based on their response to sacrificial dilemmas. The hierarchical multiple linear regression model used indicates a significant contribution from responses to sacrificial dilemmas, perceived emotional intensity of confidentiality dilemmas and training obtained in the field of ethics and deontology. The analysis further reveals the moderating effect of exposure to the experimental condition on the relationship between the tendency towards utilitarianism and the propensity to waive confidentiality in a psychotherapeutic relationship. These results have practical implications for psychology curricula, where situation scenarios with ecological validity could enhance ethics training. (shrink)

The present study investigates the relations between L2-English proficiency and L1-Turkish lexical property evaluations. We asked whether L2 proficiency affects lexical properties, including imageability and concreteness ratings of 600 Turkish words selected from the Word Frequency Dictionary of Written Turkish. Seventy-two participants provided ratings of concreteness and imageability for 600 words on a 7-point scale. In order to assess their L2 proficiency, we administered Peabody Picture Vocabulary Test-IV. We divided categories into two subcategories as high and low for the frequency, (...) concreteness, imageability, and age of acquisition. The relationship between these subcategories and imageability-concreteness was examined by mixed effects linear regression analyses. We found that L2 proficiency and imageability ratings were positively correlated and specifically, this positive association was evident for low-frequency words and later acquired words. Results are in line with the interaction of bilingual representation under the dual-coding theory which suggests that bilinguals develop an interconnected imaginal representation for two languages as opposed to separate verbal representations. As L2 proficiency increased, the imageability also increased. These findings have implications for literature investigating the relationship between L2 proficiency and linguistic outcomes. Additionally, findings point to the importance of considering the L2 proficiency of participants when lexical tasks that involve cue words or word lists are used. (shrink)

Loneliness has been described as endemic among young people. Such feelings of social isolation ‘even in a crowd’ are likely linked to adverse early life experiences that serve to diminish perceptions of social support and intensify negative social interactions. It was suggested in the present series of survey studies that childhood abuse, which compromises a child’s sense of safety in relationships, may affect social processes that contribute to loneliness in young adulthood. Study 1 assessed different adverse childhood and adult experiences (...) in relation to loneliness among young adults. Linear regression analyses indicated that childhood abuse was uniquely associated with greater loneliness, and this relationship was partially mediated by the perceived availability of social support. Study 2 assessed different forms of childhood abuse and demonstrated that early life emotional abuse was a unique predictor of loneliness, and this relationship was fully mediated by lower perceived support or value in social connections and more frequent unsupportive interactions with friends. Study 3 evaluated the implications of the age of occurrence of abuse. Both emotional and sexual abuse predicted young adult loneliness regardless of age; abuse that was recalled to have occurred at very early ages was not predictive of loneliness over and above consideration of events that happened in older childhood. These relationships were at least partially mediated by perceived social support, social connectedness, and in the case of emotional abuse, unsupportive interactions with friends. Our results add to mounting evidence pointing to the prevalence of loneliness among young adults and the links to adverse early life experiences that may serve to shape appraisals of safety, value, and personal worth in social relationships. (shrink)

This study aimed to analyse the contribution of infaq funds to the social and economic resilience of the community during the COVID-19 pandemic in West Papua, Indonesia. This study uses a mixed-method approach, combining qualitative and quantitative studies. Qualitative data were collected through focus group discussions with administrators, Dai [Islamic preacher] and mosque congregations to obtain information about the form and mechanism for disbursing infaq funds. Furthermore, the state of distribution of infaq funds is confirmed to the recipient community with (...) an online survey as quantitative data. The data obtained were tabulated and analysed with descriptive and inferential statistics using multiple linear regression assisted by SPSS software 25 version. The research findings show that: firstly, the form of the social-economic contribution of infaq funds is carried out by: (1) financial assistance, (2) social assistance, and (3) health assistance. Secondly, infaq, an instrument of Islamic economics, can contribute to tackling the social and economic impacts of the community amid the COVID-19 outbreak. Thirdly, of the three forms of assistance provided, the health assistance aspect contributed 38.320%, the financial assistance aspect amounted to 37.173% and 24.339% to social assistance. This study shows that the community most needed health and financial assistance from infaq funds during the COVID-19 pandemic.Contribution: This study complements the existing literature and provides a new scientific treasure. That the infaq fund, as a philanthropy, turned out to be able to contribute to realising the social-economic resilience of the community during a disease outbreak. The form of health, financial and social assistance from infaq funds is a priority in accelerating the community’s economic recovery. It can be a countermeasure to socio-economic impacts during disasters and disease outbreaks. (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)

ObjectiveThe scarcity of research on associations between inflammatory markers and symptoms of depression and anxiety during adolescence has yielded inconsistent results. Further, not all studies have controlled for potential confounders. We explored the associations between baseline inflammatory markers and psychological distress including moderators at follow-up in a Norwegian adolescent population sample.MethodsData was derived from 373 girls and 294 boys aged 15–18 years at baseline, in the Fit Futures Study, a large-scale 2-year follow-up study on adolescent health. Baseline data was gathered (...) from 2010 to 2011 and follow-up data from 2012 to 2013. Psychological distress was measured with Hopkins Symptom Checklist. Serum levels of the following inflammatory markers were measured: C-reactive protein, Interleukin 6, Transforming growth factor alpha, Tumor necrosis factor alpha variant 1, and variant 2. Independent associations between baseline inflammatory markers and HSCL-10 at follow-up were explored by linear regressions, in sex-stratified analyses.ResultsIn girls, analyses showed positive associations between all inflammatory markers and HSCL-10, except for TRANCE. However, all associations were non-significant in crude as well as in adjusted analyses. In boys, CRP and TGF-α showed significant associations with HSCL-10, that remained significant after adjustment. Additionally, moderators were found. In boys, CRP was associated with HSCL-10 in those with high body fat and those being physical inactive, and the association between TWEAK and HSCL-10 was dependent upon sleep duration.ConclusionThere were significant prospective associations between CRP, TFG-α, and HSCL-10 in boys aged 15–18 years at baseline. (shrink)

ObjectiveIn the context of Healthy China, the effect of external motivation on sports anomie behavior from the perspective of gender among Chinese college students was investigated.ParticipantsIn total, 2,340 college students were involved in this study.MethodsThe self-made scales were used, which were about anomie behavior and external motivation in sports. The independent sample T-test was used to compare the sports anomie behavior between male and female students. Then, multiple linear regression analysis was adopted to examine the effects of external (...) motivation on sports anomie behavior.ResultsGirls’ sports anomie behavior was lower than boys. There were significant gender differences in honor motivation, obedience motivation, and economic motivation; girls’ motivations were lower than boys. The academic motivation was negatively correlated with the sports anomie behavior of college students of both genders. The economic motivation was positively correlated with their sports anomie behavior. The honor motivation was negatively correlated with the sports anomie behavior of female college students.ConclusionFemale college students’ sports anomie behavior and external motivation are lower than that of male college students. External motivation had a significant influence on college students’ sports anomie behavior. (shrink)

Social climate is a relatively new concept measuring people’s wellbeing (Duguleană and Duguleană 2015), operationalized by perceptions of people’s conditions of living. It has been used in the Eurobarometer surveys since 2009 but still gained little attention in academic research. In this paper, issues in constructing an index of social climate are being discussed. As the rates of meaningful responses to questions on dif­ferent aspects of the social climate vary greatly, the author proposes a revisited version of the social climate (...) index as well as assesses its internal consistency and usability. The article presents the results of the regression analysis on the impact of factors related to the social climate across the three Baltic countries. The findings suggest that the political competence, the type of community, and the employment status account for most differences in social climate perceptions. (shrink)

There is growing interest in examining dark personality traits, to better explain malevolent and self-serving behaviour patterns commonly observed in clinical and non-clinical settings. Recently, taxonomies of dark personalities have been developed, along with psychometric tools to measure and delineate between traits including psychopathy, Machiavellianism and narcissism. The extent to which these constructs are distinct or overlapping remains controversial. Psychophysiological research can improve understanding of biological mechanisms contributing to personality that may help to evaluate taxonomies. This study investigated diurnal variations (...) in salivary cortisol (on waking, cortisol awakening response, and at bedtime, over two days) and their relationship to personality traits (Short Dark Triad Scale and IPIP Mini), psychopathology (Depression, Anxiety and Stress Scale; DASS) and quality of life (WHOQOL-BREF) in a non-clinical sample (N=45, 26 females, 19 males, 18-59 years). Dark personality traits correlated with low bedtime cortisol, reduced sleep, depression and poorer quality of life. Hierarchical linear regressions analyses indicated that bedtime cortisol levels significantly accounted for variance in Psychopathy (F(1,39)=5.55, p=.02, R=.52, B=-1.81, 95% CI [-3.37-(-.26)], β=-.33, sr2=.10) and reduced sleep uniquely accounted for variance Machiavellianism (F(2,40)=5.99, p=.01, R=.48, B=1.01, 95% CI [.16-2.02], β=.34, sr2=.11). These data provide preliminary evidence that supports the cortisol hypoarousal hypothesis of Psychopathy and has identified a link between Machiavellianism, sleep-wake cycles, and chronotype. The hypoarousal hypothesis of Psychopathy contends that the hypothalamic-pituitary-adrenal axis is unresponsive to the stress of social engagement. Machiavellianism was predicted by longer days with evidence for an evening chronotype. These findings indicated that the Dark Triad traits have different underlying biological mechanisms that require further exploration. (shrink)

The aim of this study, which is a field reseach, is to determine the level of religiosity and subjective well-being (SWB) of patients with chronic renal failure who are receiving hemodialysis treatment with a descriptive approach and by using socio-psychological methods and to try to determine the relationship between their religiosity and subjective well-being. The sample of the study consists of 205 individuals who were determined by stratified random sampling method from the patients treated in Turkish Ministry of Health, Hitit (...) University Çorum Erol Olçok Training and Research Hospital and Çorum District Dialysis Units. 54.1% (n=111) of the participants are males and 45.9% (n=94) are females. Data was collected via a “Religiosity Inventory”, developed by Kula, and “Subjective Well-Being Scale”, developed by Dost, to determine participants’ religiosity and subjective well-being as well as a personal information form to determine demographic characteristics. Statistical analysis of the data showed significant differences and relationships between demographic variables, religiosity, and subjective well-being. Moreover, a statistically significant relationship between the levels of religiousness and subjective well-being of the participants was found and that religiosity accounts for 31.9% of subjective well-being variance. The findings of the study show that the major hypotheses and sub-hypotheses have statistically been validated and that religiosity is a significant and important predictor of subjective well-being.Summary: Throughout history, existential questions and the answers sought for these questions have remained important for human beings. Human beings have always wondered about life, meaning of life, death, after death and pursued meaning systems that will satisfy this curiosity. While various thinkers and philosophers put forward opinions about the secrets of life; religion has presented the transcendent dimension to the individual as a source of meaning. Thus, the individual has found the definitive answer to many of his questions about existence in religion, and this sense of certainty has contributed positively to his psychological well-being. However, human beings may also face disasters, diseases or adversities in the natural flow of life and sometimes unexpectedly, such situations make them desperate. Desperation creates a feeling of loneliness in the person and this can trigger a sense of meaninglessness for him. The feeling of vacuum in the inner world of the individual who struggles with difficulty or the sense of meaninglessness somehow affects his subjective well-being.From this perspective the subject of the study is the effect of religiosity levels on subjective well-being of individuals who are struggling with chronic renal failure and have experienced a compelling treatment process such as hemodialysis. The aim of this study is to determine the level of religiosity and subjective well-being of these patients. The sample of the study consists of 205 individuals who were determined by stratified random sampling method from the patients treated in Turkish Ministry of Health, Hitit University Çorum Erol Olçok Training and Research Hospital and Çorum District Dialysis Units.The major hypothesis of the study is “The religiosity levels of hemodialysis patients have a significant effect on their subjective well-being.” Further sub-hypotheses are as follows: 1. Women’s religiosity is higher than men’s. 2. There is a significant relationship between the perception of subjective happiness and religiosity levels of the participants. The subjective happiness perception levels of the individuals with high religiosity scores are also high. 3. There is no significant difference between the SWB levels of the participants according to gender variable. 4. There is a significant relationship between the perception of subjective religiosity and SWB levels of participants. According to this; subjective well-being increases as the person’s self-religious perception levels increase.5. There is a significant positive relationship between the religiosity scores of the participants and SWB scores. 6. Religiosity is a significant predictor of subjective well-being. Thus; the SWB level is expected to increase as the religiosity scores increase.The data of the study was obtained by using Personal Information Form prepared by the researcher, Kula’s Religiosity Inventory and Dost’s Subjective Well-Being Scale. The study was conducted in hospitals. After getting the necessary legal permissions and consent of the patients, the patients were informed about the study and the study was conducted on a voluntary basis. The questionnaires were applied by the researcher in person through face-to-face interviews with patients in hospitals, sometimes on weekdays and sometimes on Saturdays. Together with the physician of the hemodialysis unit, district hospitals were visited to reach the target group receiving treatment in these districts.In accordance with the relational screening model, the data of the study was analyzed with SPSS 16.0 package program. The mean (x̄) and standard deviation (ss) scores of the Religiosity Inventory and Subjective Well-Being Scale were calculated according to independent variables and tables were created. The significance of the difference between the two means obtained from two independent sample groups was determined by independent samples t-test for independent samples. One-way Varayns analysis (ANOVA) was used to test hypotheses about whether there was a difference between the means of more than two sample groups. In case of significant differences between means, LSD test and Tamhane’s T2 test from post-hoc multiple comparison tests were usedand the direction of the differences were determined as such. To determine the relationship between two continuous variables, religiosity and subjective well-being, Pearson Moments Correlation Analysis technique was used, and Simple Linear Regression analysis was performed to determine how much religiosity explains the changes observed in the dependent variable. The significance of the obtained statistics was tested at 0.05 level and bidirectional and the analysis values were presented in sequential tables in accordance with the purpose of the research. It is possible to summarize the findings of the study as follows:• 45.9% (n=94) of the sample are women and 54.1% (n=111) are men. According to subjective evaluations, 55.6% of the sample considers themselves as happy and very happy and 79.5% defines themselves as religious and very religious. According to the findings, subjective religiosity perception is higher in patients than subjective happiness perception. • The sample group has a high level of religiosity ( x̄ =4,3713).• There is a significant difference between gender and general religiosity (t=2,933; p=,004; p 0.05). However there is a difference in the SWB averages of male and female participants, women scored higher (x̄=3,8309) in SWB than men (x̄=3,7135). • A significant relationship has been found between subjective religiosity perception and SWB level of the sample (F=12,345; p=,000, p<0.05). SWB levels increased with increasing subjective religiosity. Further, there is a significant relationship between “slightly religious” (x̄=3,3439), “religious” (x̄=3,7737) and “very religious” (x̄=4,1363) groups and SWB level. There is also a steady increase in the mean scores of SWB from “not at all religious” to “very religious”. • A positive and moderately significant relationship has been found between general religiosity and general SWB (r=,565, p<0.01). • According to the results of regression analysis to determine whether total religiosity in HD patients is a significant predictor of subjective well-being; religiosity explaines 31.9% of the variance in subjective well-being. This result shows that one of the important determinants of SWB in HD patients is religiousness. Religiosity is a significant and important predictor of subjective well-being, and increased religiosity directly affects the SWB levels of HD patients. In addition, the basic hypothesis and sub-hypotheses were validated statistically at the end of the study. (shrink)

Synchronization is a special case of self-organization in which one can observe close mimicry in behavior of the system components. Synchrony in body movements, autonomic arousal, and EEG activity among human individuals has attracted considerable attention for their possible roles in social interaction. This article is specifically concerned with autonomic synchrony and finding the best model for the dyadic relationships, with regard to both theoretical and empirical accuracy, that could be extrapolated to synchrony levels for groups and teams of three (...) or more people. The four models that are compared in this study have different theoretical origins: the two-variable linear regression function, a three-parameter nonlinear regression function, the logistic map function stated in polynomial form, and the logistic map function stated as an exponential regression structure. The data for this study were electrodermal responses collected from a team of four people engaged in an emergency response simulation that produced 12 dyadic time series. Results shows strong levels of fit between the data and all four models, although there were significant differences among them. Further research directions point toward finding conditions that favor one model over another and exploring other possible nonlinear structures. (shrink)