Presentation of Novel Multiple Regression Model for Accounting Information Quality, Corporate Investment, and Moderating Role of Ownership Structure in Companies

The conflicts of interest between managers and shareholders force managers to make decisions that do not meet the interests of shareholders. One of these decisions is to invest less in the company’s operational activities. Increasing the information quality is one way to prevent that issue, allowing stakeholders to monitor investment decisions. The present study investigates the relation of accounting information quality, corporate investment, and ownership structure for Iranian firms, using panel data analysis from 2009 to 2018. We applied a multiple regression model to test the hypotheses. The results show that the quality of accounting information significantly affects the investment decision for the company’s main operations, so the first hypothesis of the research is confirmed. At the same time, there is no effect of capital structure on accounting information quality and firms’ investment decisions. Thus, the second hypothesis of the research failed to be confirmed.

Model-Based Analyses for the Causal Relationship Between Post-stroke Impairments and Functional Brain Connectivity Regarding the Effects of Kinesthetic Illusion Therapy Combined With Conventional Exercise

Aims: Therapy with kinesthetic illusion of segmental body part induced by visual stimulation (KINVIS) may allow the treatment of severe upper limb motor deficits in post-stroke patients. Herein, we investigated: (1) whether the effects of KINVIS therapy with therapeutic exercise (TherEx) on motor functions were induced through improved spasticity, (2) the relationship between resting-state functional connectivity (rs-FC) and motor functions before therapy, and (3) the baseline characteristics of rs-FC in patients with the possibility of improving their motor functions.Methods: Using data from a previous clinical trial, three path analyses in structural equation modeling were performed: (1) a mediation model in which the indirect effects of the KINVIS therapy with TherEx on motor functions through spasticity were drawn, (2) a multiple regression model with pre-test data in which spurious correlations between rs-FC and motor functions were controlled, and (3) a multiple regression model with motor function score improvements between pre- and post-test in which the pre-test rs-FC associated with motor function improvements was explored.Results: The mediation model illustrated that although KINVIS therapy with TherEx did not directly improve motor function, it improved spasticity, which led to ameliorated motor functions. The multiple regression model with pre-test data suggested that rs-FC of bilateral parietal regions is associated with finger motor functions, and that rs-FC of unaffected parietal and premotor areas is involved in shoulder/elbow motor functions. Moreover, the multiple regression model with motor function score improvements suggested that the weaker the rs-FC of bilateral parietal regions or that of the supramarginal gyrus in an affected hemisphere and the cerebellar vermis, the greater the improvement in finger motor function.Conclusion: The effects of KINVIS therapy with TherEx on upper limb motor function may be mediated by spasticity. The rs-FC, especially that of bilateral parietal regions, might reflect potentials to improve post-stroke impairments in using KINVIS therapy with TherEx.

Predicting change-of-direction performance in elite young badminton players: A multiple regression analysis on acceleration- and deceleration-related qualities

The aim of this study was to examine the interrelationships between direct (sprint and change-of-direction [COD] velocities) and indirect measures (COD-deficit [CODD], deceleration deficit [DD], and sprint momentum) of speed-related performances in young badminton players. Thirty young male badminton players (age: 16.8 ± 1.4 years; body-mass: 61.5 ± 7.9 kg; height: 170 ± 5.8 cm) performed a 20-m sprint followed by 505 COD tests, on the same day. A Pearson product moment test was applied to determine the relationships among variables. A multiple regression analysis was used to verify whether the combination of CODD and DD increased the capacity to predict COD performance. Large and significant relationships were observed between COD and linear sprint velocity and sprint momentum ( r ranging from 0.62 to 0.84; P < 0.05). COD velocity revealed a moderate significant ( r = −0.38) and a small non-significant ( r = 0.29) relationship with CODD and DD, respectively. The multiple regression model combining CODD and DD explained 44% of the variance in COD performance. In summary, young badminton players who sprint faster are equally faster in COD manoeuvres but present higher levels of CODD and DD. Coaches should be aware that faster badminton players may exhibit greater magnitudes of CODD-DD, thus requiring specific interventions to optimize the transition between high deceleration and (re) acceleration phases.

The role and importance of perceived risk in medical tourism. Applying the theory of planned behaviour

The main aim of the study was to investigate the role and importance of perceived risk in medical tourism (MT). The research demonstrates that the country of origin and an assessment of the respondents’ own health condition significantly moderates the impact of their attitudes on behavioral intention. The research shows a strong correlation between the risk perceived by the respondents and their attitudes towards MT services. This relationship is significantly moderated by risk awareness, aversion to risk and gender. Additionally, an important factor in the model is the level of satisfaction with medical services in their own country. The survey using a fully structured questionnaire was conducted among young consumers from Jordan, Poland and Turkey. To verify the hypotheses, a multiple regression model with interactions was used.

Household Livelihood Vulnerability to Climate Change in West China

Climate change disproportionately affects natural resource-dependent communities in the ecologically vulnerable regions of western China. This study used the household livelihood vulnerability index under the Intergovernmental Panel on Climate Change (HLV-IPCC) to assess vulnerability. Data were collected from 823 households in Ningxia, Gansu, Guangxi, and Yunnan provinces, these being ecologically vulnerable regions in China. With a composite HLVI-IPCC and multiple regression model, the factors that affect households’ adaptive capability to HLVI-IPCC was estimated. Results indicate that Ningxia is the most vulnerable community, while Guangxi is the least vulnerable community across all indices. Moreover, Gansu has the heaviest sensitivity and exposure to climate change, whereas Ningxia has the highest adaptive capability to climate change. In addition, the age of household head and distance of the home to the town center had significant negative impacts on households’ adaptive capacity to HLVI-IPCC. The results also suggest that the HLVI assessment can provide an effective tool for local authorities to formulate prioritizing strategies with promoting climate-resilient development and increasing long-term adaptive capacity.

Elements for Building Supervised Statistical Machine Learning Models

This chapter gives details of the linear multiple regression model including assumptions and some pros and cons, the maximum likelihood. Gradient descendent methods are described for learning the parameters under this model. Penalized linear multiple regression is derived under Ridge and Lasso penalties, which also emphasizes the estimation of the regularization parameter of importance for its successful implementation. Examples are given for both penalties (Ridge and Lasso) and but not for penalized regression multiple regression framework for illustrating the circumstances when the penalized versions should be preferred. Finally, the fundamentals of penalized and non-penalized logistic regression are provided under a gradient descendent framework. We give examples of logistic regression. Each example comes with the corresponding R codes to facilitate their quick understanding and use.

Exploration of potential regional resources for beef cattle farming development in West Java, Indonesia

More than 90% of cattle in Indonesia are raised by smallholder farmer. They are facing limited land resources so that the input of feed mostly depend on the agriculture waste and its environment. The aimed of this study was to determine the potential agricultural resources which influence to the development of beef cattle. The sample locations were 3 districts from the eastern part of West Java province. Panel data of variables that were thought have an effect on beef cattle development were taken from 2012 to 2019 in three regencies. These data were analyses by Multiple Regression Model using the OLS method. The stationary test was done before using time series data by unit root tests of Augmented Dickey-Fuller. The results of this research showed that the corn and Cassava production, as well as the population as a source of agriculture labor in a region was potential resources to increase of beef cattle population. The integration of cattle and corn farming on suitable land in a region needs to be considered as the strategy in beef cattle farming development, because the corn production per unit of land can produce the highest feed compared to the other food crop residues.

Study on the Factors Influencing Total Health Cost Based on Multiple Regression Model

In recent years, China's social economy and income level of residents have increased rapidly, the total cost of health has increased rapidly, and the level of medical expenditure of residents has been increasing. This paper establishes a multivariate linear regression model using data from 1996 to 2020, and analyzes several important influencing factors that affect overall health expenditure. The aim is to formulate a health financing policy suitable for the coordinated development of China's social economy, and to provide a basis for adapting to the needs of economic development, structural adjustment and institutional transformation.

Effects of Meaning in Life and Social Support on Posttraumatic Growth in Pancreatic Cancer Survivors

Purpose: The purpose of this study was to examine the correlations among meaning in life, social support, and posttraumatic growth and to identify the factors related to posttraumatic growth in pancreatic cancer patients.Methods: A total of 118 pancreatic cancer patients who received care in an outpatient clinic and inpatient units in an oncology center in Seoul were enrolled from February 2021 to March 2021. We collected data using self-report survey tools. Data were analyzed using descriptive statistics, Pearson correlation coefficients, and hierarchical multiple linear regression with SPSS/WIN 26.0Results: Meaning in life, social support, and posttraumatic growth had significant positive correlations with each other, and the correlation between posttraumatic growth and meaning in life was the highest (r=.63, p<.01). In the hierarchical multiple regression model of posttraumatic growth (adjusted R2=.48, p<.001), marital status (β=-.14, p=.04), meaning in life (β=.44, p<.001), and social support (β=.27, p=.001) were statistically significant.Conclusion: There is a need for nursing interventions that provide social support and promote the meaning in life to facilitate posttraumatic growth in pancreatic cancer patients.