Variance In Dependent Variable Research Articles

The Influence of Intellectual Capital and Investment Decisions on Company Value

This study aims to determine the influence of intellectual capital and investment decision on firm value. This type of research is correlational, aimed at proving whether there is a relationship between variables as found in previous studies. The population includes all consumer non-cyclicals companies listed on the Indonesia Stock Exchange for the period from 2013 to 2022. The sampling technique used is Purposive Sampling. The population data consists of 290 companies, and a sample of 130 companies was obtained. The data analysis method used is multiple linear regression analysis. The results of the data analysis show that intellectual capital has a positive effect on firm value, and investment decision have a positive effect on firm value. The adjusted R-Square value of 0.1204 indicates that the ability of independent variables to explain the variation in dependent variables is 12.04%, and the remaining 87.6% is explained by other variables outside the research model.

Contribution of climatic variables and their interactions to reference evapotranspiration changes considering freeze-thaw cycles in the Tibetan Plateau during 1960–2022

While the direct impact of climate change on reference evapotranspiration (ET0) has been extensively studied, there is limited research on the indirect impact resulting from the interaction between climatic variables. This gap hinders a comprehensive understanding of climate change effects on ET0. Additionally, there is scarce exploration into the quantitative effect of freeze-thaw cycles on ET0 variation. In this study, we employed path analysis and dependent variable variance decomposition methods to discern the direct and interactive effects of climatic variables on ET0 in the Tibetan Plateau from 1960 to 2022. Annual ET0 exhibited variation across basins, with the coefficient of variability during the thawed period smaller than that during the non-thawed period. On an annual scale, the largest contribution to ET0 variation came from water vapor pressure deficit (VPD) at 47.7%. This contribution was amplified by its coupled interaction with temperature (T) at 47.1%, although the contribution was partially offset by the interactive effects of VPD with downward shortwave radiation and wind speed at −2.4% and − 27.6%, respectively. During different freezing-thawing periods, VPD primarily controlled ET0 variation, with its interaction with other climatic variables enhancing its impact. Furthermore, soil moisture, influenced by freeze-thaw cycles, exhibited a strong correlation with T and VPD, indicating the significant effect of freeze-thaw cycles on ET0 variation. The weak correlation between ET0 and NDVI suggested that vegetation growth had a limited regulatory effect on ET0. These findings provide valuable insights into the impact of interactions between climatic variables on hydrological processes, enhancing our understanding of the interactive roles of hydrometeorological variables.

Trend-Fourier Time Series Regression Model for Secular-Cyclical Datasets

The study proposed a Trend-Fourier Regression (TFR) model to handle time series datasets with simultaneous trend and cyclical variations. The model steps involve identification, estimation, diagnosis and forecasting. The Nigerian monthly Crude Oil Price (NMCOP) was used to implement the model and NMCOP was identified as trend-cyclical. The model estimation using Ordinary Least Squares method indicates that an increase in time will result in changes in NMCOP. Durbin-Watson statistics, histogram and autocorrelation function of residual plots were used to diagnose and specify the model to be stable. The coefficient of determination (R^2) indicates that over 80% of dependent variable variations were explained, with an adjusted (R^2) indicating a predictive ability exceeding 80%. The model efficiency was confirmed through out-sample and forecast evaluations, revealing superiority due to its smaller MAE, RMSE, and MAPE values, indicating minimal error. Conclusively, the TFR model is suitable for datasets that exhibit trend-cyclical variations simultaneously.

The relationship between muscle quality index and physical function in older adults

BACKGROUND: The muscle quality index (MQI) has been proposed as a diagnostic tool to detect individuals at risk of limited physical function. OBJECTIVE: Our goal was to examine the relationship of the MQI with physical function and compare its predictive ability with other muscle parameters of strength and mass in an older population. METHODS: Sixty-eight healthy older adults (34 male, 34 female) aged between 69 and 89 years were tested for body composition, MQI, maximum isokinetic concentric KES and physical function including sit-to-stand (STS) time, normal and maximum walking speed (WS), Timed Up and Go (TUG) and static balance. Pearson’s correlation was applied to examine the relationship between muscle parameters. Linear regression analysis including age and sex as additional covariates was performed to assess their predictive ability for physical functions. RESULTS: MQI correlated significantly with relative KES (r= 0.611, p< 0.001), total KES (r= 0.829, p< 0.001) and leg lean tissue mass (r= 0.690, p< 0.001). The MQI was not a significant predictor for any physical function (p> 0.05). STS time was a significant predictor for normal WS and TUG time (p< 0.001). Relative KES was a significant predictor for all physical functions except static balance (p< 0.001). No muscle parameter and only age was a significant predictor for static balance. Models explained 20.4%–57.3% of variances of dependent variables. CONCLUSIONS: The MQI is a useful tool to assess leg lean tissue mass and strength of the knee extensor muscles and outperforms STS time. However, STS time and relative KES are more closely related to physical function than the MQI.

Performance of Sugarcane Cultivation in Thiuvannamalai District of Tamil Nadu, India - A Socio-economic Analysis

The core objective of the study is to analyze the performance of sugarcane in terms of cost and returns and resource use efficiency. A sample of 30 Sugarcane growing farmers purposively selected at random from the Vazhavachannur and Perunduraipattu villages of Thandrampattu block of Tiruvannamalai District. Since all the sugarcane farmers use the same cultivation practices, 30 sample farmers are sufficient to represent the entire sugarcane farmers of the study area. Tools like simple average and percentage analyses were used to examine cost of cultivation, labour hours used, quantity of materials used and machine hours used by farmers in each crop production. Regression analysis was employed to measure the influence of selected explanatory variables on independent variables. Result showed that joint family constitutes about 33.00 percent and nuclear family was 67 of the total sample households. Average size of holding is 3.28 acre. Total cost of cultivation for groundnut is Rs.59376.64/acre. Gross income from groundnut is Rs.140962.50/acre. Benefit Cost Ratio (BCR) of groundnut cultivation is 2.37. All the sample farmers wholly depend on the sugarcane cooperatives for selling their produce. The human labors were used for Inter cultural operation, sowing & transplanting, chemicals, fertilizer and Farm Yard Manure (FYM). FYM, chemicals, fertilizer and seed are the major inputs used in crop production. Bund Former and Rotavator is the machines used for crop production. About 89.00 percent of variation in dependent variables explained by the selected independent variables such as quantity of chemicals and labor hours for inter cultural operation.

A Study on Income, Expenditure and Resource Use Pattern of Paddy in Tiruvannamalai District

Recently a study entitled “A study on income, expenditure and resource use pattern of paddy in Tiruvannamalai district” is conducted by a student with a view to analyze the performance of paddy in terms of cost and returns, resource use efficiency, and employment generation. The sample of 30 farmers growing paddy were selected at random from the Thanipadi, Vazhavachannur, Keezhsirupakkam, Perunduraipattu, Thandrampet, Veppur, and Andapattu villages. Average and percentage analyses were used to examine cost of cultivation, labour hours used, quantity of materials used and machine hours used by farmers in each crop production. Regression analysis was employed to measure the influence of selected explanatory variables on independent variables. Joint family constitutes about 43 percent and nuclear family was 57 of the total sample households. Average size of holding of 2.29 acre. Total cost of cultivation for paddy is Rs.17172.08/acre. Gross income from paddy is Rs.38919.09/ac. Benefit Cost Ratio (BCR) of paddy cultivation is 2.27. Farmers sell the produce at aggregators, regulated market and mill owners. The human labors were used for sowing & transplanting, cultural operation and chemicals. FYM, chemicals, fertilizer and seed are the major material used in crop production. Rotavator and harvester are the machine used for crop production. About 77 percent of variation in dependent variables explained by the selected independent variables such as labor hours for inter cultural operation and quantity of seed and hours per irrigation.

Estimation of Income, Expenditure and Resource use Pattern of Groundnut in Thandrampattu Block of Tiruvannamalai District, Tamil Nadu, India

The core objective of the research estimation of income, expenditure and resource use pattern of groundnut in Thandrampattu block of Tiruvannamalai district is to evaluate the performance of groundnut in terms of cost and returns and resource use efficiency. The sample of 30 farmers growing groundnut were selected at random from the Thandrampet, Veppur, Thanipadi and Vanapuram villages of Thandrampattu Block of Tiruvannamalai District. Average and percentage analyses were used to examine cost of cultivation, labour hours used, quantity of materials used and machine hours used by farmers in each crop production. Regression analysis was used to assess the impact of selected explanatory variables on independent variables. Joint families account for around 33% of total sample homes, with nuclear families accounting for 67 percent. Average size of holding is 2.84 acres. Total cost of cultivation for groundnut is Rs.16371.68/acre. Gross income from groundnut is Rs.54000/ac. Benefit Cost Ratio (BCR) of groundnut cultivation is 3.30. Farmers sell the produce at regulated market, mill owners and local traders. The human labors were used for sowing and transplanting, FYM, fertilizer, chemicals, intercultural operations. FYM, chemicals, fertilizer and seed are the major material used in crop production. Harvester, rotavator and bund former are the machine used for crop production. About 69 percent of variation in dependent variables explained by the selected independent variables such as labor hours for inters cultural operation, land holding size and quantity of FYM.

Menganalisis Pengaruh Strategi Bauran Pemasaran Terhadap Pembelian Sertifikat Laik Operasi Tegangan Rendah (SLO-TR)

This research aims to analysis the significant influence between marketing mix strategy on the purchase of Low Voltage Operating Eligibility Certificate (SLO-TR). Marketing mix strategy indicator as independent variable are product (X1), price (X2), location (X3), promotion (X4), people (X5), process (X6) and physical evidence (X7). The purchase decision indicator of Low Voltage Operating Eligibility Certificate (SLO-TR) as the dependent variable is the purchasing decision (Y). Quantitative research methods are described in multiple linear regression analysis models. The population is obtained from customers who make purchasing decision for Low Voltage Operating Eligibility Certificates (SLO-TR) at PT. Serkolinas. Type of research with primary data collected by dissemination of questionnaire. Using the Slovin formula, the number of respondents who became a sample of 100 people for further analysis. Stratified proportionate sampling technique by random sampling. Research results: (1) The estimation model shows an R2 value of 0,534 which represents the value of the coefficient of determination. This means that 53,40% of the dependent variable variation is able to be explained by the independent variable in this model. The remaining 46,60% is explained by other causes that are not included in the model; 2) The regression model on the independent variable simultaneously influence the dependent variable; and (3) This research produces results that product (X1), price (X2), process (X6) and physical evidence (X7) are influential and significant in analysis the influence of marketing mix strategy on purchasing decision (Y). But, place (X3), promotion (X4) and people (X5) had no influential and were not significant in analysis the influence of marketing mix strategy on purchasing decision (Y).

The Analysis of Macroeconomic and Microeconomic Factors in Non-Performing Financing of Sharia Bank in Indonesia

This study aims to analysis of macroeconomic and microeconomic factors in non-performing financing of Sharia Bank in Indonesia. Inflation and the BI7DRR are macroeconomic factors. CAR and BOPO are indicators of microeconomic factors. NPF is a problematic financing indicator. Research methods with quantitative methods described in multiple linear regression analysis models. The population is obtained from the Bank Indonesia and Financial Ratios of Sharia Commercial Banks through the Sharia Indonesia Banking Statistics of the Financial Services Authority. The data is secondary in the form of monthly data for the period January 2018 to November 2022, so that a sample of 59 data was obtained for further analysis. Sampling techniques nonprobability by means of purposive sampling. The results of the study are as follows: (1) The estimation model shows an R2 value of 0,738 which represents the value of the coefficient of determination. This means that 73,80% of the dependent variable variation is able to be explained by the independent variables in this model. The remaining 26,20% is explained by other causes that are not included in the model; 2) The regression model on the independent variable simultaneously influence the dependent variable, so that the independent variable regression model can be used to predict the dependent variable; and (3) This research produced output that the influential and significant ones are Inflation and BI7DRR as macroeconomic factors that can be used to analysis non-performing financing of Sharia Bank in Indonesia. This research also produced an output that CAR and BOPO have no influential as microeconomic factors that cannot be used to analysis non-performing financing of Sharia Bank in Indonesia. Although CAR and BOPO the results are significant.

Anxious and Angry: Early Emotional Adaptation of Medical Students in a Situational Crisis on the Example of the COVID-19 Pandemic.

The COVID-19 pandemic is an example of a situational crisis resulting in emotional destabilization. The aim of the study was to analyze changes in the level of anxiety and anger in medical students during the early adaptation to the situational crisis, and to estimate the risk factors for fear and anger in this group. Participants were 949 medical students (M = 22.88, SD = 4.10) in the first stage on March 2020, and 748 (M = 22.57, SD = 3.79) in the second stage on June 2020. The STAI, STAXI-2, and our own questionnaire were used. First vs the second stage: anxiety state (p < 0.001), anger state (p = 0.326), and feeling angry (p < 0.05). The regression model (F(14.1681) = 79.01, p < 0.001) for the level of anxiety state explains 39% of the dependent variable variance (r2 = 0.39). The model for the anger-state level (F(6.1689) = 68.04, p < 0.001)-19% (r2 = 0.19). During the early adaptation to the situational crisis, the general level of anxiety decreased, but anger was at the same level. The anxiety was explained by contact with potentially or objective infected persons, and the level of anger was based on the need for greater social support.

Latent Inhibition in Schizophrenia and Schizotypy

Abstract Background The Salience Hypothesis posits that aberrations in the assignment of salience culminate in hallucinations and unusual beliefs, the “positive symptoms” of schizophrenia. Evidence for this comes from studies on latent inhibition (LI), referring to the phenomenon that prior exposure to a stimulus impedes learning about the relationship between that stimulus and an outcome. Design This article reviewed all published studies examining the relationship between LI and both schizophrenia and schizotypy. Results Contemporary literature suggests that LI is attenuated in both people with schizophrenia and those loading highly on measures of schizotypy, the multidimensional derivative of schizophrenia. This suggests that these individuals assign greater salience to stimuli than healthy controls and people scoring low on measures of schizotypy, respectively. However, several confounds limit these conclusions. Studies on people with schizophrenia are limited by the confounding effects of psychotropic medications, idiosyncratic parsing of samples, variation in dependent variables, and lack of statistical power. Moreover, LI paradigms are limited by the confounding effects of learned irrelevance, conditioned inhibition, negative priming, and novel pop-out effects. Conclusions This review concludes with the recommendation that researchers develop novel paradigms that overcome these limitations to evaluate the predictions of the Salience Hypothesis.

METHOD FOR CALCULATION OF DISPERSIONS OF DEPENDENT VARIABLES OF A STOCHASTIC MODEL OF QUASI-STATIONARY OPERATING MODES OF THE MAIN WATER PIPELINE

The subject of research in the article is a stochastic model of quasi-stationary modes of operation of water supply and distribution systems, which adequately describes the actual operating modes of the water supply system for a given time interval and can be used as a basic model for setting and solving problems of optimal stochastic control of the development and operation of supply and distribution systems. water. The goal of the work is to develop a method for calculating estimates of the dispersions of dependent variables depending on the given values of mathematical expectations and dispersions of independent variables for a stochastic model of quasi-stationary modes of operation of the main water conduit as a subsystem of the water supply and distribution system. To achieve this goal, it is necessary to solve the following tasks: to build a deterministic equivalent of a stochastic model of quasi-stationary modes of operation of the main water conduit; calculate estimates of mathematical expectations of dependent variables; calculate estimates of variances of dependent variables. To calculate estimates of the variances of dependent variables depending on the variances of independent variables, we will use the method of statistical linearization. To obtain estimates of mathematical expectations of dependent variables, the deterministic equivalent of the stochastic model of quasi-stationary modes of operation of the main water conduit is solved by the modified Newton method. The following results are obtained: a method for calculating estimates of the dispersions of dependent variables for a stochastic model of quasi-stationary modes of operation of the main water conduit. Conclusions: the paper proposes an approximate method for calculating the statistical properties of dependent variables depending on the statistical properties of the parameters and independent variables of the stochastic model of quasi-stationary modes of operation of the main water conduit. The proposed method is based on the construction of a deterministic equivalent of a stochastic model of quasi-stationary modes of operation of the main water conduit and its use for calculating estimates of variances of dependent variables depending on the given values of mathematical expectations and variances of independent variables. Compared with the simulation method, the proposed approximate method does not require significant time and computational resources. The use of the proposed method was demonstrated by an example.

PENGARUH DISIPLIN DAN MOTIVASI TERHADAP SEMANGAT KERJA PEGAWAI PADA DINAS KOMUNIKASI DAN INFORMATIKA KOTA SIBOLGA

This study aims to determine whether there is an influence of discipline and motivation on employee morale at the Communication and Informatics Office of Sibolga City. The type of research used in this research is a quantitative research method with a descriptive approach. The sample in this study were employees at the Communication and Informatics Office of the City of Sibolga, with a total of 56 respondents distributing questionnaires.&#x0D; &#x0D; Based on the analysis of the coefficient of determination obtained by 0.603 or 60.3%. This means that what occurs in the dependent variable variation is 60.3% morale determined by the independent variables of discipline and motivation simultaneously and the remaining 39.7% is determined by other factors not discussed in this study. From the results of the T-test in the study, it is known that the T-value for discipline is 3.556, and motivation is 3.418&gt; T-table 2.00575, so the hypothesis states that discipline and motivation partially or individually influence employee morale in communication and informatics in the city of Sibolga is acceptable. Meanwhile, from the results of the F test, it is known that the calculated F value is 40,178 &gt; F table 3.17 and a significance of 0.000 &lt;0.05 so that the hypothesis stating that discipline and motivation have a joint or simultaneous significant effect on employee morale in communication and informatics in the city of Sibolga is acceptable.

Determinants of E-Government Implementation in Indonesia

Aims: This study aimed to determine the effect of Regional Original Income, general allocation funds, capital expenditures, audit opinions, and the level of public education on the implementation of e-Government in local governments in Indonesia.&#x0D; Study Design: This study uses secondary data.&#x0D; Place and Duration of Study: Local Government Financial Reports (LKPD) and audit opinions. This information can be found at the Supreme Audit Agency (BPK). The level of community education attainment uses the new Average Years of Schooling (RLS) method from the Central Statistics Agency (BPS) representative of each province from 2018 to 2020.&#x0D; Methodology: The sample used is 347 local governments. The purposive sampling method and multiple regression analysis were used in this study and used the SPSS 25 analysis tool. Before running the regression test, the data was first tested using the classical assumption test.&#x0D; Results: The independent variables in this study consisted of Regional Original Income (PAD), General Allocation Funds (DAU), Capital Expenditures, Audit Opinions, and Community Education Levels. At the same time, the dependent variable is the implementation of e-Government seen from the SPBE index value. This study carried out hypothesis testing with the Coefficient of Determination Test (R2) and the t-Statistical Test. The Coefficient of Determination Test (R2) measured how far the model's ability to explain the variance of the dependent variable was. The value of the coefficient of determination is zero or one. A small R2 value means the ability of the independent variables to provide almost all the information needed to predict the dependent variable variance [1]. At the same time, the t-test statistic shows how far the influence of one explanatory/independent variable individually in explaining the variation of the dependent variable results in a decision to reject or accept H0. This research was processed using IBM SPSS version 25 software.&#x0D; Conclusion: In this study, the variables "Regional Original Income," "General Allocation Funds," "Opinion Audits," and "Community Education Levels" have a positive effect on the implementation of e-Government. On the other hand, the capital expenditure variable does not affect the implementation of e-Government.

Quantifying the Contributions of Climate Change and Human Activities to Maize Yield Dynamics at Multiple Timescales

Under a changing environment, the effect of climate change and human activities on maize yield is vital for ensuring food security and efficient socio-economic development. The time series of maize yield is generally non-stationary and contains different frequency components, such as long- and short-term oscillations. Nevertheless, there is no adequate understanding of the relative importance of climate change. In addition, human activities on maize yield at multiple timescales remain unclear, which help in further improving maize yield prediction. Based on the ensemble empirical mode decomposition method (EEMD), the method of dependent variable variance decomposition (DVVD) and the Sen-slope method, the effect of climate change including growing-season precipitation and temperature (i.e., GSP, GEP, CDD, GST, GSMAT, and GSMT) and human activities including effective irrigation area (EIA) and the consumption of chemical fertilizers (CCF) on maize yield were explored at multiple timescales during 1979–2015. The Heilongjiang Province, a highly important maize production area in China, was selected as a case study. The results of this work indicate the following: (1) The original maize yield series was divided into 3.1-, 7.4-, 18.5-, and 37-year timescale oscillations and a residual series with an increasing trend, where the 3.1-year timescale (IMF1), the 18.5-year timescale (IMF3), and the increasing trend (R) were dominant; (2) the original sequence was mainly affected by human activities; (3) climate change and human activities had different effects on maize yield at different timescales: The short-term oscillation (IMF1) of maize yield was primarily affected by climate change. However, human activities dominated the mid- and long-term oscillations (IMF3 and R) of maize yield. This study sheds new insight into multiple timescale analysis of the role of climate and human activities on maize yield dynamics.

Which Matters More for Migration: Evidence from Russian Regions’ Data

Many researchers of migration in Russia note deficiency of the works directed on modeling of migratory processes taking place in the country. This article is devoted to an assessment of impact power of socio-economic factors on interregional migration in Russia and to its comparison to recently found influence on this process of psychological characteristics of the accepting region population. As the aggregated at regional level estimates of intelligence, personal traits and characteristics of response style were calculated on the basis of results of online testing over 200 thousand respondents in 2012–19, indicators of socio-economic development of regions (n=16) are computed by averaging for the same time interval; the source of data - the Russian Federal Service of State Statistics (reference yearbooks “Regions of Russia” and the Uniform Interagency Information and Statistical System). In the analysis data of 78 subjects of the Russian Federation are included, but after association of capital agglomerations the number of regions was reduced to 76. It is established that the majority of the socio-economic indexes included in the analysis shows significant correlations with regional net migration coefficients. In general, the revealed pattern of correlations is agreed with results of other authors received on data of earlier periods of the analysis. Regression of net migration coefficients on socio-economic variables allowed calculating series of linear multifactor models. Best of these models accounted for about 44 % of a dependent variable variance. Earlier it was shown that the models calculated on the basis of limited number of aggregated psychological characteristics of the accepting region have the same level of accuracy. Moreover, addition of psychological variables to the best models based on socio-economic indexes provides essential increase of accuracy of prediction of regional net migration: the share of variance accounted for increases from 44 to 55–57 %. It is supposed that taking into account of psychological variables of the accepting region will create premises for more effective management of migratory processes.

Medical students in the face of SARS-CoV-2 pandemic: an estimated risk of infection and the need for social support.

A pandemic triggers certain attitudes and behaviors, resulting in the need for adequate social support. The aim of the study was to determine the factors influencing the estimated risk of SARS-CoV-2 infection and to assess the need for social support in a group of medical students. The survey was conducted among 981 people, including 814 women and 167 men. For this purpose, a self-assessment survey with 22 multiple-choice questions was used. The online survey was conducted on March 20-April 2, 2020, which can be considered as the first stage of the pandemic in Poland. The tested model for the subjective SARS-CoV-2 infection risk assessment explained 32.6% of the dependent variable variance (F(7.945) = 66.61, p < 0.001). The model for adherence to the recommendations for reducing the risk of SARS-CoV-2 transmission explained 9.5% of the variability (F(8.943) = 12.39, p < 0.001), and the model for an increased need for social support due to the SARS-CoV-2 epidemic explained 27.4% of the variability (χ2(8) = 12.17, p = 0.144). Factors important for estimating the risk of SARS-CoV-2 infection and the need for social support in the group of medical students were determined. The subjective assessment of the stress level, infection symptoms experienced and the field of study proved to be crucial. Int J Occup Med Environ Health. 2022;35(4):473-84.

THE EFFECT OF RETURN ON EQUITY AND DEBT TO EQUITY RATIO ON STOCK PRICES IN THE COMPANY TRANSPORTATION SUB SECTOR LISTED ON THE INDONESIA STOCK EXCHANGE FOR THE 2015-2019 PERIOD

This research aims to analyze the effect of the Return on Equity, Debt to Equity Ratio and towards stock price in the Transportation sub-rector in 2015-2019.&#x0D; The associative quantitative method is used as a research method using secondary data. The population of this research consists of 41 companies and by purposive sampling technique, 16 companies are obtained as a sample. Multiple linear regression is used by the researcher as a data analysis technique.&#x0D; The results showed that partially Return On Equity had a positive and significant effect on stock prices. Debt To Equity Ratio has a positive and significant effect on the stock price. Simultaneously or jointly Return On Equity and Debt To Equity Ratio affect the Share Price of Transportation companies listed on the Indonesia Stock Exchange from 2015 to 2019. The coefficient of determination of 0.466 means the ability of independent variables to explain the variation of dependent variables by 46.6%, while the remaining 53.4% is explained by other variables outside the research model

THE IMPACT OF HEALTHY DIET ON COVID-19 PANDEMIC USING CONTINENTS, GROSS DOMESTIC PRODUCT AND POPULATION

Many researchers have focused on COVID-19 Pandemic which was a global phenomenon and has brought about by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) which started in late 2019, this disease has spread throughout all globes and has affected the entire world and is one of the most leading cause of death in countries and continents. The aim of the study is to ascertain the relationship that exist between COVID-19 Pandemic Continents, Gross Domestic Product and Population between November 2019 to September 2020. Design: The researchers studied 170 countries and the dataset was extracted from the Kaggle database. The factors analyzed Qualitatively were data distribution of Continents Table 1., GDP per capita Table 2., and Population Category Table 3. Also Quantitatively data of COVID-19 variables (Confirmed, Death, Recovered, and Active cases) and Obesity Table 4., Correlation was also established to test the relationship between GDP per capita &amp; COVID 19 Variables Table 5. Regression for the joint contributions of independent variables (GDP growth, Population, GDP per capita &amp; Obesity) to the prediction of COVID 19 Death Table 6 and Relative effect of the independent variables (GDP growth, Population, GDP per capita &amp; Obesity) to the prediction of COVID 19 Death Table 7 were also analyzed. Results: Based on the period we considered 170 Countries. There were 27.1 % (46) out of 170 Countries which was allotted to Africa Participants and the p-value &lt; 0.05 which shows that Africans are majority of the data collected, GDP per capita 31.2%(53) under $ 0- $2499, Population category showed highest category to be 35.9% (61) under 988,003- 9,904,896, COVID-19 variables (Confirmed, Death, Recovered, and Active cases) and Obesity shows the outbreak of pandemic, a total of 331 cases was confirmed, 6 death cases, 238 recovered cases, active cases is 92, while 3124 of the sample across continents were found to be obese.. Correlation Test used show that the relationship between GDP per capita &amp; COVID 19 Variables, GDP per capita is positively correlated with number of COVID Variables Confirmed cases (r = .477, p&lt;0.01), number of Covid 19 Death cases (r = .363, p&lt;0.01), Covid 19 Recovered cases (r = .272, p&lt;0.01) and number of Covid 19 Active cases (r= .416, p&lt; 0.01). Regression Analysis showed a coefficient of multiple regressions R = 0.514 and multiple R-square = 0.264, it means that 26.4% variation in dependent variables (COVID Death) is explained by the independent variables (GDP growth, Population, GDP per capita &amp; Obesity ) and their exist a joint contributions of independent variables to the prediction of COVID 19 Death and Regression Analysis performed reveals that (GDP per capita Beta = .201, t= 2.751, p&lt;0.01). and Obesity (Beta = .415, t= 5.465, p&lt;0.01)) are potent predictors of COVID 19 Death among patients While GDP growth (Beta = .105, t= 1.524, P&gt;0.01) and Population (Beta = .020, t = .288, p&gt;0.01) are not significant predictors of Covid 19 Death.

Self-Assessed Digital Competences of Romanian Teachers During the COVID-19 Pandemic.

Studies on the determinants of school results have shown that they depend largely on the context of learning. Concerning the pandemic, teachers have been forced to find online teaching methods, which leads us to the central issue of this study of whether the effectiveness of online education depends on teachers’ digital skills. Therefore, in this study, we analyzed the perceived digital competences of Romanian pre-tertiary cycle teachers about their professional status, school location, gender, age, taught field, and prior participation in training for online teaching. Using data from 3,419 self-completed questionnaires in an online survey of teachers performed at the beginning of the global lockdown in March 2020, we have built two reliable measures of perceived digital skills, namely the Self-Assessed Multimedia and Online Skills Score (SMOS) and the Self-Assessed Digital Office Skills Score (SDOS), which were the dependent variables in our study. Hierarchical linear regressions were used to test the hypotheses regarding the variations of dependent variables, measuring the two concepts of self-assessed digital skills (SMOS and SDOS). These concepts underlined that both decrease with age and are positively affected by prior attendance at training sessions for online teaching skills and by having ICT and informatics as a taught subject field. However, teachers of all specialties are relatively significantly less skilled in this field. The most important results concern the impact of gender and professional status on the teachers’ self-assessed digital competences. In summary, it appears that self-assessed office digital skills are a specialism demonstrated mainly by female teachers, while multimedia and online skills are perceived by teachers to be a “male” domain. Simultaneously, net of the other variables, a higher status within the teaching profession correlates positively with perceived office digital skills. Lastly, implications for future research, as well as for educational interventions and policies, are discussed.