scholarly journals Regression Analysis of Influencing Factors of Public Education in Hungary

2016 ◽  
Vol 4 (1) ◽  
pp. 67-84 ◽  
Author(s):  
Katalin Vér Gáspár ◽  
Attila Madaras ◽  
József Varga
Keyword(s):  
Public Education ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
English Language ◽  
Quality Of Education ◽  
Explanatory Variables ◽  
Average Value ◽  
Educational Spending

Abstract The education system in Hungary has been greatly criticized in the last decades regarding the standards and quality of education and its ignorance towards labour market demands. The present study focuses on factors affecting the quality of education. The first part of the research analyses the relationship between public education and competitiveness in Hungary. In the second part of the research, with the help of the linear regression model and of other statistical and mathematical tools, we tried to identify those explanatory variables which influence and mostly determine the quality of public education. The quality of education was chosen as the dependent variable of the model. Based on the data of competency measurements in Hungary, we were able to identify two explanatory variables that would also highly satisfy the goodness of fit of the linear regression model. The educational funding rates (GDP-proportionate educational spending rate) and the number of students learning English language turned out to be the two significant explanatory variables. Results show that increasing the GDP-proportionate educational spending rate with only one per cent increases the average value of competency measures with 10.9571 points without any other variable changes. Also increasing the number of English language learners with one person increases the average value with 0.000177253 points with other variables remaining the same.

Physico-Chemical Analysis of Groundwater and Effect of Climatic Change in Angul-Talcher Area

2018 ◽  
Vol 6 (12) ◽  
pp. 26
Author(s):  
Aliva Bera ◽  
D.P. Satapathy
Keyword(s):  
Water Resources ◽  
Linear Regression ◽  
Regression Model ◽  
Climatic Change ◽  
Linear Regression Model ◽  
Ann Model ◽  
Quality Of Water ◽  
Physico Chemical ◽  
Simulation Based

In this paper, the linear regression model using ANN and the linear regression model using MS Excel were developed to estimate the physico-chemical concentrations in groundwater using pH, EC, TDS, TH, HCO3 as input parameters and Ca, Mg and K as output parameters. A comparison was made which indicated that ANN model had the better ability to estimate the physic-chemical concentrations in groundwater. An analytical survey along with simulation based tests for finding the climatic change and its effect on agriculture and water bodies in Angul-Talcher area is done. The various seasonal parameters such as pH, BOD, COD, TDS,TSS along with heavy elements like Pb, Cd, Zn, Cu, Fe, Mn concentration in water resources has been analyzed. For past 30 years rainfall data has been analyzed and water quality index values has been studied to find normal and abnormal quality of water resources and matlab based simulation has been done for performance analysis. All results has been analyzed and it is found that the condition is stable. 

scholarly journals Quality of Care in Long-Term Hemodialysis Patients in Mexico

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Torres-Díaz JA ◽  
◽  
Gonzalez-Gonzalez JG ◽  
Zúniga-Hernández JA ◽  
Olivo-Gutiérrez MC ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Quality Of Care ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
World Literature ◽  
Logistic Regression Model ◽  
Hospital Admissions ◽  
Cross Sectional

Introduction: The End Stage Renal Disease (ESRD) is one of the leading causes of mortality in Mexico. The quality of care these patients receive remains uncertain. Methods: This is a descriptive, single-center and cross-sectional cohort study. The KDOQI performance measures, hemoglobin level >11 g/dL, blood pressure <140/90 mmHg, serum albumin >4 g/dL and use of arteriovenous fistula of patients with ESRD on hemodialysis were analyzed in a period of a year. The association between mortality and the KDOQI objectives was evaluated with a logistic regression model. A linear regression model was also performed with the number of readmissions. Results: A total of 124 participants were included. Participants were categorized by the number of measures completed. Fourteen (11.3%) of the participants did not meet any of the goals, 51 (41.1%) met one, 43 (34.7%) met two, 11 (8.9%) met three, and 5 (4%) met the four clinical goals analyzed. A mortality of 11.2% was registered. In the logistic regression model, the number of goals met had an OR for mortality of 1.1 (95% CI 0.5-2.8). In the linear regression model, for the number of readmissions, a beta correlation with the number of KDOQI goals met was 0.246 (95% CI -0.872-1.365). Conclusion: The attainment of clinical goals and the mortality rate in our center is similar to that reported in the world literature. Our study did not find a significant association between compliance with clinical guidelines and mortality or the number of hospital admissions in CKD patients on hemodialysis.

scholarly journals Statistical Modelling of the Capital Asset Pricing Model (CAPM)

2018 ◽  
Vol 7 (2) ◽  
pp. 146
Author(s):  
Silvi Qemo ◽  
Eahab Elsaid
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Statistical Modelling ◽  
Expected Returns ◽  
Explanatory Variables ◽  
Bid Ask Spread ◽  
Model Average

The purpose of this study is to derive a multiple linear regression model of the CAPM. More specifically, to test for other potential explanatory variables that can be added to the basic linear regression model for the expected returns on Apple Inc. The following explanatory variables were examined: share volume, outstanding shares, closing bid/ask spread, high/low spread and average spread. Using daily returns of Apple Inc. stock from 2007 till 2014 we were able to create a multiple linear regression model of CAPM that increase the R2 value from the basic linear regression model and enhances the amount of variability in the returns on an asset. This is an important modification that can help better forecast returns on assets.Keywords: CAPM; multiple linear regression model; average spread; variability in the returns

scholarly journals Solving Multicollinearity Problem in Linear Regression Model: The Review Suggests New Idea of Partitioning and Extraction of the Explanatory Variables

2021 ◽  
Vol 2 (1) ◽  
pp. 12-20
Author(s):  
Kayode Ayinde, Olusegun O. Alabi ◽  
Ugochinyere Ihuoma Nwosu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Real Data ◽  
Least Square ◽  
Model Parameters ◽  
Explanatory Variables ◽  
Ordinary Least Square ◽  
Wide Range ◽  
High Level

Multicollinearity has remained a major problem in regression analysis and should be sustainably addressed. Problems associated with multicollinearity are worse when it occurs at high level among regressors. This review revealed that studies on the subject have focused on developing estimators regardless of effect of differences in levels of multicollinearity among regressors. Studies have considered single-estimator and combined-estimator approaches without sustainable solution to multicollinearity problems. The possible influence of partitioning the regressors according to multicollinearity levels and extracting from each group to develop estimators that will estimate the parameters of a linear regression model when multicollinearity occurs is a new econometrics idea and therefore requires attention. The results of new studies should be compared with existing methods namely principal components estimator, partial least squares estimator, ridge regression estimator and the ordinary least square estimators using wide range of criteria by ranking their performances at each level of multicollinearity parameter and sample size. Based on a recent clue in literature, it is possible to develop innovative estimator that will sustainably solve the problem of multicollinearity through partitioning and extraction of explanatory variables approaches and identify situations where the innovative estimator will produce most efficient result of the model parameters. The new estimator should be applied to real data and popularized for use.

scholarly journals Selection of Appropriate Model for Quality of Life Data of Strabismus Patients Despite Censorship

2021 ◽  
Author(s):  
Zahra Ghassemi ◽  
Mehdi Yaseri ◽  
Mostafa Hosseini
Keyword(s):  
Quality Of Life ◽  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Tobit Model ◽  
P Value ◽  
Response Variable

Introduction: Previous studies on the quality of life of strabismus patients have not examined the existence of censoring to express the relation between the response variable and its predictors. Methods & Materials: The information used in this study is a conducted cross-sectional study in 2012. The sample size is 90 children in the age range (4-18) years and with congenital strabismus. We used the RAND Health Insurance Study questionnaire with ten subscales to evaluate the quality of life, which was increased to 11 dimensions by adding some items related to eye alignment concerns introduced by Archer et al. The demographic profile is also recorded by 13 other questions. We have expressed the relationship between the independent and response variables in each of the 11 dimensions of the questionnaire and the overall quality of life score by fitting the multiple linear regression model. Then we fitted the two models of classic Tobit and CLAD, which are for censoring, to all dimensions of the questionnaire. Results: We showed that in fitting the models to the overall quality of life scale variable, the best model is the multiple linear regression. Because the response variable was normal, and there was no censoring (ceiling and floor effect). However, in the depression subscale, due to the high censoring (28.89% of the ceiling effect) and the almost normal distribution of the response variable (p-value of skewness< 0.05), the appropriate model according to the criteria is the classic Tobit (AIC = 546.33). That is, the classic Tobit model is the best alternative to the multiple linear regression model in the presence of censoring. But these conditions did not exist in all variables. In the subscale, there was a severe censoring performance constraint (67.78% of the ceiling effect). When censoring is high, the distribution of the response variable becomes very skewed, and the distribution of response variables deviates drastically from normal. The distribution of the performance constraint variable was very skewed (p-value <0.001). Here the RMSE standard scale for the classic Tobit model was 28.74, which is much higher than the standard scale for the multiple linear regression model (14.23). The best model for the high censoring was CLAD. Conclusion: To use the appropriate statistical method in the analysis, one must look at how the response variable is distributed. The multiple linear regression model is very widely used, but in the presence of censoring, the use of this model gives skewed results. In this case, the classic Tobit model and its derived model, CLAD, are replaced. The nonparametric CLAD model calculates accurate estimates with minimum defaults and censoring.

scholarly journals Finite-Difference Models Application for Short-Term Forecasting of the Natural Resource Potential of the Perm Region

2021 ◽  
Vol 21 (2) ◽  
pp. 154-166
Author(s):  
N.A. Sirotina ◽  
◽  
A.V. Kopoteva ◽  
A.V. Zatonskiy
Keyword(s):  
Linear Regression ◽  
Finite Difference ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Natural Resource ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Resource Potential ◽  
Predicted Values

The article is about a problem of mathematical modeling of the natural resource potential of the Perm Territory by 1st and 2nd order finite-difference models. Such models can obtain better forecasts of complex socio-economic processes in comparison with the traditionally used linear multiple regression models. A high quality model of the natural resource potential with forecast possibi¬lities is one of the necessary conditions for the effective management of the natural resources of the region in order to ensure its sustainable economic development. Purpose of work. Aim of this work is work construction of finite-difference models of a natural resource potential complex indicators and an assessment of their prognostic properties. Materials and methods. Our research is based on Perm region statistical data for the period from 2001 to 2018. A multiple linear regression model is used as a comparison base. The natural resource potential complex indicator is calculated as a weighted sum of particular criteria characterizing the natural resources of the region. First and second order finite difference models are obtained by adding autoregressive terms of the first and second orders, respectively, to the multiple linear regression model. An estimation of the unknown parameters of the equations is carried out by a modified least squares method, which preserves the signs of the coefficients with the factors the same as in the original linear model. At the same time, the selection of explanatory factors and the assessment of the quality of the models are carried out based on the accuracy of the predicted values of the studied indicator. The results of the study. Components and factors of the natural resource potential is obtained, and a procedure for constructing finite-difference models is performed for three different time intervals: 2001–2018, 2001–2008, and 2008–2018. These intervals are chooseen because changes in the methodology for generating statistical data nearly 2008. Discussion and conclusions. The number of calculated predicted values was 18, and only in 4 out of 18 cases (22,2%) their quality is worse than forecasts obtained by the linear multiple model. So proposed modification of the multiple linear regression model with the addition of autoregressive terms makes it possible to improve the forecasting quality of the complex indicator of the natural resource potential of the region and, therefore, to make more effective decisions when managing its level.

scholarly journals Using Bayesian Networks to Predict Long-Term Health-Related Quality of Life and Comorbidity after Bariatric Surgery: A Study Based on the Scandinavian Obesity Surgery Registry

2020 ◽  
Vol 9 (6) ◽  
pp. 1895
Author(s):  
Yang Cao ◽  
Mustafa Raoof ◽  
Eva Szabo ◽  
Johan Ottosson ◽  
Ingmar Näslund
Keyword(s):  
Bariatric Surgery ◽  
Linear Regression ◽  
Bayesian Networks ◽  
Regression Model ◽  
Linear Regression Model ◽  
Health Related Quality ◽  
Related Quality ◽  
Health Related

Previously published literature has identified a few predictors of health-related quality of life (HRQoL) after bariatric surgery. However, performance of the predictive models was not evaluated rigorously using real world data. To find better methods for predicting prognosis in patients after bariatric surgery, we examined performance of the Bayesian networks (BN) method in predicting long-term postoperative HRQoL and compared it with the convolution neural network (CNN) and multivariable logistic regression (MLR). The patients registered in the Scandinavian Obesity Surgery Registry (SOReg) were used for the current study. In total, 6542 patients registered in the SOReg between 2008 and 2012 with complete demographic and preoperative comorbidity information, and preoperative and postoperative 5-year HROoL scores and comorbidities were included in the study. HRQoL was measured using the RAND-SF-36 and the obesity-related problems scale. Thirty-five variables were used for analyses, including 19 predictors and 16 outcome variables. The Gaussian BN (GBN), CNN, and a traditional linear regression model were used for predicting 5-year HRQoL scores, and multinomial discrete BN (DBN) and MLR were used for 5-year comorbidities. Eighty percent of the patients were randomly selected as a training dataset and 20% as a validation dataset. The GBN presented a better performance than the CNN and the linear regression model; it had smaller mean squared errors (MSEs) than those from the CNN and the linear regression model. The MSE of the summary physical scale was only 0.0196 for GBN compared to the 0.0333 seen in the CNN. The DBN showed excellent predictive ability for 5-year type 2 diabetes and dyslipidemia (area under curve (AUC) = 0.942 and 0.917, respectively), good ability for 5-year hypertension and sleep apnea syndrome (AUC = 0.891 and 0.834, respectively), and fair ability for 5-year depression (AUC = 0.750). Bayesian networks provide useful tools for predicting long-term HRQoL and comorbidities in patients after bariatric surgery. The hybrid network that may involve variables from different probability distribution families deserves investigation in the future.

scholarly journals Identification of High Leverage Points in Linear Functional Relationship Model

2020 ◽  
pp. 491-500
Author(s):  
Abu Sayed Md. Al Mamun ◽  
A.H.M. R. Imon ◽  
A. G. Hussin ◽  
Y. Z. Zubairi ◽  
Sohel Rana
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Linear Functional ◽  
Functional Relationship ◽  
Data Set ◽  
Leverage Points ◽  
Explanatory Variables ◽  
Gross Error ◽  
Relationship Model

In a standard linear regression model the explanatory variables, , are considered to be fixed and hence assumed to be free from errors. But in reality, they are variables and consequently can be subjected to errors. In the regression literature there is a clear distinction between outlier in the - space or errors and the outlier in the X-space. The later one is popularly known as high leverage points. If the explanatory variables are subjected to gross error or any unusual pattern we call these observations as outliers in the - space or high leverage points. High leverage points often exert too much influence and consequently become responsible for misleading conclusion about the fitting of a regression model, causing multicollinearity problems, masking and/or swamping of outliers etc. Although a good number of works has been done on the identification of high leverage points in linear regression model, this is still a new and unsolved problem in linear functional relationship model. In this paper, we suggest a procedure for the identification of high leverage points based on deletion of a group of observations. The usefulness of the proposed method for the detection of multiple high leverage points is studied by some well-known data set and Monte Carlo simulations.

scholarly journals A Learn Of Fuzzy Regression Model and its Applications

2019 ◽  
Vol 8 (2) ◽  
pp. 2967-2971
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Fuzzy Numbers ◽  
Fuzzy Regression ◽  
Regression Coefficients ◽  
Fuzzy Linear Regression ◽  
Fuzzy Regression Model ◽  
Fuzzy Linear Regression Model

Many statistics report shown in fuzzy module into clear problems using the centroid system, consequently we will research the usual linear regression model which is modified from the fuzzy linear regression model. The models enter and generate fuzzy numbers, and the regression coefficients are clear numbers. Hybrid algorithms are considered to fit the fuzzy regression model. So that the validity and quality of the suggested methods can be guaranteed. Therefore,the parameter estimation and have an impact on evaluation situated on knowledge deletion. By way of the gain knowledge of example and evaluation with other model, it may be concluded that the model in this paper is utilized without difficulty and better.

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