Search results for: logistic regression analysis

Authors: Karl Peltzer, Supa Pengpid

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Introduction: The aim of this study was to assess suicidal ideation and associated factors in school-going adolescents in the Association of Southeast Asian Nations (ASEAN) Member States. Methods: The analysis included 30284 school children aged 13-15 years from seven ASEAN that participated in the cross-sectional Global School-based Student Health Survey (GSHS) between 2007 and 2013. Results: The overall prevalence of suicidal ideation across seven ASEAN countries (excluding Brunei) was 12.3%, significantly higher in girls (15.1%) than boys (9.3%). Among eight ASEAN countries with the highest prevalence of suicidal ideation was in the Philippines (17.0%) and Vietnam (16.9%) and the lowest in Myanmar (1.1%) and Indonesia (4.2%). In multivariate logistic regression analysis, female gender, older age (14 or 15 years), living in a low income or lower middle income country, having no friends, loneliness, bullying victimization, having been in a physical fight in the past 12 months, lack of parental or guardian support, tobacco use and having a history of ever got drunk were associated with suicidal ideatiion. Conclusion: Different rates of suicidal ideation were observed in ASEAN member states. Several risk factors for suicidal ideation were identified which can help guide preventive efforts.

Keywords: adolesents, ASEAN, correlates, suicidal behaviour

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Authors: J. Satya Eswari, Ch. Venkateswarlu

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The formulation of a commercial pharmaceutical product involves several composition factors and response characteristics. When the formulation requires to satisfy multiple response characteristics which are conflicting, an optimal solution requires the need for an efficient multiobjective optimization technique. In this work, a regression is combined with a non-dominated sorting differential evolution (NSDE) involving Naïve & Slow and ε constraint techniques to derive different multiobjective optimization strategies, which are then evaluated by means of a trapidil pharmaceutical formulation. The analysis of the results show the effectiveness of the strategy that combines the regression model and NSDE with the integration of both Naïve & Slow and ε constraint techniques for Pareto optimization of trapidil formulation. With this strategy, the optimal formulation at pH=6.8 is obtained with the decision variables of micro crystalline cellulose, hydroxypropyl methylcellulose and compression pressure. The corresponding response characteristics of rate constant and release order are also noted down. The comparison of these results with the experimental data and with those of other multiple regression model based multiobjective evolutionary optimization strategies signify the better performance for optimal trapidil formulation.

Keywords: pharmaceutical formulation, multiple regression model, response surface method, radial basis function network, differential evolution, multiobjective optimization

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Authors: Luisa Windhagen, Atsumi Hirose, Alex Bottle

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Global urbanization has resulted in the expansion of slums, leaving over 10 million Tanzanians in urban poverty and at risk of poor health. Whilst rural residence has historically been associated with an increased risk of adverse pregnancy outcomes, recent studies found higher perinatal mortality rates in urban Tanzania. This study aims to understand to what extent slum neighborhoods may account for the spatial disparities seen in Tanzania. We generated a slum indicator based on UN-HABITAT criteria to identify slum clusters within the 2015-2016 Tanzania Demographic and Health Survey. Descriptive statistics, disaggregated by urban slum, urban non-slum, and rural areas, were produced. Simple and multivariable logistic regression examined the association between cluster residence type and neonatal mortality and stillbirth. For neonatal mortality, we additionally built a multilevel logistic regression model, adjusting for confounding and clustering. The neonatal mortality ratio was highest in slums (38.3 deaths per 1000 live births); the stillbirth rate was three times higher in slums (32.4 deaths per 1000 births) than in urban non-slums. Neonatal death was more likely to occur in slums than in urban non-slums (aOR=2.15, 95% CI=1.02-4.56) and rural areas (aOR=1.78, 95% CI=1.15-2.77). Odds of stillbirth were over five times higher among rural than urban non-slum residents (aOR=5.25, 95% CI=1.31-20.96). The results suggest that slums contribute to the urban disadvantage in Tanzanian neonatal health. Higher neonatal mortality in slums may be attributable to lack of education, lower socioeconomic status, poor healthcare access, and environmental factors, including indoor and outdoor air pollution and unsanitary conditions from inadequate housing. However, further research is required to ascertain specific causalities as well as significant associations between residence type and other pregnancy outcomes. The high neonatal mortality, stillbirth, and slum formation rates in Tanzania signify that considerable change is necessary to achieve international goals for health and human settlements. Disparities in access to adequate housing, safe water and sanitation, high standard antenatal, intrapartum, and neonatal care, and maternal education need to urgently be addressed. This study highlights the spatial neonatal mortality shift from rural settings to urban informal settlements in Tanzania. Importantly, other low- and middle-income countries experiencing overwhelming urbanization and slum expansion may also be at risk of a reversing trend in residential neonatal health differences.

Keywords: urban health, slum residence, neonatal mortality, stillbirth, global urbanisation

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Authors: Fatemeh Rahmani, Navid Nadimi, Vahid Khalifeh

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In the near future, cities with medium populations will face traffic congestion, air pollution, high fuel consumption, and noise pollution. It is possible to improve the sustainability of cities by utilizing public transportation. A study of the factors that influence citizens' bus usage in medium-sized cities is presented in this paper. For this purpose, Kerman's citizens were surveyed online. The model was based on a binary logistic regression. A descriptive analysis revealed that simple measures like renewing the fleet, upgrading the stations, establishing a schedule program, and cleaning the buses could improve passenger satisfaction. In addition, the modeling results showed that future traffic congestion can be prevented by implementing road and parking lot pricing plans. Further, as the number and length of trips increases, the probability of citizens taking the bus increases. In conclusion, Kerman's bus system is both secure and fast, but these two characteristics can be improved to increase bus ridership.

Keywords: sustainability, transportation, bus, congestion, satisfaction

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Authors: S. Dey, T. Mukhopadhyay, S. Adhikari

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This paper presents an exhaustive comparative investigation on sampling techniques of polynomial regression model based stochastic natural frequency of composite plates. Both individual and combined variations of input parameters are considered to map the computational time and accuracy of each modelling techniques. The finite element formulation of composites is capable to deal with both correlated and uncorrelated random input variables such as fibre parameters and material properties. The results obtained by Polynomial regression (PR) using different sampling techniques are compared. Depending on the suitability of sampling techniques such as 2k Factorial designs, Central composite design, A-Optimal design, I-Optimal, D-Optimal, Taguchi’s orthogonal array design, Box-Behnken design, Latin hypercube sampling, sobol sequence are illustrated. Statistical analysis of the first three natural frequencies is presented to compare the results and its performance.

Keywords: composite plate, natural frequency, polynomial regression model, sampling technique, uncertainty quantification

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Authors: Dileep Kumar Gupta, Rajendra Prasad, Pradeep Kumar, Varun Narayan Mishra, Ajeet Kumar Vishwakarma, Prashant K. Srivastava

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An approach was evaluated for the retrieval of soil moisture of bare soil surface using bistatic scatterometer data in the angular range of 200 to 700 at VV- and HH- polarization. The microwave data was acquired by specially designed X-band (10 GHz) bistatic scatterometer. The linear regression analysis was done between scattering coefficients and soil moisture content to select the suitable incidence angle for retrieval of soil moisture content. The 250 incidence angle was found more suitable. The support vector regression analysis was used to approximate the function described by the input-output relationship between the scattering coefficient and corresponding measured values of the soil moisture content. The performance of support vector regression algorithm was evaluated by comparing the observed and the estimated soil moisture content by statistical performance indices %Bias, root mean squared error (RMSE) and Nash-Sutcliffe Efficiency (NSE). The values of %Bias, root mean squared error (RMSE) and Nash-Sutcliffe Efficiency (NSE) were found 2.9451, 1.0986, and 0.9214, respectively at HH-polarization. At VV- polarization, the values of %Bias, root mean squared error (RMSE) and Nash-Sutcliffe Efficiency (NSE) were found 3.6186, 0.9373, and 0.9428, respectively.

Keywords: bistatic scatterometer, soil moisture, support vector regression, RMSE, %Bias, NSE

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Authors: Chokri Slim

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The objective of this research is attempting to respond to this question: Is there a significant difference between the regression model and statistical learning models in predicting credit risk management? A Multiple Linear Regression (MLR) model was compared with neural networks including Multi-Layer Perceptron (MLP), and a Support vector regression (SVR). The population of this study includes 50 listed Banks in Tunis Stock Exchange (TSE) market from 2000 to 2016. Firstly, we show the factors that have significant effect on the quality of loan portfolios of banks in Tunisia. Secondly, it attempts to establish that the systematic use of objective techniques and methods designed to apprehend and assess risk when considering applications for granting credit, has a positive effect on the quality of loan portfolios of banks and their future collectability. Finally, we will try to show that the bank governance has an impact on the choice of methods and techniques for analyzing and measuring the risks inherent in the banking business, including the risk of non-repayment. The results of empirical tests confirm our claims.

Keywords: credit risk management, multiple linear regression, principal components analysis, artificial neural networks, support vector machines

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Authors: Kibret Berhanu Getinet

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Off-farm income diversification by farm rural households has gained the attention of researchers and policymakers due to the fact that agriculture failed to meet the needs of people in developing countries like Ethiopia. The objective of this study was to investigate income diversification strategies into off-farm activities among rural households in Hawassa Zuria Woreda, Sidama National Regional State, Ethiopia. The study used primary and secondary data sources for the primary data collection questionnaire employed as a data collection instrument. A multistage sampling technique was used to collect data from a total of 197 sample households from four kebeles of the study area. Descriptive statistics, as well as econometrics methods of data analysis, were employed. The descriptive statistics result indicates that the majority of sample rural households (68.53 %) have engaged in off-farm income diversification activities while the remaining 31.47% of households did not participate in the diversification in the study area. The choice of participants among the strategies indicates that 6.60% of respondents participated in off-farm wage employment, 30.46% participated in off-farm self-employment, and about 31.47% of them participated in both off-farm wage employment. The study revealed that the share of off-farm income in total annual earnings of households was about 48.457%, and thus, the off-farm diversification significantly contributes to the rural household income. Moreover, binary and multinomial logistic regression models were employed to identify factors that affect the participation and the choices of the off-farm income diversification strategies, respectively. The binary logit model result indicated that agro-ecological zone, education status of the households, available technical skills of the household, household saving, total livestock owned by the households, access to electricity, road access and being married of household head were significant and positively affected the chance of diversification in off-farm activities while the on-farm income of households is negatively affected the chance of diversification. Similarly, the multinomial logistic regression model estimate revealed that agroecological zone, on-farm income, available technical skills, household savings, and access to electricity are positively related and significantly influenced the household’s choice of employment into off-farm wage employment. The off-farm self-employment diversification choice is significantly influenced by on-farm income, available technical skills, household savings, total livestock owned, and access to electricity. Moreover, the result showed that the factors that affect the choice of farm households to engage in both off-farm wage and self-employment are ecological zone, education status, on-farm income, available technical skills, household own saving, market access, total livestock owned, access to electricity and road access. Thus, due attention should be given to addressing the demographic, socio-economic, and institutional constraints to strengthen off-farm income diversification strategies to improve the income of rural households.

Keywords: off-farm, incoem, diversification, logit model

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Authors: Priya Kedia, Kiranmoy Das

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There is a rich literature on variable selection in regression setting. However, most of these methods assume normality for the response variable under consideration for implementing the methodology and establishing the statistical properties of the estimates. In many real applications, the distribution for the response variable may be non-Gaussian, and one might be interested in finding the best subset of covariates at some predetermined quantile level. We develop dynamic Bayesian approach for variable selection in quantile regression framework. We use a zero-inflated mixture prior for the regression coefficients, and consider the asymmetric Laplace distribution for the response variable for modeling different quantiles of its distribution. An efficient Gibbs sampler is developed for our computation. Our proposed approach is assessed through extensive simulation studies, and real application of the proposed approach is also illustrated. We consider the data from health and retirement study conducted by the University of Michigan, and select the important predictors when the outcome of interest is out-of-pocket medical cost, which is considered as an important measure for financial risk. Our analysis finds important predictors at different quantiles of the outcome, and thus enhance our understanding on the effects of different predictors on the out-of-pocket medical cost.

Keywords: variable selection, quantile regression, Gibbs sampler, asymmetric Laplace distribution

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Authors: M. T. Alodat

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In this paper, we propose a generalization to the Gaussian process regression(GPR) model called the extended skew Gaussian process for regression(ESGPr) model. The ESGPR model works better than the GPR model when the errors are skewed. We derive the predictive distribution for the ESGPR model at a new input. Also we apply the ESGPR model to FOREX data and we find that it fits the Forex data better than the GPR model.

Keywords: extended skew normal distribution, Gaussian process for regression, predictive distribution, ESGPr model

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Authors: Muller Salah Zaky Toudry

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The complexity arises in defining the development great due to its notion, based on inherent market situations and their requirements, the diverse stakeholders itself and their desired output. An quantitative survey based totally approach was adopted in this optimistic examine. A questionnaire-primarily based survey was performed for the assessment of production fine belief and expectations within the context of excellent development technique. The survey feedback of experts of the leading creation corporations/companies of Pakistan production industry have been analyzed. The monetary ability, organizational shape, and production revel in of the construction companies shaped basis for their selection. The great belief become located to be venture-scope-orientated and taken into consideration as an extra cost for a production assignment. Any excellent improvement technique changed into expected to maximize the profit for the employer, via enhancing the productiveness in a creation project. The look at is beneficial for the construction specialists to evaluate the prevailing creation great perception and the expectations from implementation of any pleasant improvement approach in production projects.

Keywords: correlation analysis, lean construction tools, lean construction, logistic regression analysis, risk management, safety construction quality, expectation, improvement, perception client loyalty, NPS, pre-construction, schedule reduction

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Authors: Cihan Çetinkaya, Eren Özceylan, Kerem Elibal

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This paper presents a solution method for selection of production facility. The motivation has been taken from a real life case, an automotive subcontractor which has two production facilities at different cities and parts. The problem is to decide which part(s) should be produced at which facility. To the best of our knowledge, until this study, there was no scientific approach about this problem at the firm and decisions were being given intuitively. In this study, some logistic cost parameters have been defined and with these parameters a mathematical model has been constructed. Defined and collected cost parameters are handling cost of parts, shipment cost of parts and shipment cost of welding fixtures. Constructed multi-objective mathematical model aims to minimize these costs while aims to balance the workload between two locations. Results showed that defined model can give optimum solutions in reasonable computing times. Also, this result gave encouragement to develop the model with addition of new logistic cost parameters.

Keywords: automotive subcontract, facility assignment, logistic costs, multi-objective models

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Authors: Yan Wang, Yasushi Asami, Yukio Sadahiro

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In urban context, urban nodes such as amenity or hazard will certainly affect house price, while classic hedonic analysis will employ distance variables measured from each urban nodes. However, effects from distances to facilities on house prices generally do not represent the true price of the property. Distance variables measured on the same surface are suffering a problem called multicollinearity, which is usually presented as magnitude variance and mean value in regression, errors caused by instability. In this paper, we provided a theoretical framework to identify and gather the data with less bias, and also provided specific sampling method on locating the sample region to avoid the spatial multicollinerity problem in three distance variable’s case.

Keywords: hedonic regression, urban node, distance variables, multicollinerity, collinearity

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Authors: Mahtab Davari, Charles Edward Okon, Somayeh Aghanavesi

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Every institute of learning is usually interested in the performance of enrolled students. The level of these performances determines the approach an institute of study may adopt in rendering academic services. The focus of this paper is to evaluate students' academic performance in given courses of study using machine learning methods. This study evaluated various supervised machine learning classification algorithms such as Logistic Regression (LR), Support Vector Machine, Random Forest, Decision Tree, K-Nearest Neighbors, Linear Discriminant Analysis, and Quadratic Discriminant Analysis, using selected features to predict study performance. The accuracy, precision, recall, and F1 score obtained from a 5-Fold Cross-Validation were used to determine the best classification algorithm to predict students’ performances. SVM (using a linear kernel), LDA, and LR were identified as the best-performing machine learning methods. Also, using the LR model, this study identified students' educational habits such as reading and paying attention in class as strong determinants for a student to have an above-average performance. Other important features include the academic history of the student and work. Demographic factors such as age, gender, high school graduation, etc., had no significant effect on a student's performance.

Keywords: student performance, supervised machine learning, classification, cross-validation, prediction

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Authors: Lina Wu, Wenyi Lu, Ye Li

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Regarding heavy video game players for boys and super online chat lovers for girls as a symbolic phrase in the current adolescent culture, this project of data analysis verifies the displacement effect on deteriorating mathematics performance. To evaluate correlation or regression coefficients between a factor of playing video games or chatting online and mathematics performance compared with other factors, we use multivariate analysis technique and take gender difference into account. We find the most important reason for the negative sign of the displacement effect on mathematics performance due to students’ poor academic background. Statistical analysis methods in this project could be applied to study internet users’ academic performance from the high school education to the college education.

Keywords: correlation coefficients, displacement effect, multivariate analysis technique, regression coefficients

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Authors: Kajingulu Malandala, Ranganai Edmore

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The asymmetric Laplace distribution (ADL) is commonly used as the likelihood function of the Bayesian quantile regression, and it offers different families of likelihood method for quantile regression. Notwithstanding their popularity and practicality, ADL is not smooth and thus making it difficult to maximize its likelihood. Furthermore, Bayesian inference is time consuming and the selection of likelihood may mislead the inference, as the Bayes theorem does not automatically establish the posterior inference. Furthermore, ADL does not account for greater skewness and Kurtosis. This paper develops a new aspect of quantile regression approach for count data based on inverse of the cumulative density function of the Poisson, binomial and Delaporte distributions using the integrated nested Laplace Approximations. Our result validates the benefit of using the integrated nested Laplace Approximations and support the approach for count data.

Keywords: quantile regression, Delaporte distribution, count data, integrated nested Laplace approximation

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Authors: Rafael Zhindon Almeida

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Surface water quality is an important concern for the evaluation and prediction of water quality conditions. The objective of this study is to develop a statistical model that can accurately predict the water quality of the El Macho estuary in the city of Machala, El Oro province. The methodology employed in this study is of a basic type that involves a thorough search for theoretical foundations to improve the understanding of statistical modeling for water quality analysis. The research design is correlational, using a multivariate statistical model involving multiple linear regression and principal component analysis. The results indicate that water quality parameters such as fecal coliforms, biochemical oxygen demand, chemical oxygen demand, iron and dissolved oxygen exceed the allowable limits. The water of the El Macho estuary is determined to be below the required water quality criteria. The multiple linear regression model, based on chemical oxygen demand and total dissolved solids, explains 99.9% of the variance of the dependent variable. In addition, principal component analysis shows that the model has an explanatory power of 86.242%. The study successfully developed a statistical model to evaluate the water quality of the El Macho estuary. The estuary did not meet the water quality criteria, with several parameters exceeding the allowable limits. The multiple linear regression model and principal component analysis provide valuable information on the relationship between the various water quality parameters. The findings of the study emphasize the need for immediate action to improve the water quality of the El Macho estuary to ensure the preservation and protection of this valuable natural resource.

Keywords: statistical modeling, water quality, multiple linear regression, principal components, statistical models

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Authors: Saiakhil Chilaka

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Understanding the demographic factors that influence mortality from respiratory diseases like COVID-19, pneumonia, and influenza is crucial for informing public health policy. This study utilizes multivariable regression models to assess the relationship between state, sex, and age group on deaths from these diseases using U.S. data from 2020 to 2023. The analysis reveals that age and sex play significant roles in mortality, while state-level variations are minimal. Although the model’s low R-squared values indicate that additional factors are at play, this paper discusses how these findings, in light of recent research, can inform future public health policy, resource allocation, and intervention strategies.

Keywords: COVID-19, multivariable regression, public policy, data science

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Authors: Yina F. Muñoz, Alexander Paz, Hanns De La Fuente-Mella, Joaquin V. Fariña, Guilherme M. Sales

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The primary approach for estimating bridge deterioration uses Markov-chain models and regression analysis. Traditional Markov models have problems in estimating the required transition probabilities when a small sample size is used. Often, reliable bridge data have not been taken over large periods, thus large data sets may not be available. This study presents an important change to the traditional approach by using the Small Data Method to estimate transition probabilities. The results illustrate that the Small Data Method and traditional approach both provide similar estimates; however, the former method provides results that are more conservative. That is, Small Data Method provided slightly lower than expected bridge condition ratings compared with the traditional approach. Considering that bridges are critical infrastructures, the Small Data Method, which uses more information and provides more conservative estimates, may be more appropriate when the available sample size is small. In addition, regression analysis was used to calculate bridge deterioration. Condition ratings were determined for bridge groups, and the best regression model was selected for each group. The results obtained were very similar to those obtained when using Markov chains; however, it is desirable to use more data for better results.

Keywords: concrete bridges, deterioration, Markov chains, probability matrix

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Authors: E. Tchandao Mangamana, V. Cariou, E. Vigneau, R. Glele Kakai, E. M. Qannari

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A new definition of a latent variable associated with a dataset makes it possible to propose variants of the PLS2 regression and the multi-block PLS (MB-PLS). We shall refer to these variants as Rd-PLS regression and Rd-MB-PLS respectively because they are inspired by both Redundancy analysis and PLS regression. Usually, a latent variable t associated with a dataset Z is defined as a linear combination of the variables of Z with the constraint that the length of the loading weights vector equals 1. Formally, t=Zw with ‖w‖=1. Denoting by Z' the transpose of Z, we define herein, a latent variable by t=ZZ’q with the constraint that the auxiliary variable q has a norm equal to 1. This new definition of a latent variable entails that, as previously, t is a linear combination of the variables in Z and, in addition, the loading vector w=Z’q is constrained to be a linear combination of the rows of Z. More importantly, t could be interpreted as a kind of projection of the auxiliary variable q onto the space generated by the variables in Z, since it is collinear to the first PLS1 component of q onto Z. Consider the situation in which we aim to predict a dataset Y from another dataset X. These two datasets relate to the same individuals and are assumed to be centered. Let us consider a latent variable u=YY’q to which we associate the variable t= XX’YY’q. Rd-PLS consists in seeking q (and therefore u and t) so that the covariance between t and u is maximum. The solution to this problem is straightforward and consists in setting q to the eigenvector of YY’XX’YY’ associated with the largest eigenvalue. For the determination of higher order components, we deflate X and Y with respect to the latent variable t. Extending Rd-PLS to the context of multi-block data is relatively easy. Starting from a latent variable u=YY’q, we consider its ‘projection’ on the space generated by the variables of each block Xk (k=1, ..., K) namely, tk= XkXk'YY’q. Thereafter, Rd-MB-PLS seeks q in order to maximize the average of the covariances of u with tk (k=1, ..., K). The solution to this problem is given by q, eigenvector of YY’XX’YY’, where X is the dataset obtained by horizontally merging datasets Xk (k=1, ..., K). For the determination of latent variables of order higher than 1, we use a deflation of Y and Xk with respect to the variable t= XX’YY’q. In the same vein, extending Rd-MB-PLS to the path modeling setting is straightforward. Methods are illustrated on the basis of case studies and performance of Rd-PLS and Rd-MB-PLS in terms of prediction is compared to that of PLS2 and MB-PLS.

Keywords: multiblock data analysis, partial least squares regression, path modeling, redundancy analysis

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Authors: Vipul M. Patel, Hemantkumar B. Mehta

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Technological innovations in electronic world demand novel, compact, simple in design, less costly and effective heat transfer devices. Closed Loop Pulsating Heat Pipe (CLPHP) is a passive phase change heat transfer device and has potential to transfer heat quickly and efficiently from source to sink. Thermal performance of a CLPHP is governed by various parameters such as number of U-turns, orientations, input heat, working fluids and filling ratio. The present paper is an attempt to predict the thermal performance of a CLPHP using Artificial Neural Network (ANN). Filling ratio and heat input are considered as input parameters while thermal resistance is set as target parameter. Types of neural networks considered in the present paper are radial basis, generalized regression, linear layer, cascade forward back propagation, feed forward back propagation; feed forward distributed time delay, layer recurrent and Elman back propagation. Linear, logistic sigmoid, tangent sigmoid and Radial Basis Gaussian Function are used as transfer functions. Prediction accuracy is measured based on the experimental data reported by the researchers in open literature as a function of Mean Absolute Relative Deviation (MARD). The prediction of a generalized regression ANN model with spread constant of 4.8 is found in agreement with the experimental data for MARD in the range of ±1.81%.

Keywords: ANN models, CLPHP, filling ratio, generalized regression, spread constant

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Authors: V. Rekha, Rama Pal

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The study examines the degree and magnitude of wealth-based inequalities in child health and its determinants in India. Despite making strides in economic growth, India has failed to secure a better nutritional status for all the children. The country currently faces the double burden of malnutrition as well as the problems of overweight and obesity. Child malnutrition, obesity, unsafe water, sanitation among others are identified as the risk factors for Non-Communicable Diseases (NCDs). Eliminating malnutrition in all its forms will catalyse improved health and economic outcomes. The assessment of the distributive dimension of child health across various segments of the population is essential for effective policy intervention. The study utilises the fourth round of District Level Health Survey for 2012-13 to analyse the inequalities among children in the age group 0-14 years in Maharashtra, a state in the western region of India with a population of 11.24 crores which constitutes 9.3 percent of the total population of India. The study considers the extent of health inequality by state, districts, sector, age-groups, and gender. The z-scores of four child health outcome variables are computed to assess the nutritional status of pre-school and school children using WHO reference. The descriptive statistics, concentration curves, concentration indices, correlation matrix, logistic regression have been used to analyse the data. The results indicate that magnitude of inequality is higher in Maharashtra and child health inequalities manifest primarily among the weaker sections of society. The concentration curves show that there exists a pro-poor inequality in child malnutrition measured by stunting, wasting, underweight, anaemia and a pro-rich overweight inequality. The inequalities in anaemia are observably lower due to the widespread prevalence. Rural areas exhibit a higher incidence of malnutrition, but greater inequality is observed in the urban areas. Overall, the wealth-based inequalities do not vary significantly between age groups. It appears that there is no gender discrimination at the state level. Further, rural-urban differentials in gender show that boys from the rural area and girls living in the urban region experience higher disparities in health. The relative distribution of undernutrition across districts in Maharashtra reveals that malnutrition is rampant and considerable heterogeneity also exists. A negative correlation is established between malnutrition prevalence and human development indicators. The findings of logistic regression analysis reveal that lower economic status of the household is associated with a higher probability of being malnourished. The study recognises household wealth, education of the parent, child gender, and household size as factors significantly related to malnutrition. The results suggest that among the supply-side variables, child-oriented government programmes might be beneficial in tackling nutrition deficit. In order to bridge the health inequality gap, the government needs to target the schemes better and should expand the coverage of services.

Keywords: child health, inequality, malnutrition, obesity

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Authors: Rutcher M. Lacaza, Stephen Jun V. Villejo

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This paper investigated the patterns of private transfers in the Philippines using the Family Income Expenditure Survey (FIES) 2009, conducted by the Philippine government’s National Statistics Office (NSO) every three years. The paper performed bivariate analysis on net transfers, using the identified determinants for a household to be either a net receiver or a net giver. The household characteristics considered are the following: age, sex, marital status, employment status and educational attainment of the household head, and also size, location, pre-transfer income and the number of employed members of the household. The variables net receiver and net giver are determined by computing the net transfer, subtracting total gifts from total receipts. The receipts are defined as the sum of cash received from abroad, cash received from domestic sources, total gifts received and inheritance. While gifts are defined as the sum of contributions and donations to church and other religious institutions, contributions and donations to other institutions, gifts and contributions to others, and gifts and assistance to private individuals outside the family. Both in kind and in cash transfers are considered in the analysis. It also performed a multiple regression analysis on transfers received and income including other household characteristics to examine the motives for giving transfers – whether altruism or exchanged. It also used the binary logistic regression to estimate the probability of being a net receiver or net giver given the household characteristics. The study revealed that receiving tends to be universal – both the non-poor and the poor benefit although the poor receive substantially less than the non-poor. Regardless of whether households are net receivers or net givers, households in the upper deciles generally give and receive more than those in the lower deciles. It also appears that private transfers may just flow within economic groups. Big amounts of transfers are, therefore, directed to the non-poor and the small amounts go to the poor. This was also supported by the increasing function of gross transfers received and the income of households – the poor receiving less and the non-poor receiving more. This is contrary to the theory that private transfers can help equalize the distribution of income. This suggested that private transfers in the Philippines are not altruistically motivated but exchanged. However, bilateral data on transfers received or given is needed to test this theory directly. The results showed that transfers are much needed by the poor and it is important to understand the nature of private transfers, to ensure that government transfer programs are properly designed and targeted so as to prevent the duplication of private safety nets already present among the non-poor.

Keywords: private transfers, net receiver, net giver, altruism, exchanged.

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Authors: Tshaudi Motsima

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Class attendance is key at all levels of education. At tertiary level many students develop a tendency of not attending all classes without being aware of the repercussions of not attending all classes. It is important for all students to attend all classes as they can receive first-hand information and they can benefit more. The student who attends classes is likely to perform better academically than the student who does not. The aim of this paper is to assess the relationship between class attendance and academic performance of industrial engineering students. The data for this study were collected through the attendance register of students and the other data were accessed from the Integrated Tertiary Software and the Higher Education Data Analyzer Portal. Data analysis was conducted on a sample of 93 students. The results revealed that students with medium predicate scores (OR = 3.8; p = 0.027) and students with low predicate scores (OR = 21.4, p < 0.001) were significantly likely to attend less than 80% of the classes as compared to students with high predicate scores. Students with examination performance of less than 50% were likely to attend less than 80% of classes than students with examination performance of 50% and above, but the differences were not statistically significant (OR = 1.3; p = 0.750).

Keywords: class attendance, examination performance, final outcome, logistic regression

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Authors: M. Ouassaf, S. Belaid

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A series of quinoline derivatives with antimalarial activity were subjected to two-dimensional quantitative structure-activity relationship (2D-QSAR) studies. Three models were implemented using multiple regression linear MLR, a regression partial least squares (PLS), nonlinear regression (MNLR), to see which descriptors are closely related to the activity biologic. We relied on a principal component analysis (PCA). Based on our results, a comparison of the quality of, MLR, PLS, and MNLR models shows that the MNLR (R = 0.914 and R² = 0.835, RCV= 0.853) models have substantially better predictive capability because the MNLR approach gives better results than MLR (R = 0.835 and R² = 0,752, RCV=0.601)), PLS (R = 0.742 and R² = 0.552, RCV=0.550) The model of MNLR gave statistically significant results and showed good stability to data variation in leave-one-out cross-validation. The obtained results suggested that our proposed model MNLR may be useful to predict the biological activity of derivatives of quinoline.

Keywords: antimalarial, quinoline, QSAR, PCA, MLR , MNLR, MLR

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Authors: Claudia García-García, Catalina B. García-García, Román Salmerón-Gómez

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Much economic-environmental research includes the analysis of possible interactions by using Moderated Regression Analysis (MRA), which is a specific application of multiple linear regression analysis. This methodology allows analyzing how the effect of one of the independent variables is moderated by a second independent variable by adding a cross-product term between them as an additional explanatory variable. Due to the very specification of the methodology, the moderated factor is often highly correlated with the constitutive terms. Thus, great multicollinearity problems arise. The appearance of strong multicollinearity in a model has important consequences. Inflated variances of the estimators may appear, there is a tendency to consider non-significant regressors that they probably are together with a very high coefficient of determination, incorrect signs of our coefficients may appear and also the high sensibility of the results to small changes in the dataset. Finally, the high relationship among explanatory variables implies difficulties in fixing the individual effects of each one on the model under study. These consequences shifted to the moderated analysis may imply that it is not worth including an interaction term that may be distorting the model. Thus, it is important to manage the problem with some methodology that allows for obtaining reliable results. After a review of those works that applied the MRA among the ten top journals of the field, it is clear that multicollinearity is mostly disregarded. Less than 15% of the reviewed works take into account potential multicollinearity problems. To overcome the issue, this work studies the possible application of recent methodologies to MRA. Particularly, the raised regression is analyzed. This methodology mitigates collinearity from a geometrical point of view: the collinearity problem arises because the variables under study are very close geometrically, so by separating both variables, the problem can be mitigated. Raise regression maintains the available information and modifies the problematic variables instead of deleting variables, for example. Furthermore, the global characteristics of the initial model are also maintained (sum of squared residuals, estimated variance, coefficient of determination, global significance test and prediction). The proposal is implemented to data from countries of the European Union during the last year available regarding greenhouse gas emissions, per capita GDP and a dummy variable that represents the topography of the country. The use of a dummy variable as the moderator is a special variant of MRA, sometimes called “subgroup regression analysis.” The main conclusion of this work is that applying new techniques to the field can improve in a substantial way the results of the analysis. Particularly, the use of raised regression mitigates great multicollinearity problems, so the researcher is able to rely on the interaction term when interpreting the results of a particular study.

Keywords: multicollinearity, MRA, interaction, raise

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Authors: Nutcha Taneepanichskul, Helen C. Hailes, Mark Miodownik

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Plastic waste has emerged as a critical global environmental challenge, primarily driven by the prevalent use of conventional plastics derived from petrochemical refining and manufacturing processes in modern packaging. While these plastics serve vital functions, their persistence in the environment post-disposal poses significant threats to ecosystems. Addressing this issue necessitates approaches, one of which involves the development of biodegradable plastics designed to degrade under controlled conditions, such as industrial composting facilities. It is imperative to note that compostable plastics are engineered for degradation within specific environments and are not suited for uncontrolled settings, including natural landscapes and aquatic ecosystems. The full benefits of compostable packaging are realized when subjected to industrial composting, preventing environmental contamination and waste stream pollution. Therefore, effective sorting technologies are essential to enhance composting rates for these materials and diminish the risk of contaminating recycling streams. In this study, it leverage hyperspectral imaging technology (HSI) coupled with advanced machine learning algorithms to accurately identify various types of plastics, encompassing conventional variants like Polyethylene terephthalate (PET), Polypropylene (PP), Low density polyethylene (LDPE), High density polyethylene (HDPE) and biodegradable alternatives such as Polybutylene adipate terephthalate (PBAT), Polylactic acid (PLA), and Polyhydroxyalkanoates (PHA). The dataset is partitioned into three subsets: a training dataset comprising uncontaminated conventional and biodegradable plastics, a validation dataset encompassing contaminated plastics of both types, and a testing dataset featuring real-world packaging items in both pristine and contaminated states. Five distinct machine learning algorithms, namely Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Logistic Regression, and Decision Tree Algorithm, were developed and evaluated for their classification performance. Remarkably, the Logistic Regression and CNN model exhibited the most promising outcomes, achieving a perfect accuracy rate of 100% for the training and validation datasets. Notably, the testing dataset yielded an accuracy exceeding 80%. The successful implementation of this sorting technology within recycling and composting facilities holds the potential to significantly elevate recycling and composting rates. As a result, the envisioned circular economy for plastics can be established, thereby offering a viable solution to mitigate plastic pollution.

Keywords: biodegradable plastics, sorting technology, hyperspectral imaging technology, machine learning algorithms

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Authors: My Ha, Suwei Lin, Huiying Zou

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This study aimed at investigating the association between teacher support and academic resilience in a developing country. Using the data from PISA 2018 Student Questionnaire and Cognitive Tests, the study provided evidence of the significant impact teacher support had on reading literacy among 15-year-old students from low socio-economic status (SES) homes in Vietnam. From a total of 5773 Vietnamese participants from all backgrounds, a sample of 1765 disadvantaged students was drawn for analysis. As a result, 32 percent of the low SES sample was identified as resilient. Through their response to the PISA items regarding the frequency of support they received from teachers, the result of Latent Class Analysis (LCA) divides children into three subgroups: High Support (74.6%), Fair Support (21.6%), and Low Support (3.8%). The high support group reported the highest proportion of resilient students. Meanwhile, the low support group scored the lowest mean on reading test and had the lowest rate of resilience. Also, as the level of support increases, reading achievement becomes less dependent on socioeconomic status, reflected by the decrease in both the slope and magnitude of their correlation. Logistic regression revealed that 1 unit increase in standardized teacher support would lead to an increase of 29.1 percent in the odds of a student becoming resilient. The study emphasizes the role of supportive teachers in promoting resilience, as well as lowering educational inequity in general.

Keywords: academic resilience, disadvantaged students, teacher support, inequity, PISA

Procedia PDF Downloads 90

Authors: Iman Fakhrian, Ali Salehi Manzari

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Consistent Profitability is one of the most important priorities in manufacturing companies. One of the fundamental factors for increasing the companies profitability is cost management. Isfahan's mobarakeh steel company is one of the largest producers of the slab product grades in the middle east. Raw material cost constitutes about 70% of the company's expenditures. The costs of the ferro alloys have a remarkable contribution of the raw material costs. This research aims to determine the ferro alloys which have significant effect on the variability of the standard cost of the slab product grades. Used data in this study were collected from standard costing system of isfahan's mobarakeh steel company in 2022. The results of conducting the regression analysis model show that expense items: 03020, 03045, 03125, 03130 and 03150 have dominant role in variability of the standard cost of the slab product grades. In other words, the mentioned ferro alloys have noticeable and significant role in variability of the standard cost of the slab product grades.

Keywords: consistent profitability, ferro alloys, slab product grades, regression analysis

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Authors: Kang-Mo Jung

Abstract:

The use of regularization for statistical methods has become popular. The least absolute shrinkage and selection operator (LASSO) framework has become the standard tool for sparse regression. However, it is well known that the LASSO is sensitive to outliers or leverage points. We consider a new robust estimation which is composed of the weighted loss function of the pairwise difference of residuals and the adaptive penalty function regulating the tuning parameter for each variable. Rank regression is resistant to regression outliers, but not to leverage points. By adopting a weighted loss function, the proposed method is robust to leverage points of the predictor variable. Furthermore, the adaptive penalty function gives us good statistical properties in variable selection such as oracle property and consistency. We develop an efficient algorithm to compute the proposed estimator using basic functions in program R. We used an optimal tuning parameter based on the Bayesian information criterion (BIC). Numerical simulation shows that the proposed estimator is effective for analyzing real data set and contaminated data.

Keywords: adaptive penalty function, robust penalized regression, variable selection, weighted rank regression

Procedia PDF Downloads 474