Comparison Of Regression Analysis Research Articles

Comparison of Regression Analysis and Mathematical Methods for Stature Estimation from the Upper Limb Measurements of the Modern South Indian Population

This study aimed to establish reliable mathematical and regression analysis (RA) methods for estimating stature using upper limb (UL) anthropometric parameters in the modern South Indian (MSI) population. This study was conducted on a sample size (117 men and 97 women) from the MSI population. Dead bodies within 3 hours of death were included in this study, as the study outcome is meant to apply to the dead and dismembered UL parts. Mathematical and linear RA methods were used to derive multiplication factors (MFs) and regression equations (REs) for the estimation of stature from UL measurements (arm length [AL], forearm length, palm length, and middle finger length [MFL]) and assess their prediction accuracy. A statistically significant correlation was found between UL measurements and stature, indicating their potential use for stature estimation. Both genders showed a statistically significant positive relationship between UL measurements and stature. Both MF and RA methods exhibited reasonable goodness of fit, albeit the RA method yielded a closer estimate of the actual stature than the former. The derived RE showed reasonable goodness of fit and precision in both genders, as evidenced by high R2 values and low standard errors. The RA and MF methods slightly over- and under-estimated the actual stature, respectively. The MFL and AL showed the strongest and weakest relationship with the stature. Both methods have practical significance though the precision is higher with RA methods. The MF method is simpler, as it is independent of statistical resources, while the RA method may be more useful in cases where there is a large amount of variation in the data. The outcome of this study has significant implications for the forensic and anthropology fields. The derived RE and MF provide a reliable framework for estimating stature from anthropometric measurements of dismembered UL remnants. This information can be valuable in forensic investigations, unidentified remains cases and archaeological studies.

Comparison of Regression Analysis with Machine Learning Supervised Predictive Model Techniques

The happiness index is a parameter used to measure the level of happiness and well-being of people in a particular country or region. This research aims to determine the factors that contribute to people's happiness. In terms of modelling, this study will compare several regressions modelling using machine learning, including regression trees, random forests and Support Vector Regression (SVR). The SVR model has a minor error value in terms of MSE, RMSE and MAE compared to the other three models. The same thing happened when viewed from the value of R2 that the SVR model has an enormous value. This result indicates that SVR modelling is the best of the four models. A comprehensive policy is needed to increase a country's happiness index.

Interprofessional Team Diagnostics Consultation Improves Health Professions’ Practice Outcomes and Clinical Research

Background: Communication gaps in health services delivery significantly compromise quality in clinical decision making. Information generated by diagnostics professionals’ accounts for much of the objective data in the clinical record and therefore is foundational in clinical decision support. This work describes the Diagnostics Consultation Model©, a diagnostics communications portal, which supports communications among interprofessional teams, providers, and institutions. Aims: Study aims were to develop and validate a workflow prediction index (the complexity index) to assign resolution of consultation requests to diagnostics practitioners with requisite competencies based on an algorithm comprised of characteristics available at the point of consultation initiation. The complexity index functions as the entry into a workflow process directing consultation requests, first, to diagnostics practitioners for investigation and then into communication processes for tracking medical history, patient/consumer clinical information, resolution logic, conclusions, and next step recommendations among all healthcare providers. Methods: Data to develop the complexity index (N = 325 consultation cases) were collected during daily activities in the clinical diagnostics laboratory and describe consultation characteristics important in clinical decision making and available at the point of consultation initiation. The complexity index was developed and validated by comparison of regression analyses using consultation characteristics, i.e., clinical outcomes, available at the point of consultation initiation (development) and after consultation completion (validation). Results: Diagnostics Consultation Model© methodology links communication processes among all providers in all care settings, i.e., community, institutional, and referral, involved in the care paths of individual patient/consumers. This methodology also provides the capability to follow individuals’ medical histories longitudinally and, through regular consultations and practice-based clinical research, to address issues of medical effectiveness, cost efficiency, access, equity, timeliness, safety, and compliance. Conclusion: Implementation of Diagnostics Consultation Model© methods and curriculum in health professions’ daily practice has the potential to change health services delivery by the redistribution of care through interprofessional teams coordinated by standardized communication processes. Employed as a systems approach to individualized patient/consumer care, the Diagnostics Consultation Model© could provide the communications technology and methodology structure for value-based healthcare continuously optimized to address the needs of individuals, populations, and health systems throughout the continuum of care.

Cortical thickness, resting state heart rate, and heart rate variability in female adolescents.

Resting state heart rate variability (HRV) is a psychophysiological marker that has gained increasing research interest, in particular in developmental neuroscience. HRV has been shown to be associated with mental and physical health, beyond simple measures of heart rate (HR) and shows inter- and intraindividual variance across aging. Recently, three studies reported on a positive correlation between resting state HRV and cortical thickness in selected regions of interest (ROIs) in adult samples. Structural thickness, HRV, and HR change during the sensitive period of adolescence. Previously, no study has addressed the structural concomitants of resting HR and HRV in adolescents. Cortical thickness (3-T MRI), HR, and HRV were recorded in 20 healthy, female adolescents (mean age: 15.92 years; SD = 1.06; range: 14-17). In line with existing research in adults, cortical thickness in a number of ROIs was associated with resting state HRV but not HR. The comparison of regression analyses using the Bayes factor revealed evidence for a correlation between HRV and cortical thickness of the bilateral rostral anterior cingulate cortex. However, unlike in adults, greater cortical thickness was associated with reduced HRV in female adolescents. Analyses on HR showed no superior model fit. Results suggest that greater HRV might be beneficial for cortical development during adolescence (cortical thinning). On the other hand, cortical development might determine changes in autonomic nervous system function in adolescents. Future studies are needed to replicate these findings in larger samples including boys and to test these hypotheses in longitudinal designs.

Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines

Accurate electricity consumption forecast has primary importance in the energy planning of the developing countries. During the last decade several new techniques are being used for electricity consumption planning to accurately predict the future electricity consumption needs. Support vector machines (SVMs) and least squares support vector machines (LS-SVMs) are new techniques being adopted for energy consumption forecasting. In this study, the LS-SVM is implemented for the prediction of electricity energy consumption of Turkey. In addition to the traditional regression analysis and artificial neural networks (ANNs) are considered. In the models, gross electricity generation, installed capacity, total subscribership and population are used as independent variables using historical data from 1970 to 2009. Forecasting results are compared using diverse performance criteria in this study with each other. Receiver operating characteristic (ROC) analysis is realized for determining the specificity and sensitivity of the empirical results. The results indicate that the proposed LS-SVM model is an accurate and a quick prediction method.

Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks

This study presents three modeling techniques for the prediction of electricity energy consumption. In addition to the traditional regression analysis, decision tree and neural networks are considered. Model selection is based on the square root of average squared error. In an empirical application to an electricity energy consumption study, the decision tree and neural network models appear to be viable alternatives to the stepwise regression model in understanding energy consumption patterns and predicting energy consumption levels. With the emergence of the data mining approach for predictive modeling, different types of models can be built in a unified platform: to implement various modeling techniques, assess the performance of different models and select the most appropriate model for future prediction.

P.76 Validation of bio-impedance spectroscopy, comparisonof regression analysis with the Hanai mixture theory

P.76 Validation of bio-impedance spectroscopy, comparisonof regression analysis with the Hanai mixture theory

Errata: A Comparison of Regression Analysis and Data Envelopment Analysis as Alternative Methods for Performance Assessments

"Errata: A Comparison of Regression Analysis and Data Envelopment Analysis as Alternative Methods for Performance Assessments." Journal of the Operational Research Society, 45(5), p. 607

Errata: A Comparison of Regression Analysis and Data Envelopment Analysis as Alternative Methods for Performance Assessments

Errata: A Comparison of Regression Analysis and Data Envelopment Analysis as Alternative Methods for Performance Assessments

A Comparison of Regression Analysis and Data Envelopment Analysis as Alternative Methods for Performance Assessments

This paper compares regression analysis and data envelopment analysis as two alternative methods for assessing the comparative performance of homogeneous organizational units such as bank branches or schools. The comparison is restricted to units using a single resource or securing a single output. It focuses on the estimates of relative efficiency, marginal input-output values and target input-output levels that the two methods offer. A set of hypothetical hospitals is used to illustrate the performance of the two methods. It is found that, in general, data envelopment analysis outperforms regression analysis on accuracy of estimates but regression analysis offers greater stability of accuracy.

A Comparison of Regression Analysis and Data Envelopment Analysis as Alternative Methods for Performance Assessments

A Comparison of Regression Analysis and Data Envelopment Analysis as Alternative Methods for Performance Assessments

A Comparison of Regression Analysis and Data Envelopment Analysis as Alternative Methods for Performance Assessments

A Comparison of Regression Analysis and Data Envelopment Analysis as Alternative Methods for Performance Assessments