Regression Coefficients Of Variables Research Articles

Design of Input Signal for System Identification of a Generic Fighter Configuration

This article investigates the design of time-accurate input signals in the angle-of-attack and pitch rate space to identify the aerodynamic characteristics of a generic triple-delta wing configuration at subsonic speeds. Regression models were created from the time history of signal simulations in DoD HPCMP CREATETM-AV/Kestrel software. The input signals included chirp, Schroeder, pseudorandom binary sequence (PRBS), random, and sinusoidal signals. Although similar in structure, the coefficients of these regression models were estimated based on the specific input signals. The signals covered a wide range of angle-of-attack and pitch rate space, resulting in varying regression coefficients for each signal. After creating and validating the models, they were used to predict static aerodynamic data at a wide range of angles of attack but with zero pitch rate. Next, slope coefficients and dynamic derivatives in the pitch direction were estimated from each signal. These predictions were compared with each other as well as with the ONERA wind tunnel data and some CFD calculations from the DLR TAU code provided by the NATO Science and Technology Organization research task group AVT-351. Subsequently, the models were used to predict different pitch oscillations at various mean angles of attack with given amplitudes and frequencies. Again, the model predictions were compared with wind tunnel data. Final predictions involved responses to new signals from different models. A feed-forward neural network was then used to model pressure coefficients on the upper surface of the vehicle at different spanwise sections for each signal and the validated models were used to predict pressure data at different angles of attack. Overall, the models predict similar integrated forces and moments, with the main discrepancies appearing at higher angles of attack. All models failed to predict the stall behavior observed in the measurements and CFD data. Regarding the pressure data, the PRBS signal provided the best accuracy among all the models.

Parenting Styles and Their Impact on the Emotional Intelligence of Adolescents at SMKN 1 Padang

This study aims to evaluate the impact of parenting styles on the emotional intelligence of adolescents at SMKN 1 Padang. Emotional intelligence is the ability to recognize, understand, and manage one's own emotions and the emotions of others. Parenting styles play a fundamental role in the development of emotional intelligence in adolescents. The research method applied is quantitative with a survey approach. Data were collected from questionnaires distributed to 208 students at SMKN 1 Padang. Data analysis was performed using multiple linear regression to examine the impact of parenting styles on emotional intelligence. The analysis results obtained an F value of 92.616 with a probability (P) of 0.000 < 0.005. This means that the regression coefficients of the parenting style variables collectively influence emotional intelligence. This explains that Ha is accepted and H0 is rejected. Overall, parenting styles contribute 57% to the variance in emotional intelligence of adolescents at SMKN 1 Padang. The significance values for authoritarian, authoritative, and permissive parenting styles are 0.004 (<0.05), 0.000 (<0.05), and 0.000 (<0.05), respectively, indicating that these parenting styles have a significant impact on emotional intelligence. The study results show that authoritative parenting style has a positive effect on adolescents' emotional intelligence. Conversely, authoritarian and permissive parenting styles have a negative impact. The influence of authoritative parenting style on emotional intelligence is 52%, permissive parenting style is 3%, and authoritarian parenting style is 2%. This study contributes to understanding how different parenting styles affect adolescents' emotional intelligence. The findings can serve as a reference for parents, educators, and policymakers in developing effective parenting strategies to enhance emotional intelligence and reduce juvenile delinquency.

Cultural omnivorousness in Ukraine: Examining relationships with social indicators via literary preferences among readers

The ability to accumulate extensive tastes, which is known as cultural omnivorousness, can play a significant role in social stratification and marking social groups. The omnivore argument posits that higher-status cultural tastes tend to be broad and diverse (omnivore). In contrast, lower-status cultural tastes are limited and exclusive (univore). This study investigates cultural omnivorousness in Ukraine by (1) examining latent classes of audiences and (2) exploring the relationship between cultural omnivorousness and positions in the social structure, considering educational attainment, financial situation, age, gender and type of settlement. Relying on the data from the survey ‘Reading in the context of media consumption and life construction’ conducted in 2020, this study focuses on the breadth/volume of behavioral preferences in literary genres among readers in Ukraine (N = 1302). The latent class analysis presents evidence of the distinctiveness of the omnivore class. Subsequent regression analysis shows a strong positive association between high education and omnivorous taste, whereas the financial situation bears no significant direct effect. Regression coefficients for socio-demographic variables indicate a more omnivorous taste amongst women and residents of big cities. The study concludes by discussing potential limitations and further perspectives for investigating the relationships between cultural omnivorousness and social stratification.

US neonatal intensive care unit registered dietitian nutritionists salary description and correlates: results of a survey.

This survey described the compensation of neonatal intensive care unit (NICU) registered dietitian nutritionists (RDNs) in the United States and examined correlates of higher salaries within this group. A cross-sectional online survey was completed in 2021 by 143 NICU RDNs from 127 US hospitals who reported hourly wage in US dollars (USD). We used initial bivariate analyses to assess the relationship of selected institution-level and individual-level variables to hourly wage; the rank-sum test for binary variables; bivariate regression and Pearson correlation coefficients for continuous variables; the Kruskal-Wallis test for categorical variables. Variables with a compelling relationship to the hourly wage outcome were considered in model creation. Final model selection was based on comparisons of model fit. Median hourly compensation was USD 33.24 (interquartile range [IQR] 29.81, 38.49). Seven variables had a compelling bivariate relationship with hourly wage: cost of living, employer facility with a paediatric residency, employer facility with a neonatal fellowship, NICU bed: full-time equivalents (FTE) RDN ratio, years in neonatal nutrition, having a certification and order writing privileges. In the final adjusted model (R2 = 0.42), three variables remained associated with increased hourly wage: higher cost of living, longer length of career in neonatal nutrition and fewer NICU beds per NICU RDN FTE. US NICU RDNs earn similar or slightly higher wages than other US paediatric RDNs; they earn substantially less than other NICU healthcare team members. Employers need to improve compensation for NICU RDNs to incentivise their retention and recognise their additional non-clinical responsibilities.

Robust confidence regions for the index and functional coefficients in the single-index varying coefficients regression model

A single-index varying coefficients regression model is considered, from which robust confidence regions/intervals of the regression parameters are derived. Considering the rank-based estimating equations, empirical likelihood objective functions of the index and functional coefficients objective functions are defined and their asymptotic properties are established under mild regularity conditions. The performance of the proposed approach is demonstrated via extensive Monte Carlo simulation experiments. The simulation results are compared with those obtained from a normal approximation alternative. Also the proposed method is compared with the least squares and least absolute deviations alternatives. Finally, a real data example is given to illustrate the method.

Abstract PO4-07-10: Validation of An Ultrasound-Based Scoring System of Axillary Metastasis in Breast Cancer

Abstract Introduction: Ultrasound is the imaging modality of choice in the evaluation of axillary involvement in breast cancer. Our group previously created a scoring method to predict axillary lymph node metastasis (ALNM) based on ultrasound characteristics. In this study we validated the model and tested it among different Memorial Sloan Kettering Breast Cancer Sentinel Lymph Node Metastasis Nomogram1 (MSK) subgroups. Methods: The ultrasound score (table 1) was previously developed using data collected at a single institution from 2019 – 2021 by allocating points based on the regression coefficients of variables found to significantly predict ALNM. Subsequent evaluation of the discrimination of this model found it was robust to different patient demographic and tumor characteristics based on receiver operating characteristic curve analysis2. In this study, we validated the test statistics of our score at an outside institution. We also pooled patients from both institutions and evaluated the score performance in different MSK subgroups by the likelihood for sentinel node metastasis based off of clinical and pathologic data without accounting for imaging findings. Results: Between 2019-2021, 140 patients with breast cancer were analyzed for validation of the axillary ultrasound scoring system and when combined with the dataset of patients from the index institution, 358 pooled patients were stratified by predicted ALNM positivity according to MSK. In the validation cohort, the NPV for low risk (0-1) scores was 87%, while the PPV for high-risk (5+) scores was 71%. Overall in the combined cohort, 241 (67%) patients had low risk (0-1) axillary ultrasound scores and 33 (9%) had high risk (5+) scores. In this combined cohort, NPV was 84% (203/241 low-risk score patients were node negative), while PPV for high-risk scores was 85% (28/33 high risk score patients were node positive). When analyzed according to level of MSK predicted ALNM rates, for patients with < 50% predicted ALNM positivity, the NPV of low-risk scores was 87-89% and the PPV for high-risk axillary scores was 100%. For patients with >50% predicted ALNM positivity, the NPV of low-risk scores was 66% and the PPV of high-risk scores was 82%. Conclusions: A scoring system to predict ALNM among biopsy proven breast cancer patients undergoing upfront surgery was successfully developed from a multivariate model based on axillary ultrasound characteristics. This model was validated at a separate institution. The scoring system shows advantageous negative and positive predictive values for axillary node metastasis, especially among patients with < 50% predicted nodal involvement based on the MSK nomogram. This data may help foster better communication about ALNM risk between radiologists and treating clinicians to inform treatment decisions. Table. Scoring System for Likelihood of ALNM based on Ultrasound Characteristics Citation Format: Spencer Van Decar, Elizabeth Carpenter, Alexandra Adams, Jason Shore, Iulian Dragusin, Erika Davis, Craig Tork, Robert Krell, Troy Graybeal, Katherine Clifton, Arianna Buckley, Guy Clifton. Validation of An Ultrasound-Based Scoring System of Axillary Metastasis in Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-07-10.

A principal-weighted penalized regression model and its application in economic modeling

This paper introduces a novel Principal-Weighted Penalized (PWP) regression model, designed for dimensionality reduction in large datasets without sacrificing essential information. This new model retains the favorable features of the principal component analysis (PCA) technique and penalized regression models. It weighs the variables in a large data set based on their contributions to principal components identified by PCA, enhancing its capacity to uncover crucial hidden variables. The PWP model also efficiently performs variable selection and estimates regression coefficients through regularization. An application of the proposed model on high-dimensional economic data is studied. The results of comparative studies in simulations and a real example in economic modeling demonstrate its superior fitting and predictive abilities. The resulting model excels in accuracy and interpretability, outperforming existing methods.

Efficient Modeling of Distributed Energy Resources’ Impact on Electric Grid Technical Losses: A Dynamic Regression Approach

Technical losses in electrical grids are inherent inefficiencies induced by the transmission and distribution of electricity, resulting in energy losses that can reach up to 40% of the generated energy. These losses pose significant challenges to grid operators regarding energy sustainability, reliability, and economic viability. Distributed Energy Resources (DERs) offer promising solutions to lower technical losses by decentralizing energy generation and consumption, reducing the need for long-distance transmission and optimizing grid operation. Hence, estimating the impact of DERs on grid technical losses becomes paramount for grid operators and planners. In response, this article proposes the application of regression modeling and nonlinear curve fitting algorithms to provide a more nuanced understanding and better characterize the intricate interplay between DER deployment and technical losses. Through a comprehensive case study based on more than 1080 computer simulations, we demonstrate the effectiveness of our proposed dynamic polynomial varying coefficient regression model in estimating the impact of DERs on technical losses within electrical grids. The proposed model offers a simple and effective methodology that allows grid operators to gain insights into the nonlinear dynamics of DER integration and make quicker and more informed decisions regarding grid management strategies, infrastructure investments, and policy interventions. Also, this research contributes to advancing the field of grid optimization by offering a simple equation that enhances our ability and haste to assess and mitigate technical losses in the context of an evolving energy landscape characterized by increasing DER adoption.

Pengaruh Citra Merek Dan Kualitas Pelayanan Terhadap Keputusan Konsumen Dalam Memilih Jasa Wedding Organizer Di Purandewi Catering

The purpose of this research is to study how brand image and service quality impact the decisions made by customers when choosing Purandewi Catering wedding organizer services. Descriptive quantitative method with Likert measurement tool was used in this research. Primary data from this study came from 68 respondents who used Purandewi Catering services during the period January - December 2023. The results of the multiple linear regression test show that the brand image variable regression coefficient value is 0.403 or has a positive and significant effect on consumer decisions, then the service quality variable regression coefficient value of 0.298 has a positive effect on consumer decisions. The two variables also simultaneously or together have an effect on consumer decisions to use Purandewi Catering wedding organizer services with the results of the f test count 31.754 greater than the f table value of 3.13.

Semi-supervised estimation for the varying coefficient regression model

<abstract><p>In many cases, the 'labeled' outcome is difficult to observe and may require a complicated or expensive procedure, and the predictor information is easy to be obtained. We propose a semi-supervised estimator for the one-dimensional varying coefficient regression model which improves the conventional supervised estimator by using the unlabeled data efficiently. The semi-supervised estimator is proposed by introducing the intercept model and its asymptotic properties are proven. The Monte Carlo simulation studies and a real data example are conducted to examine the finite sample performance of the proposed procedure.</p></abstract>

Impact of Digitalization on Global Sustainable Development Across Countries

This study develops an economic development index, a social development index, an environmental sustainability index, and an information and communication technology index to explore the comparison of 34 countries in global sustainable development (GSD) and its promoting indicators, and digitalization. The composite Z-score method is used to create the above-mentioned indexes between 2000 - 2021. The statistical values of these indexes are used to explicate the performance of these countries in all components of sustainable development and digitalization. The correlation coefficients among the mentioned indicators are also estimated to measure associations among them. The regression coefficients of explanatory variables with GSD and digitalization are also estimated using a log-linear regression model. There is reported a high diversity in digitalization, economic development (ED), social development (SD), environmental sustainability (ES), and GSD across countries. GSD is not possible without ED, SD, and ES. Digitalization has a positive impact on sustainable, social, and economic development. Digital technology helps to increase GSD, while GSD seems favorable to increasing digitalization. Digitalization improves as ED, SD, and GSD increase. While digitalization has a negative impact on ES. Green innovation, green and appropriate technologies, green entrepreneurship, and technological advancement would be supportive to increase digitalization and GSD.

The Impact of Self Directed Learning and Digital Literacy for Study Result Pengelolaan Bisnis Ritel Subject Grade XI BDP SMK Negeri 1 Medan 2022/2023

This research aims to find out the influence of Self Directed Learning and Digital Literacy on the Learning Results of Class XI BDP SMK Negeri 1 Medan. The population in this study is the entire student class XI BDP SMK State 1 Medan T.A 2022/2023 which totaled 66 students. The sampling technique was carried out using total samplings to obtain a sample of 66 students. In the meantime, for the collection of research data obtained from elevators and lists of student values. The result of the double linear regression test showed the constant value (a) of 4.019, the self-directed learning variable regression coefficient value of 0.623, then the Digital Literation variabel regression factor of 0.362. It was concluded that Self Directed Learning and Digital Literacy had a significant impact on Retail Business Management Lesson Results in Class XI BDP SMK State 1 Medan T.A 2022/2023.

GENERALIZED MATRIX DECOMPOSITION REGRESSION: ESTIMATION AND INFERENCE FOR TWO-WAY STRUCTURED DATA.

Motivated by emerging applications in ecology, microbiology, and neuroscience, this paper studies high-dimensional regression with two-way structured data. To estimate the high-dimensional coefficient vector, we propose the generalized matrix decomposition regression (GMDR) to efficiently leverage auxiliary information on row and column structures. GMDR extends the principal component regression (PCR) to two-way structured data, but unlike PCR, GMDR selects the components that are most predictive of the outcome, leading to more accurate prediction. For inference on regression coefficients of individual variables, we propose the generalized matrix decomposition inference (GMDI), a general high-dimensional inferential framework for a large family of estimators that include the proposed GMDR estimator. GMDI provides more flexibility for incorporating relevant auxiliary row and column structures. As a result, GMDI does not require the true regression coefficients to be sparse, but constrains the coordinate system representing the regression coefficients according to the column structure. GMDI also allows dependent and heteroscedastic observations. We study the theoretical properties of GMDI in terms of both the type-I error rate and power and demonstrate the effectiveness of GMDR and GMDI in simulation studies and an application to human microbiome data.

A varying coefficient model with matrix valued covariates

Modern data are often collected in a matrix form. In this paper, we consider modelling the varying coefficient regression with matrix valued covariate X and scalar index variable U. The proposed model simultaneously makes principal component analysis for both the row and column dimensions of the matrix objects, maintaining the matrix structure while achieving substantial dimension reduction. We develop an iterative estimation method for the involved principal parameters and nonparametric functions. Under regularity conditions, the asymptotic distributions of the estimators are derived. In addition, by incorporating the estimation with the adaptive group Lasso and the group SCAD penalties, variables of X in entire rows or columns are selected. The proximal gradient algorithm is further utilised to solve the regularised optimisation problems. The asymptotic properties of the penalised estimators are also studied. Our model and estimation methods are demonstrated by simulated experiments. Real applications to the primary biliary cirrhosis (PBC) data reveal that the effects of the blood measurements to the survival time vary with levels of age.

PENGARUH LABA KOTOR, LABA OPERASI, DAN LABA BERSIH UNTUK MEMPREDIKSI ARUS KAS MASA MENDATANG

This study is quantitative and draws on secondary information from financial records that can be viewed at www.idx.ac.id.. The research population consisted of 10 pharmaceutical companies and 8 research samples obtained using a purposive sampling method for 5 years & the number of samples was 40 observational data. The data analysis technique used is multiple linear regression analysis which was using SPSS 26. The results of the research conducted in this research are: (1) From the results of the regression test, the regression coefficient of the Gross Profit variable is positive. This shows there is such positive influence between Gross Profit and future Cash Flows. It is known that the Sig value shows number that is less than the significance value. then the gross profit partially affects the prediction of future cash flows, (2) From the results of the regression test, the regression coefficient of the variable Operating Profit has no impact on future cash flows. Sig probability value. the greater the significance value, the operating profit partially does not affect the prediction of future cash flows, (3) The regression test's findings indicate that the Net Income variable's regression coefficient has a favorable impact on upcoming cash flows. Probability value for sig. Operating profit partially influences future cash flows if it is greater than the substantial value and a positive coefficient., (4) The value obtained from the F test is 122.176, and the significance level is 0.000. Value of Sig is known. displays a value less than the significance level (0.000 0.05). This leads to the conclusion that gross profit, operating profit, and net profit all have an impact on forecasting future cash flows at the same time. Furthermore, gross profit, operating profit, and net profit can predict future cash flows by 90.3%. While other factors and variables outside of this study can explain and have an impact on the remaining 9.7%.

Does mobility restriction significantly control infectious disease transmission? Accounting for non-stationarity in the impact of COVID-19 based on Bayesian spatially varying coefficient models.

COVID-19 is the most severe health crisis of the 21st century. COVID-19 presents a threat to almost all countries worldwide. The restriction of human mobility is one of the strategies used to control the transmission of COVID-19. However, it has yet to be determined how effective this restriction is in controlling the rise in COVID-19 cases, particularly in small areas. Using Facebook's mobility data, our study explores the impact of restricting human mobility on COVID-19 cases in several small districts in Jakarta, Indonesia. Our main contribution is showing how the restriction of human mobility data can give important information about how COVID-19 spreads in different small areas. We proposed modifying a global regression model into a local regression model by accounting for the spatial and temporal interdependence of COVID-19 transmission across space and time. We applied Bayesian hierarchical Poisson spatiotemporal models with spatially varying regression coefficients to account for non-stationarity in human mobility. We estimated the regression parameters using an Integrated Nested Laplace Approximation. We found that the local regression model with spatially varying regression coefficients outperforms the global regression model based on DIC, WAIC, MPL, and R2 criteria for model selection. In Jakarta's 44 districts, the impact of human mobility varies significantly. The impacts of human mobility on the log relative risk of COVID-19 range from -4.445 to 2.353. The prevention strategy involving the restriction of human mobility may be beneficial in some districts but ineffective in others. Therefore, a cost-effective strategy had to be adopted.

The Effect Of Sales Growth And Dar On The Profitability Of Infrastructure Sector Companies Listed On The IDX In 2020-2022

Infrastructure is the most important thing in the progress of a country. Several countries in the world have been able to develop forward thanks to a strong infrastructure foundation supported by good synergies between government companies and the private sector. Infrastructure development is an important matter in the process of growing a nation in the economic, educational, social, cultural, agricultural, and other sectors. This study aims to determine the effect of sales growth and DAR (Debt to Assets Ratio) on company profitability (Return on Asset). This research was conducted on infrastructure sector companies listed on the Indonesia Stock Exchange for 2020-2022. The population in this study are all infrastructure companies listed on the Indonesia Stock Exchange (IDX) for 2020-2022, totaling 37 companies. The sample selection used a purposive sampling technique with several predetermined criteria, so the number of samples was 11 companies. The research data is secondary data obtained from the Indonesian Stock Exchange (IDX) 2020-2022. Data were analyzed using panel data regression with the help of Stata software version 14 to determine the significant level of each independent variable regression coefficient to the dependent. The results showed that sales growth and DAR (Debt to Asset Ratio) simultaneously had a positive effect on company profitability (Return on Asset). Sales growth has no effect on profitability, and DAR (Debt to Asset Ratio) has a negative effect on company profitability (Return on Asset).

Sparse estimation in semiparametric finite mixture of varying coefficient regression models.

Finite mixture of regressions (FMR) are commonly used to model heterogeneous effects of covariates on a response variable in settings where there are unknown underlying subpopulations. FMRs, however, cannot accommodate situations where covariates' effects also vary according to an "index" variable-known as finite mixture of varying coefficient regression (FM-VCR). Although complex, this situation occurs in real data applications: the osteocalcin (OCN) data analyzed in this manuscript presents a heterogeneous relationship where the effect of a genetic variant on OCN in each hidden subpopulation varies over time. Oftentimes, the number of covariates with varying coefficients also presents a challenge: in the OCN study, genetic variants on the same chromosome are considered jointly. The relative proportions of hidden subpopulations may also change over time. Nevertheless, existing methods cannot provide suitable solutions for accommodating all these features in real data applications. To fill this gap, we develop statistical methodologies based on regularized local-kernel likelihood for simultaneous parameter estimation and variable selection in sparse FM-VCR models. We study large-sample properties of the proposed methods. We then carry out a simulation study to evaluate the performance of various penalties adopted for our regularized approach and ascertain the ability of a BIC-type criterion for estimating the number of subpopulations. Finally, we applied the FM-VCR model to analyze the OCN data and identified several covariates, including genetic variants, that have age-dependent effects onOCN.

Application of Real-time Submental Ultrasonography to Assess Swallowing.

Speech and swallowing dysfunction are common problems in head-and-neck cancer (HNC) survivors. Ultrasound (US) is a good method to assess suprahyoid muscles and hyoid bone movement, and it can provide valuable information on swallowing. The aims of this study were to measure the biometry of the supraglottic muscles and hyoid bone movement during swallowing and elucidate the application of real-time US for assessing swallowing dysfunction. We collected data from HNC and thyroid cancer patients with dysphagia symptoms and healthy controls without a history of cancer or dysphagia symptoms for comparison. Real-time submental US was used to check the anterior belly of the digastric muscle, geniohyoid (GH) muscles, and hyoid bone movement during swallowing. Logistic regression analysis was used to explore significant US predictors of dysphagia. Based on the regression coefficients of independent variables, we established the nomogram prediction model for dysphagia. There were significant differences in GH size at contraction, GH size increase percentage, GH length at rest, GH length increase percentage, anterior displacement of the hyoid bone and superior displacement of the hyoid bone between the cancer survivors with dysphagia and volunteers without dysphagia. In multivariate logistic analysis, after adjusting for sex and age, the proportion of GH length contraction <22% (odds ratio [OR]: 6.8 95% confidence interval [CI]: 1.1-42.6) and hyoid bone superior displacement <3.3 mm (OR: 10.7, 1.8-64.1) were associated with a higher risk of dysphagia (P < 0.05). We confirmed that GH muscle and hyoid bone movement are important for normal swallowing function. US is a good method to assess the suprahyoid muscles and hyoid bone movement, which could provide valuable information on swallowing.

Efficient estimation in varying coefficient regression models

Varying coefficient models have been studied for many years, and local kernel estimation is an important method to estimate the coefficient functions. Since same bandwidth parameter is used for each coefficient function, local kernel estimation is not effective when the degree of smoothness is different for each coefficient function. In this paper, a new global estimation method based on kernel function and backfitting is proposed. In this method, different bandwidth parameters can be used for each coefficient function to improve the estimation accuracy, and asymptotic properties of the estimators are proved. To reduce the computational complexity, an adaptive global estimation method, which is based on an estimation of the bandwidth ratio and has only one bandwidth parameter to be selected, is proposed. Simulation results show that the proposed methods work well, and two real data examples further demonstrate the potential of the proposed methods.