Assumptions Of Linear Regression Research Articles

Enhancing Multiple Linear Regression with Stacking Ensemble for Dissolved Oxygen Estimation

Maintaining optimal dissolved oxygen levels is essential for aquatic ecosystems, yet industrial and domestic waste has led to a global decline in dissolved oxygen. Traditional measurement methods, such as oxygen meters and Winkler titration, are often costly or time-consuming. This study aims to improve the Root Mean Square Error, Mean Absolute Error, and R2 values for estimating dissolved oxygen levels. The research method uses Multiple Linear Regression with various training and testing data splits, both before and after applying polynomial features. The model is further optimized using a stacking technique, with Random Forest Regressor and Gradient Booster Regressor as base models.The results show that the best model was achieved using the stacking ensemble technique with a 90:10 data split and polynomial features, yielding a Root Mean Square Error of 1.206, Mean Absolute Error of 0.990, and R2 of 0.670. This model has also met the assumptions of linear regression, such as residual normality, homoscedasticity, and no autocorrelation of residuals. This study concluded that the ensemble stacking technique and the addition of polynomial features could improve the model in estimating dissolved oxygen values and also contribute by providing an accessible user interface using the Gradio Framework, allowing users to estimate dissolved oxygen levels effectively.

Lifetime minimally invasive assessment of iron in the pig liver

Microelements are an integral part of the mammalian body, and their content in organs and tissues is associated with other components of a complex biological system. Based on this, it is feasible to evaluate the concentration of specific chemical elements within the structures of the body in non-invasive or minimally invasive methods. The meat and by-products of farm animals serve as a readily assimilateable source of iron, which is one of the reasons for potentially defining the quality of agricultural products in conditions of widespread iron deficiency. Landrace pigs were raised in standard conditions at an industrial complex located in the Altai Territory in order to fatten up to a live weight of 100 kg. Venous blood was collected using the acute method from the jugular vein in accordance with the principles of asepsis and pre-analytical guidelines. The hematologic and biochemical examination of the blood and serum of animals was performed by apparatus. After slaughter, liver samples were collected, and the method of atomic emission spectral analysis using inductively coupled plasma on iCAP-PRO equipment (Thermo Fisher Scientific) was used to estimate the iron level in them. To manipulate the data, Microsoft Office Excel software and RStudio data analysis environment version 2023.03.1 (RStudio, PBC) were employed. For regression analysis, the least squares approach was used. The model was fitted using a stepwise selection of predictors in both directions using the Akaike information criterion, Bayesian information criterion, and adjusted coefficient of determination. The linear regression assumptions were evaluated. The final regression model used for determining iron levels in pig liver contains mean hemoglobin content in erythrocytes, hemoglobin, and serum inorganic phosphorus as predictors. There is no evidence that there is multicollinearity between the predictors of the final model. The proposed model satisfies the requirements for a normal distribution of residuals, the absence of their correlation, and influential observations. The proposed multiple regression model has the capability to estimate iron levels in pig liver in vivo for various purposes.

The relationship between psychological capital and career commitment in teachers

When teachers have a high level of psychological capital, it can motivate them to be more proactive, exhibit higher confidence, and achieve better performance. This positive impact, in turn, can lead to strong commitment to their work, career, and the organization they belong to, resulting in the formation of high-quality teaching staff. This research aims to examine the relationship between psychological capital and career commitment among teachers. The participants in this study were 336 teachers. The research utilized a quantitative research model with a correlational method employing the simple linear regression assumption test. The instruments used in this study were the Psychological Capital Questionnaire (PCQ) and Career Commitment Measure (CCM). The results of this study indicate a correlation between two psychological capital dimensions (efficacy and optimism) and three dimensions of career commitment. There is also a relationship between the hope dimension and the identity dimension of career commitment, while the other two dimensions do not have a connection with career commitment dimensions. Additionally, the resilience dimension is also associated with career planning.

Development of an empirical model for the prediction of the sound absorption coefficient for thin and low-density fibrous materials

Currently, FEA software such as ABAQUS uses empirical models to predict the sound absorption coefficient of poroelastic materials. However, based on a recent review of the literature it was found that the current sound absorption empirical models are inadequate for accurate prediction of thin (t < 20 mm), low-density materials (ρB < 50 kg/m3). Therefore, the predictions of the sound pressure levels in vehicle cabins, using such software, will be inaccurate since the trim materials are thin and have a low density. Thus, this research aimed to develop an empirical model that can accurately predict the sound absorption coefficient of these materials. Hence, polypropylene fibres consisting of four different diameters were manufactured and converted into nonwovens. Thereafter, airflow resistivity and impedance tube experimental testing were performed on the specimens. Subsequently, statistical analysis of the data was performed using SAS software. SAS was used to identify which independent variables should be included in the models to be developed. The empirical models were developed using the regression analysis toolbox in Microsoft Excel. Once the models were developed, various checks were performed to validate the assumptions of linear regression. The software NumXL was used to perform Cook’s distance tests. Thereafter, the models were validated against the validation dataset, where it was found that the developed exponential model performed best. Finally, the exponential model was compared to existing models using two data sets i.e. an internal dataset, and an external dataset derived from the literature. The developed model outperformed all the historic models on both datasets.

A meta-analysis of the relationship between milk protein production and absorbed amino acids and digested energy in dairy cattle

Milk protein production is the largest draw on AA supplies for lactating dairy cattle. Prior NRC predictions of milk protein production have been absorbed protein (MP)-based and used a first-limiting nutrient concept to integrate the effects of energy and protein, which yielded poor accuracy and precision (root mean squared error [RMSE] >21%). Using a meta-data set gathered, various alternative equation forms considering MP, absorbed total EAA, absorbed individual EAA, and digested energy (DE) supplies as additive drivers of production were evaluated, and all were found to be superior in statistical performance to the first limitation approach (RMSE = 14%-15%). Inclusion of DE intake and a quadratic term for MP or absorbed EAA supplies were found to be necessary to achieve intercept estimates (nonproductive protein use) that were similar to the factorial estimates of the National Academies of Sciences, Engineering, and Medicine (2021). The partial linear slope for MP was found to be 0.409, which is consistent with the observed slope bias of -0.34 g/g when a slope of 0.67 was used for MP efficiency in a first-limiting nutrient system. Replacement of MP with the supplies of individual absorbed EAA expressed in grams per day and a common quadratic across the EAA resulted in unbiased predictions with improved statistical performance as compared with MP-based models. Based on Akaike's information criterion and biological consistency, the best equations included absorbed His, Ile, Lys, Met, Thr, the NEAA, and individual DE intakes from fatty acids, NDF, residual OM, and starch. Several also contained a term for absorbed Leu. These equations generally had RMSE of 14.3% and a concordance correlation of 0.76. Based on the common quadratic and individual linear terms, milk protein response plateaus were predicted at approximately 320 g/d of absorbed His, Ile, and Lys; 395 g/d of absorbed Thr; 550 g/d of absorbed Met; and 70 g/d of absorbed Leu. Therefore, responses to each except Leu are almost linear throughout the normal in vivo range. De-aggregation of the quadratic term and parsing to individual absorbed EAA resulted in nonbiological estimates for several EAA indicating over-parameterization. Expression of the EAA as g/100 g total absorbed EAA or as ratios of DE intake and using linear and quadratic terms for each EAA resulted in similar statistical performance, but the solutions had identifiability problems and several nonbiological parameter estimates. The use of ratios also introduced nonlinearity in the independent variables which violates linear regression assumptions. Further screening of the global model using absorbed EAA expressed as grams per day with a common quadratic using an all-models approach, and exhaustive cross-evaluation indicated the parameter estimates for BW, all 4 DE terms, His, Ile, Lys, Met, and the common quadratic term were stable, whereas estimates for Leu and Thr were known with less certainty. Use of independent and additive terms and a quadratic expression in the equation results in variable efficiencies of conversion. The additivity also provides partial substitution among the nutrients. Both of these prevent establishment of fixed nutrient requirements in support of milk protein production.

Rush regression workbench: An integrated open-source application for regression modeling and analysis in healthcare analytics

Regression is widely used in healthcare analytics, whether for examining hospital quality and safety, characterizing patterns of patient volume and healthcare costs, or predicting patient outcomes. Simple linear regression and other basic forms can be conducted with spreadsheet programs and are useful for examining simple linear relationships. However, expert statistical knowledge, computational skills, and specialized tools may be needed to characterize nonlinear relationships and complex interactions, to examine data that fail the assumptions of linear regression, to identify confounding variables and lessen the influence of outliers, and to build and evaluate predictive models. We constructed the Rush Regression Workbench to accomplish these tasks and to automate cautious and sophisticated analyses, provide interpretive outputs, enable reproducible results, and to provide the community with an evolving open-source good containing a diverse set of analyses and a growing library of over 170 preprocessed public healthcare datasets. The Rush Regression Workbench can be accessed via the web or downloaded and used locally.

PENINGKATAN KUALITAS HIDUP PASIEN BRACHIALIS PLEXUS INJURY MELALUI PENDAMPINGAN KEWIRAUSAHAAN

Brachial Plexus Injury is a severe peripheral nerve injury that affects the upper extremities, resulting in functional damage and physical disability. Patients with this condition will experience functional limitations in the upper limbs due to a decrease in joint range of motion and muscle strength, thereby affecting productivity. The aim of this research is to determine the improvement in the quality of life of Brachial Plexus Injury Patients through Entrepreneurship Assistance in the Brachial Plexus Injury community. Research Method: This research uses a quantitative descriptive research design. The research was conducted in the Brachialis Plexus Injury community with a sample size of 25 patients using probability sampling techniques with simple random sampling. The instruments used were questionnaires about entrepreneurial intentions, entrepreneurial personality, family support and quality of life. The analysis test used is the multiple linear regression assumption test. Research Results: There is an influence of entrepreneurial mentoring including family support, entrepreneurial intentions, entrepreneurial personality on the quality of life. The F count results are 17.045 with a significance of 0.000. It is known that the G table value is 3.08 because the calculated F value > F table means HO is rejected and Ha is accepted, meaning that there is a significant influence between entrepreneurial assistance on the quality of life of patients with Brachial Plexus Injury conditions. There is a correlation between the variables Family support (X1), Entrepreneurial Personality (X2), Entrepreneurial intentions, with a significance value of 0.000. This means that the significance value of the three variables is smaller than 0.05 or in other words 0.000 > 0.05. So it can be concluded that there is a correlation. To see the level of closeness of the relationship, it can be seen from the R Square of 0.709, which is strongly correlated (according to the rules, if the value is 0.61 to 0.80, it is in the strong correlation category). Conclusion: There is an influence of entrepreneurial assistance including family support, entrepreneurial intentions, entrepreneurial personality on quality of life

A multivariable analysis to predict variations in hospital mortality using systems-based factors of healthcare delivery to inform improvements to healthcare design within the English NHS.

Over the last decade, the strain on the English National Health Service (NHS) has increased. This has been especially felt by acute hospital trusts where the volume of admissions has steadily increased. Patient outcomes, including inpatient mortality, vary between trusts. The extent to which these differences are explained by systems-based factors, and whether they are avoidable, is unclear. Few studies have investigated these relationships. A systems-based methodology recognises the complexity of influences on healthcare outcomes. Rather than clinical interventions alone, the resources supporting a patient's treatment journey have near-equal importance. This paper first identifies suitable metrics of resource and demand within healthcare delivery from routinely collected, publicly available, hospital-level data. Then it proceeds to use univariate and multivariable linear regression to associate such systems-based factors with standardised mortality. Three sequential cross-sectional analyses were performed, spanning the last decade. The results of the univariate regression analyses show clear relationships between five out of the six selected predictor variables and standardised mortality. When these five predicators are included within a multivariable regression analysis, they reliably explain approximately 36% of the variation in standardised mortality between hospital trusts. Three factors are consistently statistically significant: the number of doctors per hospital bed, bed occupancy, and the percentage of patients who are placed in a bed within four hours after a decision to admit them. Of these, the number of doctors per bed had the strongest effect. Linear regression assumption testing and a robustness analysis indicate the observations have internal validity. However, our empirical strategy cannot determine causality and our findings should not be interpreted as established causal relationships. This study provides hypothesis-generating evidence of significant relationships between systems-based factors of healthcare delivery and standardised mortality. These have relevance to clinicians and policymakers alike. While identifying causal relationships between the predictors is left to the future, it establishes an important paradigm for further research.

The Nexus Between Foreign and Portfolio Investments, and Youth Unemployment in Kenya

Youth unemployment is a global problem as countries at different stages of development that is underdeveloped, developing and developed are grappling with the problem of youth unemployment, which has been addressed in different country specific ways. Investments of whatever form are the life wire of the economy. Investments are a remedy for macroeconomic problems of unemployment, income inequality and economic growth. Investments change the economic structure to affect certain economic sectors eyed by private and public sectors. Investments from foreign entities provide a supportive role in an open foreign economic system with accompanying effects on economic development and growth. Foreign direct investments effects are seen unevenly or uniformly around the globe due to variety economic conditions. Investments in whatever form in a certain country is a key ingredient of economic growth or a catalyst of growth and development leading to job creation for the youth and other people in the labor force. Facilitated portfolio investments and capital market ease of penetration are pull factors for investors. The study seeks to determine the investments effects on Kenya’s youth unemployment rate. The study objectives are to determine the foreign direct investments and portfolio investments effects on Kenya’s youth unemployment rate. Investments have not been much prioritized to affect the level of youth unemployment; this informs this research work. The Keynesian theory of unemployment anchors the study. This research would be non-experimental as the time series data would be collected from Kenya National Bureau of Statistics, World Bank Development Indicators and Central Bank of Kenya for 1993 to 2022 period. Time series properties of trend and stationarity that is unit roots tests were tested for all the variables if it exists before estimation. Linear regression assumptions of homoscedasticity, linearity and collinearity were tested before estimation. Multiple regression analysis method was used to analyze stationary data. Tables were used to present results. Study findings, on the effect of foreign direct investments had a statistical insignificant effect on youth unemployment ages 15- 24 years in Kenya while portfolio investments had a statistical insignificant effect on youth unemployment rate ages 15-24 years in Kenya. Conclusion of the study, portfolio investments and foreign direct investments t-statistics were insignificant at five per cent (5%) level of significance therefore could not affect the level of youth unemployment rate ages 15-24 years. Recommendations of the study portfolio investments, foreign direct investments and other forms of investments should be directed towards labor absorbing sectors so that different categories of unemployment can be absorbed thus employment creation. Kenya’s macro-economic measures need to be directed towards all categories of unemployment.

Feature selection of ground motion intensity measures for data‐driven surrogate modeling of structures

AbstractIn the probabilistic seismic performance assessment of structures, intensity measures (IMs) represent seismic characteristics and variations. Traditional fragility analysis method based on the assumption of linear regression requires selecting an optimal IM as input variable. By introducing machine learning (ML) techniques, nonparametric fragility analysis theoretically allows for considering all potential IMs as inputs. Nevertheless, to reduce input dimensionality and improve training efficiency, the feature selection of IMs remains imperative. This paper proposes a method to select optimal ground motion IMs for data‐driven surrogate modeling of structures. Specifically, the elastic net algorithm is employed to select the optimal multiple IMs based on the coefficient of determination and regression coefficient, differing from the efficiency and practicality emphasized in the traditional method. Using the optimal multiple IMs as input variables, several ML techniques are employed to construct surrogate models for seismic damage assessment of structures, thereby developing fragility functions, that is, the conditional probability of exceeding a damaged state given seismic intensity. A 3‐span, 6‐storey, reinforced concrete frame is utilized to illustrate the proposed methodology. The predictive performance of all ML models with the optimal multiple IMs outperforms that of the models with the commonly used IM (e.g., peak ground acceleration,PGA) as sole input and all candidate IMs as inputs. Additionally, the surrogate models with the optimal multiple IMs enable a more comprehensive seismic fragility modeling of structures under two or more IMs simultaneously, such as the fragility surface under spectral acceleration at 1.0s (Sa‐1.0s) and velocity spectrum intensity (VSI).

R-Squared (R2) – How much variation is explained?

Linear regression is a simple yet powerful tool that has been extensively used in all fields where the relationships among variables are of interest. When linear regression is applied, the coefficient of determination or R-squared ( R2) is commonly reported as a metric gauging the model’s goodness of fit. Despite its wide usage, however, R2 has been commonly misinterpreted as the proportion or percent of variation in the dependent variable that is explained by the independent variables (PVE -- percent of variation explained). This study demonstrated R2 substantially overstates the true PVE. When the assumptions of linear regression are met, R2 overstates PVE by up to 100%. For instance, when R2 is 0.99, 0.80, 0.50, or 0.10, the true PVE is 0.9, 0.55, 0.29, or 0.05, respectively. The misinterpretation of R2, which greatly exaggerates the effect of the interventions or causes on the outcomes, could exert undue influence on clinical decisions in medicine and policy decisions in other fields such as environmental protection and climate change research. Therefore, when linear regression is applied, reporting the true PVE is warranted.

Statistical considerations on real time and extended controlled temperature conditions (ECTC) stability data analysis of vaccines

BackgroundAlthough maintaining vaccines in a strict cold chain has cost and logistical implications in low- and middle-income countries, only a few vaccines have obtained approval for extended controlled temperature conditions (ECTC) application, which permits the administration of vaccines after storage outside of the cold chain for a defined period. We developed a methodology to evaluate stability data and calculate minimum release potency (MRP) in support of ECTC application. MethodsThe methodology is focused on statistical considerations consisting of stability data collection, statistical analysis plan, statistical modelling, and statistical report. It uses mock stability data from a hypothetical product and may serve as a helpful guide for other products. The statistical data analysis is performed using the R program which is an open-source program and validated using the SAS software. ResultsWe developed a stability data testing scheme that included 24 lots with six-time points for up to 24 months under real-time and real condition (RT) in the cold chain samples stored at 2–8 °C and 12 lots with six timepoints for 14 days under ECTC samples stored at 40 °C. The log-transformed stability data met the linear regression assumptions and were poolable from representative lots with no significant lot variation. The linear regression analysis model with a common slope and intercept confirmed the stable antigen content over time under RT and ECTC by the mean regression line and 95% confidence interval. Based on the fitted models and the estimated coefficients, the antigen content value of 966 was derived as the MRP under RT for 24 months followed by 14 days under ECTC. ConclusionThe presented framework of statistical considerations, with practical methods and R program codes to perform statistical analysis, may serve as a guide for developing the CTC data for a vaccine’s stability evaluation prospectively.

ANALISIS PERSEPSI MAHASISWA AKUNTANSI TERHADAP ETIKA PROFESI AKUNTAN PADA PERGURUAN TINGGI DI KOTA BATAM

This research aimed to analyze the perceptions of accounting students regarding the ethics of the accounting profession, based on the variables of gender and education. Using quantitative research methodology, primary data were collected through the distribution of questionnaires via Google Forms utilizing the Slovin formula. The population comprised 1,258 accounting students from several universities in Batam city, with a sample size of 303 respondents. The questionnaire results were subjected to validity and reliability testing. Data analysis techniques included classical assumption analysis, linear regression assumptions, determination coefficients, T-tests, and F-tests, using SPSS version 26. The findings indicated that gender had a significant partial impact on the ethics of the accounting profession (t-value 5.375 > t-table 1.967). Education also had a significant partial impact on the ethics of the accounting profession (t-value 9.337 > t-table 1.967). Furthermore, simultaneous testing revealed that both gender and education had a positive and significant influence on the ethics of the accounting profession (F-value 255.550 > F-table 3.025). These results provide valuable insights into the factors that shape the ethical perceptions of accounting students.

Influence of Grand Strategies on Performance of Manufacturing Firms in Nairobi County, Kenya

Relationship between grand strategies and performance of manufacturing firms has gained credence globally. Manufacturing firms in Japan have recorded greater success in manufacturing sector globally due to their ability to adopt various grand strategies that have aided them to achieve sustained competitive advantage. However, manufacturing industry in Kenya has experienced decline over the last five years. Manufacturing sector GDP contribution in Kenya has reduced in 2022 first quarter to KES 118,134 from KES 113,460 million 2022 second quarter. Therefore, this study sought to examine the influence of grand strategies on performance of manufacturing firms in Nairobi County, Kenya. The specific objectives were to examine the influence of product development strategy on performance of manufacturing firms and to establish the influence of turnaround strategy on performance of manufacturing firms in Nairobi County, Kenya. The study was guided by Igor Ansoff’s theory, and Stage theory of successful turnaround. Descriptive research design was used in this study. One hundred (100) respondents from 20 large manufacturing firms in Nairobi County were targeted. The sampling frame comprised of marketing/sales managers, finance managers, human resource managers, operational managers, Strategy & Business Development Managers. The study sampled 100 using census sampling technique. Primary data was collected using a well-designed questionnaire. Quantitative data was analyzed using descriptive and inferential statistics. Descriptive analysis was summarized data in form of central tendency as well as dispersion and inferential analysis was used to test hypothesis at a significance level of 0.05. Descriptive analysis included; frequencies, Mean, Standard deviation and percentage while inferential analysis involved correlation analysis and multiple linear regression analysis. Prior to conducting multiple linear regressions, the study ensured that the assumptions of linear regression are met. The data was presented in form of tables and models. The results indicated that product development strategy had positive and significant effect on organizational performance. Turnaround strategy had a positive and significant effect on performance. On the other hand, the regression analysis revealed that the grand strategies explained up to 64.5% change in organizational performance of manufacturing firms in Nairobi County. The study concluded that grand strategies significantly influence organizational performance of manufacturing firms in Nairobi County. This study recommends that management of manufacturing firms pursuing product development strategies so as to come up with products that meet the changing needs of their customers. It is recommended that the companies should open new branches in new geographical areas to reach new customers not only beyond Nairobi County, but also beyond East Africa. The companies can expand their product lines by developing new products that may or may not be related to the current products to target current customers. The study recommends that management of manufacturing firms should create an organizational culture that is in line with their turnaround strategy. Keywords: [Organizational Performance, Product Development Strategy, Turnaround Strategy]

Investigation of heteroscedasticity in polygenic risk scores across 15 quantitative traits.

The polygenic risk score (PRS) could be used to stratify individuals with high risk of diseases and predict complex trait of individual in a population. Previous studies developed a PRS-based prediction model using linear regression and evaluated the predictive performance of the model using the R 2 value. One of the key assumptions of linear regression is that the variance of the residual should be constant at each level of the predictor variables, called homoscedasticity. However, some studies show that PRS models exhibit heteroscedasticity between PRS and traits. This study analyzes whether heteroscedasticity exists in PRS models of diverse disease-related traits and, if any, it affects the accuracy of PRS-based prediction in 354,761 Europeans from the UK Biobank. We constructed PRSs for 15 quantitative traits using LDpred2 and estimated the existence of heteroscedasticity between PRSs and 15 traits using three different tests of the Breusch-Pagan (BP) test, score test, and F test. Thirteen out of fifteen traits show significant heteroscedasticity. Further replication using new PRSs from the PGS catalog and independent samples (N = 23,620) from the UK Biobank confirmed the heteroscedasticity in ten traits. As a result, ten out of fifteen quantitative traits show statistically significant heteroscedasticity between the PRS and each trait. There was a greater variance of residuals as PRS increased, and the prediction accuracy at each level of PRS tended to decrease as the variance of residuals increased. In conclusion, heteroscedasticity was frequently observed in the PRS-based prediction models of quantitative traits, and the accuracy of the predictive model may differ according to PRS values. Therefore, prediction models using the PRS should be constructed by considering heteroscedasticity.

When to Use Different Inferential Methods for Power Analysis and Data Analysis for Between-Subjects Mediation

Several options exist for conducting inference on indirect effects in mediation analysis. Although methods that use bootstrapping are the preferred inferential approach for testing mediation, they are time-consuming when the test must be performed many times for a power analysis. Alternatives that are more computationally efficient are not as robust, meaning accuracy of the inferences from these methods is more affected by nonnormal and heteroskedastic data. Previous research has shown that different sample sizes are needed to achieve the same amount of statistical power for different inferential approaches with data that meet all the statistical assumptions of linear regression. By contrast, we explore how similar power estimates are at the same sample size, including when assumptions are violated. We compare the power estimates from six inferential methods for between-subjects mediation using a Monte Carlo simulation study. We varied the path coefficients, inferential methods for the indirect effect, and degree to which assumptions are met. We found that when the assumptions of linear regression are met, three inferential methods consistently perform similarly: the joint significance test, the Monte Carlo confidence interval, and the percentile bootstrap confidence interval. When the assumptions were violated, the nonbootstrapping methods tended to have vastly different power estimates compared with the bootstrapping methods. On the basis of these results, we recommend using the more computationally efficient joint significance test for power analysis only when no assumption violations are hypothesized a priori. We also recommend the joint significance test to pick an optimal starting sample size value for power analysis using the percentile bootstrap confidence interval when assumption violations are suspected.

Methodological progress note: Machine learning methods in healthcare research.

Methodological progress note: Machine learning methods in healthcare research.

The Effectiveness of Using Android-Based Interactive Learning Media Development Results With The Assistance of Smart Apps Creator (SAC) on Social Aritmatic Materials

This study aims to: determine the effectiveness of the use of instructional media, and determine the practical effect of using instructional media on student learning outcomes. The research method used is descriptive quantitative which aims to describe the practicality of using learning media, the effectiveness of using learning media, and the effect of using learning media on student learning outcomes. The analysis technique used in this study to determine the effect of the practicality of using instructional media on student learning outcomes is simple linear regression analysis. The results showed that the practicality level of learning media was 80%, and the completeness level of students was 76%, so it can be concluded that the learning media developed met practical and effective criteria. The results showed that the assumptions of linear regression were met, namely the data were normally distributed, the data were homogeneous and there was no correlation between the data. So that a simple linear regression test can be carried out partially and the results obtained are tcount = 5.617 which is greater than the value ttable = 2.04841 which means H0 is rejected, so the practicality of using instructional media influences student learning outcomes.

Machine learning-based RC beam-column model parameter estimation and uncertainty quantification for seismic fragility assessment

The simplicity and computational efficiency of lumped plasticity beam-column element models have been widely utilized for nonlinear response estimation of reinforced concrete (RC) frame elements under seismic shaking. At present, a prevalent approach for estimating modeling parameters includes linear regression-based semi-empirical equations developed after calibration of experimental column test results with varying design details. Since the choice of modeling parameters affects the prediction of complex seismic behavior, an underlying assumption of static linear relationships may not be valid. Furthermore, experimental column test data is limited and typically sourced from multiple independent studies, reflecting significant heterogeneity prevailing in column properties. Consequently, the homoskedastic assumption of the prediction uncertainty in linear regression is questionable. This study addresses the above drawbacks through a Gaussian process regression (GPR) approach that is capable of analyzing nonlinear patterns in data sets, despite small sample sizes. The kernel-based framework of GPR also efficiently estimates the pointwise prediction uncertainty as opposed to the homoskedastic assumption of linear regression. Using a case study example of an archetypical reinforced concrete moment resisting frame building, the prediction uncertainty is propagated in the collapse fragility framework. Results reveal that overall fragility uncertainty can substantially change through the consideration of pointwise prediction uncertainty and better fitting models.

Perceived stress and its psychosocial and clinical correlates among patients with pulmonary tuberculosis: A cross-sectional study.

Although various psychosocial consequences of pulmonary tuberculosis (PTB) have been thoroughly evaluated, perceived stress has not been well studied. This study assessed perceived stress and its psychosocial and clinical correlates. An institution-based cross-sectional study was conducted among 410 PTB patients. Data were analyzed by Statistical Package for the Social Sciences (SPSS) v23. Independent sample t-test and Pearson correlation were used to test the association between perceived stress and other variables. Assumptions of linear regression were checked. Multiple regression analysis was done to identify statistically significant association at P < 0.05. Anxiety, perceived social support, and stigma were found to have significant association with perceived stress in multiple regression analysis. Perceived social support and duration of treatment were negatively significantly associated with perceived stress. Patients with PTB had high perceived stress, and moderate to strong significant correlation was observed among variables. Interventions tailored at addressing various psychosocial aspects of tuberculosis (TB) are needed.