Advanced Statistical Techniques Research Articles

Enhancing Workplace Productivity: The Nexus of Organizational Culture and Policies in Public Institutions

Aim – This study aims to explore the intricate interplay between organizational culture, policies, and productivity within a major public institution in Ghana. The primary purpose is to investigate the relationships between organizational policies, characteristics of organizational culture, and non-financial indicators of organizational productivity. Design/methodology/approach – This study utilizes a descriptive survey design, using a sample of 119 respondents randomly selected from a population of 1489 employees. Advanced statistical techniques, including Pearson Correlation and Multiple Linear Regression, are employed to identify relationships and test hypotheses. Findings – The results indicate positive and statistically significant relation-ships between organizational policies, cultural characteristics, and productivi-ty. Multiple linear regression analysis supported the hypothesis, demonstrat-ing a substantial influence of these factors on productivity. Notably, a moder-ate to strong positive correlation was found, highlighting the critical role of organizational culture in fostering improved performance. Limitations – Reliance on self-reported data raises concerns about social de-sirability bias. Additionally, the descriptive survey design allows for the identification of correlations, but causal relationships between variables can-not be established. Practical implications – This research highlights the importance of strategic policies and a supportive culture for public sector productivity. Clear policy implementation, leadership fostering a positive work environment, and en-gagement in policymaking likely enhance performance, particularly within diverse workforces. However, future research should explore causal relation-ships between these factors and productivity for deeper understanding and continuous optimization. Originality/value – This study investigates the combined effects of organiza-tional culture, policy clarity, and employee participation on productivity in a Ghanaian public institution. It contributes to the literature by confirming the positive influence of a supportive culture and well-defined policies, while emphasizing the importance of employee involvement in policy develop-ment. These findings offer practical insights for leaders seeking to enhance productivity in similar contexts.

Estimation of rice yield using multivariate analysis techniques based on meteorological parameters

This study aims to develop predictive models for rice yield by applying multivariate techniques. It utilizes stepwise multiple regression, discriminant function analysis and logistic regression techniques to forecast crop yield in specific districts of Haryana. The time series data on rice crop have been divided into two and three classes based on crop yield. The yearly time series data of rice yield from 1980–81 to 2020–21 have been taken from various issues of Statistical s of Haryana. The study also utilized fortnightly meteorological data sourced from the Agrometeorology Department of CCS HAU, India. For comparing various predictive models' performance, evaluation of measures like Root Mean Square Error, Predicted Error Sum of Squares, Mean Absolute Deviation and Mean Absolute Percentage Error have been used. Results of the study indicated that discriminant function analysis emerged as the most effective to predict the rice yield accurately as compared to logistic regression. Importantly, the research highlighted that the optimum time for forecasting the rice yield is 1 month prior to the crops harvesting, offering valuable insight for agricultural planning and decision-making. This approach demonstrates the fusion of weather data and advanced statistical techniques, showcasing the potential for more precise and informed agricultural practices.

Advancing Solar Energetic Particle Event Prediction through Survival Analysis and Cloud Computing. I. Kaplan–Meier Estimation and Cox Proportional Hazards Modeling

Solar energetic particles (SEPs) pose significant challenges to technology, astronaut health, and space missions. This initial paper in our two-part series undertakes a comprehensive analysis of the time to detection for SEPs, applying advanced statistical techniques and cloud-computing resources to deepen our understanding of SEP event probabilities over time. We employ a range of models encompassing nonparametric, semiparametric, and parametric approaches, such as the Kaplan–Meier estimator and Cox Proportional Hazards models. These are complemented by various distribution models—including exponential, Weibull, lognormal, and log-logistic distributions—to effectively tackle the challenges associated with “censored data,” a common issue in survival analysis. Employing Amazon Web Services and Python’s “lifelines” and “scikit-survival” libraries, we efficiently preprocess and analyze large data sets. This methodical approach not only enhances our current analysis, but also sets a robust statistical foundation for the development of predictive models, which will be the focus of the subsequent paper. In identifying the key determinants that affect the timing of SEP detection, we establish the vital features that will inform the machine-learning (ML) techniques explored in the second paper. There, we will utilize advanced ML models—such as survival trees and random survival forests—to evolve SEP event prediction capabilities. This research is committed to advancing space weather, strengthening the safety of space-borne technology, and safeguarding astronaut health.

Monitoring of group-structured high-dimensional processes via sparse group LASSO

In a general high-dimensional process, a large number of process parameters or quality characteristics is found to be featured through their dependencies and relevance. The features that have similar characteristics or behaviors in the process operation can be categorized into multiple groups. Thus, when a few quality characteristics in the process change, it is highly probable that the process shift would have occurred in a few relevant groups. Recently, several advanced statistical process control techniques are developed to monitor the changes in high-dimensional processes under sparsity. However, monitoring schemes that utilize the grouped pattern of the quality characteristics are sparse. This paper proposes a new method to monitor the high-dimensional process when the grouped structure of the process data is observed. The proposed method identifies the potentially changed groups and individual variables within the groups based on a modified sparse group LASSO (MSGL) model. Then, a monitoring statistic is obtained using MSGL-based likelihood function to test abnormality of the process. Extensive numerical studies are conducted to demonstrate the effectiveness and efficiency of the proposed method. In addition, a real-life application of a liquefied natural gas process is presented to illustrate the proposed method.

A scoping review of research on second language test preparation

Test preparation has garnered considerable attention in second language (L2) education due to the significant implications that successful performance on a language test may have for academic advancement, future career opportunities, and immigration prospects. Meanwhile, an overemphasis on test preparation has been criticized for encouraging the cultivation of construct-irrelevant test-taking strategies at the expense of developing general language proficiency. To systematically explore how test preparation has been investigated in the literature, we conducted a scoping review of 66 studies on L2 test preparation. Specifically, this study examined the key characteristics of publications on test preparation, the main themes explored, the study and participant characteristics, as well as the essential aspects of their research methodologies. The results of this review revealed various trends in the literature on L2 test preparation, such as the exclusive focus on English as the target language, the lack of diversity in stakeholders as participants, the dominance of international language tests, and the paucity of experimental studies that utilize advanced statistical techniques. In addition to interpreting the results of our analysis, we discuss the implications of this scoping review and outline several directions for future research on test preparation.

#1665 Mechanisms underlying long-term weight changes and their impact on all-cause mortality among chronic hemodialysis patients

Abstract Background and Aims Observational studies have demonstrated a significant association between long-term weight loss and weight gain and an increased risk of mortality among chronic hemodialysis patients. However, underlying mechanisms of these associations remain insufficiently explored. To address this knowledge gap, we conducted an in-depth analysis using a cohort of hemodialysis patients, benefiting from comprehensive baseline data, regular six-month evaluations, and an extended follow-up period. We employed advanced statistical techniques, including Lasso regression, to construct predictive models for long-term weight loss and gain. Furthermore, we conducted mediation analyses to elucidate whether weight change mediates the impact of major risk factors on all-cause mortality. Method This post-hoc analysis utilized data from participants in a previous randomized controlled trial conducted between 2006 and 2011, named as the Olmesartan clinical trial in Okinawan patients under the OKIDS (OCTOPUS) study. Subsequently, we tracked participants' mortality outcomes until July 2018. At the time of enrollment, we collected comprehensive baseline information, encompassing demographics, medical history, and laboratory data. Throughout the trial, participants' body weights were meticulously recorded every six months. Our analysis was restricted to patients who survived for at least two years post-enrollment, ensuring a minimum of five body weight measurements during the trial. Lasso regression was employed in training datasets (comprising 60% of participants) to identify crucial risk factors associated with long-term weight gain (>1 kg per year) or weight loss (<−1 kg per year) among chronic hemodialysis patients. These selected models were subsequently applied to paired validation datasets (comprising 40% of participants), and receiver operating characteristic (ROC) curves were used to calculate the area under the curve (AUC), aiding in the identification of key risk factors for long-term weight changes. Subsequently, we conducted mediation analyses to investigate whether weight change mediated the relationship between risk factors and all-cause mortality. Results Our analysis included 404 out of 461 OCTOPUS participants, with 60.9% being male, a mean age of 59.2 years (±11.7), and a mortality rate of 41.6%. The median follow-up period extended to 10.3 years. In the training datasets, Lasso models identified critical predictors for weight increase, including a history of diabetes, younger age, normal cardio-thoracic ratio (CTR) (<50% for males and <53% for females), and BMI <20. Conversely, predictors for weight decrease included BMI ≥20, serum sodium <135 mEq/L, serum albumin <3.5 g/dL, and older age. Weight increase demonstrated higher predictability (mean AUC of 0.59) compared to weight decrease (mean AUC of 0.55) in the validation datasets. Mediation analyses revealed that weight loss mediated 30% of the impact of older age (≥70 years old) on all-cause mortality (P = 0.068). Conclusion Our findings suggest that, among chronic hemodialysis patients, long-term weight increase is associated with a history of diabetes and younger age, while long-term weight loss is linked to BMI (≥20), hyponatremia, hypoalbuminemia, and older age. Notably, long-term weight loss appears to explain 30% of the increased risk of all-cause mortality associated with older age (≥70 years old).

Regional Differences and Spatial-Temporal Evolution Characteristics of Digital Economy Development in the Yangtze River Economic Belt

Digital economy has emerged as one of the primary driving forces for economic globalization. However, assessing digital economy development in a robust and scientific manner remains a great challenge. This paper proposes an evaluation system with measurement errors correction to accurately research the regional differences in and the spatial-temporal evolution characteristics of digital economy development in the Yangtze River Economic Belt (YEB), combining the entropy method, the Dagum–Gini coefficient, an σ convergence model and grey correlation analysis. The results present that the digital economy development index in the YEB rose from 2012 to 2021, with the greatest weight being social livelihood benefits. Meanwhile, there were noticeable regional differences in digital economy development in the YEB; in particular, the middle reaches showed obvious convergence. The grey correlation degree between the influence factors and the digital economy development ranged from 0.5286 to 0.9144, demonstrating a robust positive correlation. The theoretical framework of this paper integrates economic development models with advanced statistical analysis techniques, providing a robust analytical perspective for examining the complexities of digital economy evolution. The insights offer a blueprint for policymakers seeking to foster a robust and equitable digital economy, underscoring the potential of data-driven policy formulations in navigating the intricate landscape of economic globalization.

Analyzing the correlation between body composition variables and cellular phase angle via computerized bioelectrical impedance analysis (BIA)

Background: Various diseases and pathological conditions, as well as inflammatory processes, either in isolation or in combination with advanced age, can lead to significant alterations in body composition. This study investigates the correlation between various body composition metrics and the cellular Phase Angle (PhA) through Computerized Bioelectrical Impedance Analysis (BIA) in a diverse age group of individuals ranging from 18 to 80 years. Objective: Our aim was to explore the interplay between body composition variables such as Body Mass Index, Lean Body Mass, Skeletal Muscle Mass, and their potential influence on the cellular integrity as indicated by PhA. Methods: Utilizing a dataset of 199 participants, we employed both linear and advanced machine learning models, including Random Forest regression, to analyze the predictive relationships and variable importance within our body composition metrics. Results: Initial analyses revealed strong correlations between mass-related measures and suggested complex relationships with the PhA, an indicator of cellular health and membrane integrity. The Random Forest model significantly outperformed simple linear regression in predicting PhA, emphasizing the nonlinear nature of these relationships and the importance of a comprehensive approach in body composition assessment. Conclusion: Our findings highlight the nuanced interactions between body composition variables and their collective impact on cellular health as measured by PhA. This study underscores the potential of utilizing PhA alongside traditional metrics for a more nuanced understanding of body composition and its implications for health. Future research should continue to leverage advanced statistical and machine learning techniques to further elucidate these complex relationships, with implications for nutritional, sports medicine, and biomedical fields. Initial analyses revealed strong correlations between mass-related measures and suggested complex relationships with the Phase Angle, an indicator of cellular health and membrane integrity. The Random Forest model significantly outperformed simple linear regression in predicting PhA, emphasizing the nonlinear nature of these relationships and the importance of a comprehensive approach in body composition assessment. Our findings highlight the nuanced interactions between body composition variables and their collective impact on cellular health as measured by PhA. This study underscores the potential of utilizing PhA alongside traditional metrics for a more nuanced understanding of body composition and its implications for health. Future research should continue to leverage advanced statistical and machine learning techniques to further elucidate these complex relationships, with implications for nutritional, sports medicine, and biomedical fields.

Assessment of the Efficiency of a Hybrid Photovoltaic and Photovoltaic Heating System (PV–Solar) in the Context of a Warehouse for a Housing Community in Poland

In light of global challenges such as the war in Ukraine and the depletion of fossil fuel resources, it is essential to explore sustainable energy solutions. Hybrid energy systems represent a potential solution, offering energy independence to urban housing estates and reducing CO2 emissions. This article aims to explore the feasibility of integrating photovoltaic systems (utilizing vacuum collectors) and combined utilities (system heat and electricity) in a hybrid setup, leveraging existing technical infrastructure with necessary modifications. A key aspect is to perform calculations on the amount of heat and electricity generated from these systems. The study analyzes the demand for heat and electricity among consumers compared to the estimated production from renewable sources. Calculations also include the potential energy savings and CO2 emission reductions achievable through the proposed solutions. The findings indicate that hybrid photovoltaic systems with heat storage could effectively address energy issues in urban housing estates, given adequate support and community involvement. The innovative methodology employed in this study encompasses both analytical and experimental research approaches. The analysis employs advanced statistical techniques and data integration to enhance understanding of the phenomena studied, while the experimental research provides robust results through controlled variable manipulation and precise measurement tools, thereby verifying the study’s objectives.

Analysis of the Impact of Urban Green Spaces on Mitigating the Urban Heat Island Effect

The Urban Heat Island (UHI) effect, characterized by higher temperatures in urban areas compared to their rural surroundings, poses significant challenges for energy consumption, air quality, and public health. This study examines the impact of urban green spaces on mitigating the UHI effect in four major Spanish cities: Madrid, Barcelona, Seville, and Valencia. Using high-resolution satellite imagery, ground-based temperature measurements, and advanced GIS and statistical modeling techniques, we quantified the temperature differences between green and non-green urban areas. Our findings indicate that green spaces, including parks, street trees, and green roofs, significantly reduce urban temperatures, with parks exhibiting the highest cooling effects. The spatial analysis revealed that UHI hotspots are concentrated in densely built-up areas, while green spaces effectively mitigate these effects. Seasonal variations show that the cooling benefits of green spaces are more pronounced during the summer months. This study highlights the importance of incorporating diverse and well-distributed green spaces in urban planning to enhance thermal comfort, reduce energy consumption, and improve urban livability. Policy recommendations are provided to guide the integration of green infrastructure in climate adaptation strategies for Spanish cities.

Development and validation of a risk prediction model for aspiration in patients with acute ischemic stroke

BackgroundAspiration is a frequently observed complication in individuals diagnosed with acute ischemic stroke, leading to potentially severe consequences. However, the availability of predictive tools for assessing aspiration probabilities remains limited. Hence, our study aimed to develop and validate a nomogram for accurately predicting aspiration probability in patients with acute ischemic stroke. MethodsWe analyzed 30 potential risk factors associated with aspiration in 359 adult patients diagnosed with acute ischemic stroke. Advanced statistical techniques, such as Least absolute shrinkage and selection operator (LASSO) and Multivariate Logistic regression, were employed to identify independent predictors. Subsequently, we developed a nomogram prediction model based on these predictors, which underwent internal validation through 1000 bootstrap resampling. Two additional cohorts (Cohort A n = 64; Cohort B, n = 105) were included for external validation. The discriminatory power and calibration performance of the nomogram were assessed using rigorous methods, including the Hosmer-Lemeshow test, area under the receiver operating characteristic curve (AUC), calibration curve analyses, and decision curve analyses (DCA). ResultsThe nomogram was established based on four variables: sputum suction, brain stem infarction, temporal lobe infarction, and Barthel Index score. The predictive model exhibited satisfactory discriminative ability, with an area under the receiver operating characteristic curve of 0.853 (95 % confidence interval, 0.795–0.910), which remained consistent at 0.852 (95 % confidence interval, 0.794–0.912) during the internal validation. The Hosmer-Lemeshow test (P = 0.394) and calibration curve demonstrated favorable consistency between the predicted and observed outcomes in the development cohort. The AUC was 0.872 (95 % confidence interval, 0.783–0.962) in validation cohort A and 0.877 (95 % confidence interval, 0.764–0.989) in validation cohort B, demonstrating sustained accuracy. DCA showed a good net clinical benefit of the nomogram. ConclusionsA nomogram for predicting the probability of aspiration in patients with acute ischemia has been successfully developed and validated.

Comprehensive Elemental Profiling of Romanian Honey: Exploring Regional Variance, Honey Types, and Analyzed Metals for Sustainable Apicultural and Environmental Practices.

We investigated the mineral concentrations of 61 honey samples from eight Romanian regions, employing advanced techniques to assess 30 chemical elements. Potassium emerged as the dominant element, showcasing significant variations across geographical locations. Essential minerals like calcium, magnesium, sodium, and manganese maintained consistent levels, while zinc, copper, and chromium were present in smaller proportions. Critically, lead and cadmium levels exceeded established safety limits in some samples, suggesting potential environmental contamination. Additionally, elevated levels of lithium, strontium, nickel, and aluminum were detected, hinting at possible atmospheric pollution. These findings highlight the importance of regional analysis, as mineral content varied significantly between locations. Furthermore, correlation analysis revealed interdependencies among elements, suggesting shared environmental influences. Advanced statistical techniques like hierarchical clustering and principal component analysis effectively captured the impact of geographical origin on honey composition. These insights contribute valuable information for future efforts in honey quality control, traceability systems, and regulatory measures. By providing valuable insights into environmental influences on honey composition, this study informs future research endeavors and paves the way for the development of robust regulatory measures to ensure honey safety for consumers.

Parameter Extraction of Single, Double, and Triple-Diode Photovoltaic Models Using the Weighted Leader Search Algorithm.

This study presents the parameter extraction of single, double, and triple-diode photovoltaic (PV) models using the weighted leader search algorithm (WLS). The primary objective is to develop models that accurately reflect the characteristics of PV devices so that technical and economic benefits are maximized under all constraints. For this purpose, 24 models, 6 for two different PV cells, and 18 for six PV modules, whose experimental data are publicly available, are developed successfully. The second objective of this research is the selection of the most suitable algorithm for this problem. It is a significant challenge since the evaluation process requires using advanced statistical tools and techniques to determine the reliable selection. Therefore, seven brand-new algorithms, including WLS, the spider wasp optimizer, the shrimp and goby association search, the reversible elementary cellular automata, the fennec fox optimization, the Kepler optimization, and the rime optimization algorithms, are tested. The WLS has yielded the smallest minimum, average, RMSE, and standard deviation among those. Its superiority is also verified by Friedman and Wilcoxon signed-rank test based on 144 pairwise comparisons. In conclusion, it is demonstrated that the WLS is a superior algorithm in PV parameter extraction for developing accurate models.

The Moderating Role of Reader Characteristics on E-Book Reading Intention in Thailand: A Multigroup Analysis

Purpose: This investigation explores the moderating role of reader characteristics in shaping individuals' intentions to engage in e-book reading in Thailand. Theoretical Framework: Drawing upon a diverse sample of readers in Thailand, this analysis employs advanced statistical techniques to assess the moderating effects of reader characteristics, including sex, age, education level, income, and devices used to read e-books. Design/methodology/approach: Utilizing a quantitative approach, the study analyzes responses from 701 Thai people who have engaged with a minimum of five e-books to understand the moderating role of reader characteristics on e-book reading intention in Thailand. Findings: The results from the structural equation model analysis showed that several factors in the proposed model had a significant influence on intention to read e-books. These factors include trust, diversity of e-book options, social norms, and resource usage. Practical implications: The results that come from this investigation contribute to the growing body of literature on digital reading behavior and offer practical implications for stakeholders in the publishing sector and government organizations that promote reading. By comprehending how reader characteristics moderate the factors influencing e-book reading intentions, tailored strategies can be developed to promote digital literacy and enhance the adoption of e-books in the Thai market. As the global landscape of reading continues to evolve, this research offers a precious viewpoint to explore the intricate interplay between individual characteristics and the digital reading experience in Thailand. Originality/value: Limited research has examined the moderating influence of reader characteristics on the intention to read e-books. Therefore, this study seeks to address this gap in the existing literature by conducting a multigroup analysis in the context of Thailand.

Further Clarification of Pain Management Complexity in Radiotherapy: Insights from Modern Statistical Approaches.

The primary objective of this study was to assess the adequacy of analgesic care in radiotherapy (RT) patients, with a secondary objective to identify predictive variables associated with pain management adequacy using a modern statistical approach, integrating the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and the Classification and Regression Tree (CART) analysis. This observational, multicenter cohort study involved 1387 patients reporting pain or taking analgesic drugs from 13 RT departments in Italy. The Pain Management Index (PMI) served as the measure for pain control adequacy, with a PMI score < 0 indicating suboptimal management. Patient demographics, clinical status, and treatment-related factors were examined to discern the predictors of pain management adequacy. Among the analyzed cohort, 46.1% reported inadequately managed pain. Non-cancer pain origin, breast cancer diagnosis, higher ECOG Performance Status scores, younger patient age, early assessment phase, and curative treatment intent emerged as significant determinants of negative PMI from the LASSO analysis. Notably, pain management was observed to improve as RT progressed, with a greater discrepancy between cancer (33.2% with PMI < 0) and non-cancer pain (73.1% with PMI < 0). Breast cancer patients under 70 years of age with non-cancer pain had the highest rate of negative PMI at 86.5%, highlighting a potential deficiency in managing benign pain in younger patients. The study underscores the dynamic nature of pain management during RT, suggesting improvements over the treatment course yet revealing specific challenges in non-cancer pain management, particularly among younger breast cancer patients. The use of advanced statistical techniques for analysis stresses the importance of a multifaceted approach to pain management, one that incorporates both cancer and non-cancer pain considerations to ensure a holistic and improved quality of oncological care.

Hydrogen storage properties of magnesium based alloys Mg67Ni(32-x)Nb1Alx (x = 1, 3, and 5) by using response surface methodology

Nano metals and hydrogen storage have attracted significant attention in recent years due to their numerous unique properties and wide range of applications. This study explores the synthesis of nanostructured Al and Nb-substituted Mg2Ni intermetallic compounds through high-energy ball milling and investigates their electrochemical performance for energy-related applications. The research emphasizes the critical influence of crystallinity and crystallite size on electrode material performance. Employing Response Surface Methodology (RSM), the study identifies key factors affecting discharge capacity. Notably, current density emerges as the most significant factor, contributing 73% to discharge capacity, as confirmed by perturbation plots. Interaction effects among the factors were found to be relatively insignificant concerning the chemical kinetics of the electrode material. Furthermore, a second-order polynomial equation was developed through RSM to quantitatively relate discharge capacity to composition, milling time, and current density, with a high R2 value of 98.3%. To optimize discharge capacity, a fuzzy parameter setting was generated based on the mathematical model, resulting in a predicted discharge capacity of 398.209 mAh g−1, closely aligned with actual experimental results (394.203 mAh g−1). This work showcases the significance of advanced statistical techniques in elucidating the intricate relationships governing electrochemical performance, particularly in the context of nanocrystalline materials.

Enhancing Wear Resistance of UHMWPE Composites with Micro MoS2 and Nano Graphite: A Taguchi-DOE Approach.

This study presents an in-depth investigation into the wear characteristics of ultrahigh-molecular-weight polyethylene (UHMWPE) composites reinforced with microsized MoS2 and nanosized graphite particles. The objective is to enhance the wear resistance of the UHMWPE by examining the effects of various parameters and optimizing the wear performance. To achieve this goal, wet wear tests were conducted under controlled conditions, and the results were compared between composites with micro MoS2 and nano graphite reinforcements. The Taguchi method was employed to design the experiments (DOE) using an L9 orthogonal array. Four key parameters, namely, reinforcement percentage, load, speed, and track radius, were varied systematically to analyze their impact on wear characteristics, including wear rate, frictional forces, and the coefficient of friction (COF). The data obtained from the experiments were subjected to analysis of variance (ANOVA) to identify the significant factors affecting wear behavior. Subsequently, the optimal wear parameters were determined through regression analysis, allowing for the prediction of wear characteristics under the optimum conditions. This research not only provides insights into the comparative performance of micro MoS2 and nano graphite reinforcements in UHMWPE composites but also offers a comprehensive approach to optimizing wear resistance by employing advanced statistical and experimental techniques. The findings contribute to the development of more durable and wear-resistant materials with potential applications in various industries, such as those investigated in the study, which are commonly employed, such as automotive, aerospace, medical devices, or manufacturing.

LITERATURE REVIEW: BASIC STATISTICS IN EDUCATIONAL RESEARCH

This paper provides a comprehensive exploration of the role and significance of basic statistics in educational research. Drawing upon historical foundations and theoretical frameworks, the study elucidates the evolution of statistical methodologies and their application in understanding complex educational phenomena. Key pioneers such as Francis Galton and Karl Pearson are highlighted for their foundational contributions, laying the groundwork for statistical analysis in education. The paper examines various statistical techniques, including descriptive and inferential statistics, correlation analysis, regression analysis, and analysis of variance (ANOVA), showcasing their relevance in analyzing diverse aspects of teaching, learning, and educational outcomes. Moreover, the integration of statistical methodologies into educational research is examined across domains such as educational management, policy-making, and teaching practice, emphasizing their role in informed decision-making, resource allocation, and policy formulation. Despite the benefits, challenges such as sample bias and misapplication of statistical tests are acknowledged, underscoring the importance of methodological rigor and statistical literacy among researchers and practitioners. Looking ahead, the paper discusses future directions in educational research, including the integration of advanced statistical techniques and efforts to promote statistical literacy. Overall, this paper offers a comprehensive framework for understanding the complexities of basic statistics in educational research, highlighting its transformative potential in driving positive change and innovation within the education sector.

Low-cycle fatigue design for reinforced high-strength concrete under high compressive stress

As engineering endeavors push the boundaries of material and design capabilities, the significance of understanding and mitigating fatigue in construction materials becomes paramount. This study specifically investigates the low-cycle fatigue performance of reinforced high-strength concrete (RHSC). Using rigorous data collection, we established a clear link between interpretable machine learning analysis and the fatigue properties of RHSC. A trained model was developed, yielding a straightforward formula tailored to low-cycle fatigue design considerations for RHSC. This model stands as a testament to the potential for marrying traditional engineering practices with advanced statistical techniques. Our results emphasize that, when appropriately applied, regression analysis can be instrumental in enhancing the durability and longevity of RHSC structures exposed to dynamic loadings. This research not only underscores the pivotal role of statistical methods in fatigue design but also champions the broader adoption of such techniques in evolving engineering landscapes.

Data-driven evolution of water quality models: An in-depth investigation of innovative outlier detection approaches-A case study of Irish Water Quality Index (IEWQI) model

Recently, there has been a significant advancement in the water quality index (WQI) models utilizing data-driven approaches, especially those integrating machine learning and artificial intelligence (ML/AI) technology. Although, several recent studies have revealed that the data-driven model has produced inconsistent results due to the data outliers, which significantly impact model reliability and accuracy. The present study was carried out to assess the impact of data outliers on a recently developed Irish Water Quality Index (IEWQI) model, which relies on data-driven techniques. To the author's best knowledge, there has been no systematic framework for evaluating the influence of data outliers on such models. For the purposes of assessing the outlier impact of the data outliers on the water quality (WQ) model, this was the first initiative in research to introduce a comprehensive approach that combines machine learning with advanced statistical techniques. The proposed framework was implemented in Cork Harbour, Ireland, to evaluate the IEWQI model's sensitivity to outliers in input indicators to assess the water quality. In order to detect the data outlier, the study utilized two widely used ML techniques, including Isolation Forest (IF) and Kernel Density Estimation (KDE) within the dataset, for predicting WQ with and without these outliers. For validating the ML results, the study used five commonly used statistical measures.The performance metric (R2) indicates that the model performance improved slightly (R2 increased from 0.92 to 0.95) in predicting WQ after removing the data outlier from the input. But the IEWQI scores revealed that there were no statistically significant differences among the actual values, predictions with outliers, and predictions without outliers, with a 95 % confidence interval at p < 0.05. The results of model uncertainty also revealed that the model contributed <1 % uncertainty to the final assessment results for using both datasets (with and without outliers). In addition, all statistical measures indicated that the ML techniques provided reliable results that can be utilized for detecting outliers and their impacts on the IEWQI model. The findings of the research reveal that although the data outliers had no significant impact on the IEWQI model architecture, they had moderate impacts on the rating schemes' of the model. This finding indicated that detecting the data outliers could improve the accuracy of the IEWQI model in rating WQ as well as be helpful in mitigating the model eclipsing problem. In addition, the results of the research provide evidence of how the data outliers influenced the data-driven model in predicting WQ and reliability, particularly since the study confirmed that the IEWQI model's could be effective for accurately rating WQ despite the presence of the data outliers in the input. It could occur due to the spatio-temporal variability inherent in WQ indicators.However, the research assesses the influence of data input outliers on the IEWQI model and underscores important areas for future investigation. These areas include expanding temporal analysis using multi-year data, examining spatial outlier patterns, and evaluating detection methods. Moreover, it is essential to explore the real-world impacts of revised rating categories, involve stakeholders in outlier management, and fine-tune model parameters. Analysing model performance across varying temporal and spatial resolutions and incorporating additional environmental data can significantly enhance the accuracy of WQ assessment. Consequently, this study offers valuable insights to strengthen the IEWQI model's robustness and provides avenues for enhancing its utility in broader WQ assessment applications.Moreover, the study successfully adopted the framework for evaluating how data input outliers affect the data-driven model, such as the IEWQI model. The current study has been carried out in Cork Harbour for only a single year of WQ data. The framework should be tested across various domains for evaluating the response of the IEWQI model's in terms of the spatio-temporal resolution of the domain. Nevertheless, the study recommended that future research should be conducted to adjust or revise the IEWQI model's rating schemes and investigate the practical effects of data outliers on updated rating categories. However, the study provides potential recommendations for enhancing the IEWQI model's adaptability and reveals its effectiveness in expanding its applicability in more general WQ assessment scenarios.