The manuscript examines the role of Artificial Intelligence (AI) and telemedicine in Improving healthcare access and services for older adults. The study focuses on digital consultations and early diagnosis and employs a descriptive research design. Data were collected from 120 older adults in rural and urban areas of Dehradun, Uttarakhand. The analysis uses statistical techniques including descriptive statistics, regression analysis, ANOVA, and chi-square tests through SPSS to evaluate telemedicine usage, challenges, and satisfaction levels among older adults.
This paper introduces a new three-parameter lifetime model, called the Sujatha Power Series (SPS) distribution, motivated by the need for more flexible distributions in reliability and survival analysis. The model is constructed by compounding the Sujatha distribution with a family of power-series distributions, allowing it to accommodate various density and hazard rate shapes, including the important bathtub form. Key distributional properties are derived, and parameter estimation is performed using maximum likelihood methods. The practical performance of the SPS distribution is demonstrated through applications to two real lifetime datasets and comparisons with established competing models. The results indicate that the proposed model provides superior or comparable fits, highlighting its usefulness as an effective and flexible tool for modeling lifetime data in reliability and survival studies.
Wheat productivity is greatly influenced by soil nutrient variability, especially in areas with agroclimatic diversity like Jammu. The study analyzed a comprehensive dataset comprising 5,196 soil samples and corresponding wheat yield records from the districts of Jammu, Rajouri, and Kathua in Jammu region. This work predicted wheat yield from soil factors using machine learning (ML) models. The ML models used include Random Forest, Gradient Boosting, Support Vector Regression, and Decision Trees, in conjunction with embedded and wrapper-based feature selection methods. The soil variables analyzed in this study included pH, EC, OC, N, P, K, S, Cu, Zn, Mn, and Fe. Among the tested machine learning models, Random Forest yielded the highest predictive accuracy, with RMSE = 2.6570, MAE = 2.1578, and MAPE = 44.87%. Recursive Feature Elimination identified an optimal subset of 10 soil predictors, with S, Mn, Zn, and EC emerging as the most influential variables for wheat yield estimation. In all models, sulfur (S), manganese (Mn), electrical conductivity (EC), and zinc (Zn) were consistently found to be the most significant predictors. In comparison to other models, Random Forest and Support Vector Machines generated more reliable and broadly applicable predictions, according to stability study using k-fold cross-validation. The study highlights the effectiveness of machine learning techniques, particularly Random Forest, in predicting wheat yield from soil parameters. The consistent importance of micronutrients like S, Mn, and Zn underscores the need for micronutrient-focused soil management strategies. These findings demonstrate the usefulness of data-driven approaches in heterogeneous soil and climatic conditions.
Proportion data is significant in many disciplines, including as economics, finance, reliability engineering, medicine, biology, and chemistry, since it serves as the basis for identifying trends, expediting procedures, and making well-informed conclusions that result in advances and innovations. For the representation and analysis of proportional data, the beta distribution is a standard model. In this paper, we explore the unit omega distribution [15] and their ordered properties such as the exact expressions, as well as recurrence relations, for the single moments of the order statistics (OSs) and record values. Additionally, various L-moment characteristics based on OS moments and record values were analyzed. The applicability of the unit omega distribution over beta distribution was demonstrated through its successful fit to the original IRT temperature dataset. For comparative assessment, the performance of the unit omega distribution was evaluated against the commonly used beta distribution using the Kolmogorov–Smirnov (KS) goodness-of-fit test. The results indicate that the unit omega distribution yields a smaller KS test statistic and a larger associated p-value compared to the Beta distribution, suggesting a superior fit to the observed data. These findings highlight the flexibility and effectiveness of the unit omega distribution in modelling bounded data. Furthermore, the study provides a strong foundation for future research, particularly in extending the analysis to generalized order statistics and progressive censoring schemes associated with the unit omega distribution.
Fisher’s exact test is a fundamental statistical tool for analyzing associations in 2×2 contingency tables, especially when dealing with small samples or sparse data common in preliminary medical studies. Despite its widespread use, misconceptions regarding its assumptions, application criteria, and interpretation persist. This tutorial provides a structured, practical guide to Fisher’s exact test. We begin with the theoretical foundation, contrasting it with the chi-square test and explaining its reliance on the hypergeometric distribution. The core of the tutorial features step-by-step manual calculations for educational clarity, followed by practical implementation guides using R, SPSS, and Stata. We address common pitfalls, including misuse in large samples, confusion between one- and two-tailed p-values, and the need for multiple testing corrections. Extensions to larger tables via the Fisher-Freeman-Halton test and Monte Carlo simulation are also discussed. By integrating theory with actionable examples, this tutorial aims to enhance statistical literacy and ensure the accurate application of Fisher’s exact test in clinical and epidemiological research, thereby improving the reliability and reproducibility of findings.
