Statistical Applications Research Articles

Pendampingan Pengembangan Aplikasi Statistik Komoditi Berbasis Web pada Dinas Perkebunan Sumatera Selatan

This article discusses a community service program involving assistance in developing a web-based commodity statistical application at the South Sumatra Plantation Office. This activity aims to enhance data processing efficiency and disseminate information related to plantation commodities. This program involves training staff in the use of information technology and developing applications that meet user needs. The methodologies applied include needs analysis, system design, implementation, and application evaluation. The assistance results indicate a significant improvement in data accuracy and ease of information access, positively impacting policy decision-making in the plantation sector. This activity is expected to contribute to developing similar applications in other government agencies and encourage the use of technology in commodity data management.

New Modified Gamma and Beta Functions with an Application

This note presents a novel set of modified Gamma and Beta k functions. The authors introduce these new functions along with their first and second summation relations, functional relations, symmetry relations, Mellin transforms, and integral representations that incorporate algebraic powers and Mittag-Leffler functions, as well as those involving trigonometric terms, powers, and Mittag-Leffler functions. In contrast to other studies that generalize Gamma and Beta functions, this note highlights a statistical application, demonstrating the practical utility of the proposed functions across various fields.

Harnessing Long-Range Temporal Correlations for Advanced Epilepsy Classification

In this study, we suggest that detrended fluctuation analysis may reveal alternations in long-range temporal correlations associated with epileptic status. We consider two groups of subjects: patients with confirmed focal epilepsy, and healthy controls. Both groups were exposed to intermittent photic stimulation. Analysis based on event-related spectral perturbations revealed that photic driving in epileptic patients is higher in the α band and lower in other bands. This result is related to altered excitability in nonphotosensitive epilepsy. To prove this, we tested two hypotheses. First, we assess long-range temporal correlations using detrended fluctuation analysis, with the objective of evaluating whether this metric differs between epileptic patients and healthy controls through the application of between-subject statistics. Second, we investigate whether detrended fluctuation analysis provides more valuable insights into the aforementioned differences in comparison to traditional spectral analysis of brain signals. More precisely, we test if a machine learning algorithm trained on detrended fluctuation analysis-based features outperforms one trained on the spectral-based features in classifying between epileptic patients and controls. Furthermore, we study whether the features differ between classifiers by implementing feature importance assessment. Our findings demonstrate that the classifier based on detrended fluctuation analysis exhibits higher efficiency, and its features are notably distinct from those of the classifier based on spectral analysis. We postulate that the long-range temporal correlations captured via detrended fluctuation analysis reveal novel aspects of the epileptic brain response to intermittent photic stimulation, and they are more pronounced than the features captured via spectral analysis. Published by the American Physical Society 2025

Political Science Student Preferences for Online, Onsite, and Mixed-Mode Courses: Computer Literacy, Curricular Preferences, and Research Experiences

What explains Political Science (PS) student preferences for online, onsite and mixed online-onsite course mode? Much of the academic debate about students’ preferences for different course modes assumes correlates and determinants of those preferences as binary: If a factor positively predicts students’ preferences for online courses, the same factor would negatively predict preferences for onsite courses. In reality, this might not be the case, but we still have no explanation about what would predict those preferences. We frame our argument within the social cognitive theory, interaction equivalency theorem, and social integration theory, and test it with three sets of variables: students’ computer literacy, their preferences for vocational orientation of PS studies, and prior research experiences. We found computer literacy as well as preferences for philosophical, statistical, mathematical and computer application orientation of PS studies to positively predict all course modes considered; and that students’ prior research experiences with peers negatively predict preferences for all course modes considered. We test our hypotheses with data collected by surveying PS students in 11 universities in three Western Balkans countries, Albania, Kosovo, and North Macedonia, during the 2021–2022 academic year.

Influence of Financial Management Accountability and The Spirituality of Pastoral Services In the Parishes of The Tomohon Victory Towards The Achievement of Pastoral Work

The Manado Diocese Catholic Church implements a Church financial management system within the framework of transparent, objective and responsible financial management. In this context, it is necessary to involve lay pastoral staff who are considered capable and competent and have a high spirituality of service in order to realize the pastoral works of the Manado Diocese Church. This research aims to examine the phenomenon of the influence of accountability in parish financial management and the spirituality of pastoral servants in the context of achieving pastoral work. This research was conducted in all churches or parishes in the Kevikepan Tomohon area by taking 95 samples from 133 populations. The research method used is a quantitative research method with a survey approach. Through this method, researchers want to test hypotheses and at the same time explain the relationship between variables. The data analysis process uses the application of correlation statistics and multiple regression test to analyze the influence between variables and measure the level of relationship between each variable studied. The research results show that: First, there is a significant and linear influence between Financial Management Accountability and Pastoral Work Achievement, meaning that if Financial Management Accountability is implemented well and appropriately it will increase Pastoral Work Achievement in the Tomohon Kevikepan Parishes. Second, there is a relationship between the Spirituality of Pastoral Servants and the Achievement of Pastoral Work. This means that if the Spirituality of Pastoral Servants is interpreted well and effectively, then this will have the effect of increasing the Achievement of Pastoral Work in Tomohon Kevikepan Parishes. Third, the results of hypothesis testing and multiple correlation and multiple regression analysis prove that there is an influence between Financial Management Accountability and Pastoral Servant Spirituality together on Pastoral Work Achievement. Furthermore, it can be concluded that an accountable financial management model supported by the spirituality of pastoral servants can increase the achievement of pastoral work in parishes.

Analisis Determinan Makroekonomi Inflasi dan Konsumsi Rumah Tangga terhadap Penerimaan Pajak Pertambahan Nilai (PPN) dan Pajak Penjualan atas Barang Mewah (PPnBM) di Indonesia

This article identifies and examines the determinants of state revenue in the taxation sector specifically on VAT and STLG in Indonesia. The main objective of this study is to analyze whether inflation and household consumption affect VAT and STLG revenues along with how much influence these determinants have. The type of research used is quantitative with descriptive statistical analysis methods. Techniques to analyze the effect of inflation and household consumption on VAT and STLG revenue variables used descriptive analysis, classical assumption tests and also multiple linear regression tests using statistical application platforms. The results of the research analysis identified that simultaneously, inflation and household consumption affect VAT and STLG revenues. So that if inflation and household consumption increase, then VAT and STLG revenues also increase, as well as in the opposite condition. The findings are in line with previous research, theories and hypotheses formulated.

Quadratic Forms in Random Matrices with Applications in Spectrum Sensing.

Quadratic forms with random kernel matrices are ubiquitous in applications of multivariate statistics, ranging from signal processing to time series analysis, biomedical systems design, wireless communications performance analysis, and other fields. Their statistical characterization is crucial to both design guideline formulation and efficient computation of performance indices. To this end, random matrix theory can be successfully exploited. In particular, recent advancements in spectral characterization of finite-dimensional random matrices from the so-called polynomial ensembles allow for the analysis of several scenarios of interest in wireless communications and signal processing. In this work, we focus on the characterization of quadratic forms in unit-norm vectors, with unitarily invariant random kernel matrices, and we also provide some approximate but numerically accurate results concerning a non-unitarily invariant kernel matrix. Simulations are run with reference to a peculiar application scenario, the so-called spectrum sensing for wireless communications. Closed-form expressions for the moment generating function of the quadratic forms of interest are provided; this will pave the way to an analytical performance analysis of some spectrum sensing schemes, and will potentially assist in the rate analysis of some multi-antenna systems.

Evolution of Acceptance Behavior and Using E-commerce in Indonesia

The number of business entities influences the level of competitiveness of a country. Micro, Small and Medium Enterprises (MSMEs) are under intense pressure in Indonesia's business entities. MSME strategies are encouraged to be able to survive, be sustainable, and have a bright future (OJK-BJG Joint Research, 2020). The current situation would need to formulate various initiatives that can be taken to help MSMEs to get through the crisis with the best response so that MSMEs can survive and develop and continue growth. This study aimed to determine the dominant factors and behavior of receiving and using digital services in Indonesia. Sampling was carried out by conducting an online survey of users of e-commerce services. The data obtained were analyzed by the application of PLS ​​statistics. The total data obtained were 589 respondents with 17 invalid data, so there were 572 valid data. The results of the study show that the digital needs of SMEs in Indonesia that the intention to use digital services, i.e. Convenience (Perceive Convenience, 24.1%), Trust (Trust, 23.6%), Habit (Habit, 19.9%), Availability of Facilities (Facilitating Condition, 17.8%).In contrast, several other factors are not proven, namely Benefit (Performance Expectation 8.7%), Ease (Effort Expectation, 6.8%) and Risk (Risk, 3.5%). Intention to use digital services has an effect of 67.2%. The value of R2 in the model is 0.451.

Implementasi Strategi Layanan Prima Pada MAN 2 Kota Serang Sebagai Madrasah Model Berbasis Boarding School

Madrasas as Islamic educational institutions have several advantages compared to other types of schools. The advantages possessed by madrasas include: vitality, fighting power, endurance, adaptability and evolution, and diversity. This research uses a quantitative approach by creating and distributing questionnaires. Primary data was obtained directly from the results of distributing questionnaires to respondents consisting of 100 student representatives, 38 educational staff representatives and 72 teaching staff representatives. Because the data from this research was obtained through distribution of armatures using the Google Form application, the data processing process also uses statistical formula applications that are integrated with existing questionnaires and Google Forms. The research aims to understand the forms of MAN 2 Serang City's excellent service strategy as a madrasa model or as a Boarding School-based madrasa as well as certain factors that support and inhibit the implementation of the excellent service strategy. The results of the research show that Madrasah Aliyah Negeri 2 Serang City acts as a model madrasa based on Islamic boarding schools and is a center for the development of madrasas in the surrounding area. Implementation of quality education services at Madrasah Aliyah Negeri 2 Serang City focuses on providing learning needs, one-stop integrated services (PTSP), digital-based services, as well as facilities and infrastructure services.

Compositional Imprecise Probability: A Solution from Graded Monads and Markov Categories

Imprecise probability is concerned with uncertainty about which probability distributions to use. It has applications in robust statistics and machine learning. We look at programming language models for imprecise probability. Our desiderata are that we would like our model to support all kinds of composition, categorical and monoidal; in other words, guided by dataflow diagrams. Another equivalent perspective is that we would like a model of synthetic probability in the sense of Markov categories. Imprecise probability can be modelled in various ways, with the leading monad-based approach using convex sets of probability distributions. This model is not fully compositional because the monad involved is not commutative, meaning it does not have a proper monoidal structure. In this work, we provide a new fully compositional account. The key idea is to name the non-deterministic choices. To manage the renamings and disjointness of names, we use graded monads. We show that the resulting compositional model is maximal and relate it with the earlier monadic approach, proving that we obtain tighter bounds on the uncertainty.

Properties of fractional generalized entropy in ordered variables and symmetry testing

<p>Uncertainty measures are widely used in various statistical applications, including hypothesis testing and characterizations. Numerous generalizations of information measures with different extensions have been developed. Inspired by this, our study introduced the principle of the fractional generalized entropy measure and investigated its properties through stochastic comparisons and characterizations using order statistics and upper random variables. We explored the monotonicity and symmetry properties of the fractional generalized entropy, emphasizing conditions under which it uniquely identified the parent distribution. In the case of distributions that were completely continuous, The symmetrical nature of order statistics suggested that symmetry of the underpinning distribution. Based on the fractional generalized entropy measure in non-parametric estimate of order statistics, a new test for the symmetry hypothesis was put forward. This test offered the supremacy of not requiring the symmetry center to be specified. Additionally, an example of real-world data was shown to illustrate how the suggested technique might be applied.</p>

Estimation of Population Mean using Neutrosophic Exponential Estimators with Application to Real Data

One of the traditional problems in survey sampling is to estimate the population parameter like mean variance etc. This article investigates the mathematical derivations and application of neutrosophic statistics to address the challenges posed by imprecise, indeterminacies or ambiguous data, such as daily stock prices, weather forecast, social media sentiment and temperatures. The suggested estimators are highly useful for computing results while working with unclear, hazy, and neutrosophic-type data. These estimators produce answers that are interval-form rather than single-valued, which may give our population parameter a better chance of being off. We propose three novel neutrosophic exponential ratio-type estimators for the population mean, utilizing information of neutrosophic auxiliary variables. Expressions for bias and mean square error (MSE) of these estimators are derived using first-order approximations to assess their performance in terms of accuracy. To demonstrate their effectiveness, we apply the proposed estimators to real-life neutrosophic data sets. Additionally, a simulation study shows that our estimators outperform existing methods in terms of MSEs and percentage relative efficiency (PREs). This study further expands its originality by including pre-existing estimators into the neutrosophic framework, showcasing its versatility and adaptability. The results suggest that neutrosophic statistics provide a robust framework for analyzing uncertain data, facilitating more reliable decision-making in various applications.

Compare Noise Robust Least-Squares Method with Other Methods for Estimation of the Parameters of Frechet Distribution and Neutrosophic Generalization

The Frechet distribution is a versatile probability distribution that is used within a loose range in many important statistical fields, such as image processing, data analysis, and pattern recognition. It aims to explore and study the estimation of the parameters of the Frechet distribution using the noise-robust least squares method, as in the research paper, and it also has uses. There are many real-world scenarios. It is known that there is a growing challenge in estimating the parameter because of the noisy data. Depending on rigorous simulations and experimental analysis, we provide a novel powerful way to estimate the parameters for the Frechet Distribution Robust Least Squares approach to be flexible. Also, the results approach of this work will be very helpful in estimating the Frechet distribution parameters for diverse statistical applications. Also, we generalize our results to include the generalized neutrosophic case of this distribution dealing with neutrosophic numbers.

Research on the Integration of Data Statistics and Analysis in the Training of Private Equity Talents

The private equity industry is in its early stages of development and has huge potential for future development. In the specific training of private equity talents in schools, we have encountered many difficulties. The first thing we face is the formulation of a private equity training program. The private equity direction of our school's finance is set up to meet the huge demand of the economic society for private equity talents. This article will focus on the application of data statistics and analysis in the training of private equity talents.

Статистики вищих порядків в задачах виявлення сигналів на фоні корельованих негаусових завад

Signal detection in noise is critical in telecommunications, navigation and radar systems, image processing and biomedical research, where noise often deviates from a normal distribution, and sample values exhibit statistical dependence. Traditional methods for analyzing and designing such systems face significant limitations, including algorithmic and computational complexity, severely restricting their practical application. An effective approach to developing signal detection systems involves using moment and cumulant descriptions of random variables, simplifying the design of detection systems for signals with various probability density functions. The authors propose a novel approach based on one-dimensional (1D) and two-dimensional (2D) moment-cumulant models to describe correlated non-Gaussian processes. This approach enables the modification of the moment-based criterion for statistical hypothesis testing and synthesising polynomial stochastic decision rules for detecting signals in correlated non-Gaussian noise. The study demonstrates that the nonlinear processing of sample values and the application of higher-order statistics to describe the investigated processes account for the structure of non-Gaussian noise and its statistical dependencies. This reduces the error probabilities of the synthesized decision rules compared to traditional Gaussian models. The study aims to enhance the efficiency of signal detection systems under additive interaction with correlated non-Gaussian noise by developing new moment-cumulant models of the investigated processes, modifying the moment-based criterion for hypothesis testing, and designing polynomial decision rules. The study's practical significance lies in creating simple-to-implement algorithms with high accuracy that achieve lower error probabilities in decision rules compared to existing methods.

Pain Scales: Types and Comparison

Objective: Pain is a common and significant symptom in clinical practice, particularly among emergency and cancer patients. This article explores various pain scales and their clinical applications, alongside postoperative pain management in oncologic patients. A review of unidimensional pain scales, including the Numeric Rating Scale (NRS), Visual Analog Scale (VAS), Verbal Rating Scale (VRS), and Faces Pain Rating Scale (FPS), is presented. Additionally, the pathophysiology and pharmacological and non-pharmacological strategies for managing postoperative pain in cancer patients are discussed. Pain scales vary in sensitivity, ease of use, and clinical suitability. NRS is widely preferred for its simplicity and statistical applicability, whereas VAS offers higher sensitivity but practical limitations. Postoperative pain in oncologic patients is influenced by surgical trauma, psychological factors, and pre-existing pain conditions, with inadequate management leading to systemic complications and increased cancer recurrence risk. Effective management incorporates both non-opioid and opioid analgesics based on pain severity. Pain assessment tools and effective management strategies are critical for improving patient outcomes in clinical settings. Tailored approaches, particularly in oncologic postoperative care, can mitigate complications and enhance quality of life.

Perception of scientific research in Dentistry

Scientific research is the fundamental basis for the complete education of dental students from the beginning of their studies since it provides them with the necessary tools to deal with real situations. Objectives: To determine the level of knowledge about scientific research in dental students of the Universidad Regional Autónoma de Los Andes. Methodology and methods: A comparative, descriptive and cross-sectional study was used, applied to students enrolled from first to tenth semester of the dental career of the Universidad Regional Autónoma de Los Andes in the academic period May 2023 - September 2023. Application of descriptive statistics. Results: The results obtained in the survey of 840 students, show that there is an average knowledge of scientific writing with 44.05% and 3.1% say they have no knowledge of it, being this a weakness as students to conduct research work that can help to progress academically, thus gaining experience and benefiting favorably to their resume that later will open more doors as a professional or student. Conclusions: Based on the results, deficient knowledge about the relationship between scientific research and dentistry was identified. This situation leads us to implement a plan for the development of efficient strategies to improve scientific activity, which will undoubtedly increase the level of research of the graduate student and give him/her the opportunity to practice evidence-based dentistry in an adequate manner.

Identifikasi Keberadaan Jamur Udara dan Karakteristik Suhu, Kelembaban, dan Pencahayaan Ruangan di Puskesmas Panjatan II

Microorganisms in a room’s air are organisms that are carried by the air in the form of spores through air ventilation or people entering the room. Pathogenic fungi can spread and enter the human body which can cause infection. Laboratory studies explain that there are factors that can help fungal growth such as temperature, humidity and lighting. This research aims to identify the presence and types of airborne fungi and the characteristics of temperature, humidity and room lighting at the Panjatan II Community Health Center. This research method is experimental with a quantitative descriptive approach and the sample size of this research is 15 samples that was taken with a purposive sampling technique. The instruments used in this research were a hygrometer, luxmeter, PDA media, and LPCB staining. This research used a statistical application, namely SPSS to do a data processing and descriptive analysis which aims to see the characteristics of humidity, humidity and room lighting at the Panjatan II Community Health Center. This research found that there were airborne fungi like Aspergillus sp., and Candidia sp from 15 room samples in the media. In addition, the measurement results of temperature, humidity, and room lighting at the Panjatan II Community Health Center were 29-32°C temperatures, 65-69% humidity, and 70-160 Lux lighting. The conclusion of this research is that the type of fungus found in the Panjatan II Community Health Center room is a type of pathogenic fungus which consists of Aspergillus sp., and Candidia sp. The characteristics of room measurements on temperature, humidity and lighting characteristics exceed the standard requirements for air physical quality according to Minister of Health Regulation No. 2 of 2023.

A novel circular dynamics in financial networks with cross-correlated volatility and asset movements

In this paper, we propose a novel application of classical directional statistics to model the cross-correlation of asset volatility in financial networks. The proposed novel Circular Volatility Model (CVM) provides a framework for studying the interdependencies of financial assets whose returns exhibit periodic behaviors. By extending traditional volatility models into a circular framework, we establish new pathways for understanding the cyclicity inherent in market dynamics. Our model is rigorously grounded in classical \& directional statistics, utilizing von Mises distributions for parameter estimation and novel circular covariance structures. We offer formal derivations, maximum likelihood estimates, and a novel goodness-of-fit testing framework for this circular model. We establish our methodologies using simulation studies.

Characterization of pedagogical models in basic Social Studies education

Over the years, pedagogical models have guided the teaching process in the area of Social Studies, through dynamic environments to transmit scientific knowledge effectively. The main objective of the present study was to characterize the use of pedagogical models in the basic teaching of Social Studies. The methodology employed was of mixed approach, combining quantitative and qualitative methods in data collection. The population selected for the study included 50 teachers from four types of educational institutions. Survey and interview techniques were used, in this sense, quantitative data were analyzed through the application of descriptive statistics and for the interview data, qualitative coding was used. The results revealed deficiencies in the application of a standardized pedagogical model adapted to the current generation of students. The teachers expressed their interest in acquiring new teaching methods to improve the learning of their subject, however, they considered that the humanistic model needs to be worked on with the students. To conclude, the detailed characterization of the importance, characteristics and timely application of the pedagogical models was fundamental for the strengthening of knowledge in teachers and students.