Statistical Analysis Techniques Research Articles

Sustainable Livelihood Options Adopted by Rural Communities in Response to Climate Change Dynamics: A Case Study Approach in Vhembe District, South Africa

The Vhembe district of South Africa is dominantly rural, with agricultural practice being the main major livelihood activity. Furthermore, many of the rural subsistence farmers rely on rain-fed agriculture, making them highly vulnerable to climate change, particularly high temperatures. Changes in climate cause extreme losses in agricultural productivity, increasing the level of vulnerability among rural subsistence farmers. The study examines the livelihood options adopted by rural communities in response to climate change dynamics in the Vhembe district. Using qualitative and quantitative approaches, both primary and secondary data from 365 rural community members were employed. Questionnaires and semi-structured interviews were administered using purposive sampling and employed statistical analysis techniques. The results revealed that livestock production was the most dominant livelihood-resilient activity at 30%, while vendor activity is common in Ṱhohoyanḓou at 29%. The findings indicated that the highest establishment of water tanks was in the Madimbo area, with 51% as a form of livelihood resilience. Further, 70% of respondents demonstrated livelihood sustainability activities such as agricultural forums, conferences, and the practice of indigenous activities. The proposed strategies can be used by policymakers to effectively consider the most vulnerable groups and articulate unique local vulnerabilities.

Features of Medan's Millennials and Z-Gens as Social Media Users

Objectives: The study aimed at examining two generations behavior (Generation Y and Generation Z), especially on social media. The respondents are Gen Y and Gen Z in Medan City, North Sumatra Province, Indonesia. The study focuses on similarities and differences in digital communication behavior between these two generations on social media. Stakeholders such as the government that formulates public policies, industry, politicians, and so on, need to map and understand what the characteristics of social media users are their citizens. Methods: The researchers used quantitative methods with descriptive statistical analysis techniques with a total of 400 respondents. Data was collected using questionnaires and analyzed using the statistical utility SPSS. Results: Research has shown that Generation Y prefers Facebook, while Generation Z prefers Instagram and YouTube. Both generations spend 30–60 minutes on social media, especially at night. While the level of caution on social media tends to appear in Gen Z. A significant difference between the two generations can be seen in the topics they post on social media, Gen Y posts topics about family more often. Meanwhile, Gen Z posts more about achievements. But in terms of how to communicate with each other, these two generations are no different. The majority of these two generations interact online every day with their friends, be it through social media, chat applications, or gaming. Conclusions: Social media interaction has replaced interaction in the real world. Generation Y and Generation Z, as social media users, develop different communication behaviors compared to the other older generations.

DLCDroid an android apps analysis framework to analyse the dynamically loaded code

To combat dynamically loaded code in anti-emulated environments, DLCDroid is an Android app analysis framework. DL-CDroid uses the reflection API to effectively identify information leaks due to dynamically loaded code within malicious apps, incorporating static and dynamic analysis techniques. The Dynamically Loaded Code (DLC) technique employs Java features to allow Android apps to dynamically expand their functionality at runtime. Unfortunately, malicious app developers often exploit DLC techniques to transform seemingly benign apps into malware once installed on real devices. Even the most sophisticated static analysis tools struggle to detect data breaches caused by DLC. Our analysis demonstrates that conventional tools areill-equipped to handle DLC. DLCDroid leverages dynamic code interposition techniques for API hooking to expose concealed malicious behavior without requiring modifications to the Android framework. DLCDroid can unveil suspicious behavior that remains hidden when relying solely on static analysis. We evaluate DLCDroid’s performance using a dataset comprising real-world benign and malware apps from reputed repositories like VirusShare and the Google Play Store. Compared to state-of-the-art approaches, the results indicate a significant improvement in detecting sensitive information leaks, more than 95.6% caused by reflection API. Furthermore, we enhance DLCDroid’s functionality by integrating it with an event-based trigger solution, making the framework more scalable and fully automated in its analysis process.

Analysis of Factors that Influence Consumers’ Trust to Use AI Service: The Case of Mandiri Bank’s MITA Chatbot

The use of chatbots has seen rapid development across various sectors in recent years, including in the banking industry. This study evaluated and examined the relationship between factors that influence consumer trust in the use of the AI chatbot service, MITA, at Mandiri Bank. The importance of conducting this research lies in answering questions regarding the key factors that affect users' or customers' trust in chatbots, such as perceived benefits and ease of use, which influence the intention to adopt new technologies, particularly Mandiri Bank’s MITA services. A total of 266 survey samples were collected from users of the Mandiri Bank’s MITA chatbot via an online survey, using convenience sampling for sample selection. The statistical analysis technique used in this study was Partial Least Squares Structural Equation Modeling (PLS-SEM) through the SmartPLS software. This study shows that perceived usefulness and user comfort do not have a significant effect on user trust in Mandiri Bank’s MITA chatbot. However, other factors positively and significantly affected user trust in the MITA chatbot. This study is expected to encourage marketing managers to explore more broadly and deeply how to build user trust in the banking industry and its future implications

Recent Advancements in Anomaly Detection for Manufacturing Systems

Recent advancements in anomaly detection and root cause analysis have revolutionized manufacturing processes, enabling improved efficiency, quality control, and cost reduction. This review explores the latest developments in these fields, focusing on supervised and unsupervised anomaly detection methods, statistical techniques for root cause analysis, and predictive modeling approaches. Supervised anomaly detection methods, such as classification-based approaches, support vector machines, and decision trees, leverage labeled data to identify anomalies. Unsupervised techniques, including clustering-based methods (e.g., k-means and DBSCAN), density-based approaches (e.g., local outlier factor), and dimensionality reduction (e.g., PCA and autoencoders), detect anomalies without prior knowledge of anomalous instances. Statistical methods for root cause analysis, such as statistical process control, hypothesis testing, correlation analysis, and multivariate techniques (e.g., factor analysis), enable the identification of the underlying causes of manufacturing issues. Predictive modeling techniques, including time-series forecasting, machine learning for predictive maintenance, and optimization methods for process improvement, further enhance manufacturing efficiency. The integration of these techniques has led to significant improvements in quality control, equipment failure prediction, and supply chain optimization. However, challenges remain in handling complex, high-dimensional data and integrating domain knowledge with data-driven approaches. Future research directions include the development of explainable AI, edge computing, and federated learning for anomaly detection in manufacturing. This analysis offers a thorough examination of cutting-edge techniques for detecting anomalies and analyzing root causes, emphasizing their capacity to transform manufacturing operations and stimulate future advancements in these research areas. Keywords—anomaly detection, manufacturing, supervised and unsupervised anomaly detection

Explicit relation between thin film chromatography and column chromatography conditions from statistics and machine learning

In chemistry, empirical paradigms prevail, especially within the realm of chromatography, where the selection of separation conditions frequently relies on the chemist’s experience. However, the underlying rationale for such experiential knowledge has not been established or analysed. This study explicitly elucidates how chemists use thin-layer chromatography (TLC) to determine column chromatography (CC) conditions, employing statistical analysis and machine learning techniques. An experimental dataset of the CC is generated from the automatic platform developed in this study. On this basis, an “artificial intelligence (AI) experience” is generated through a knowledge discovery framework, where the relationship between the retardation factor (RF) value from TLC and retention volume from CC is unveiled in the form of explicit equations. These equations demonstrate satisfactory accuracy and generalizability, providing a scientific basis for the selection of the experimental conditions, and contributing to a better understanding of chromatography.

Merging textual and numerical databases: a steppingstone for statistical analyses of illegal events

Abstract This paper aims to define a methodological path—merging judgments and official statistical data—to organize complete, objective, and reliable data in a database, thus simplifying the analysis of illegal social phenomena. Judiciary judgments are a new data source: they deal with illegal events that describe social phenomena—even if they are only the "illegal" ones—and contain objective and reliable data and information. Judiciary judgments are also texts, so the first step is a statistical textual analysis and text mining techniques to discover information and organize it in a statistical database. The final database is obtained by integrating numerical data from other information sources. It therefore has statistical properties such as reliability, completeness and updating. Subsequent statistical analyses or modelling are then possible based on the entire set or subsets of data adequately extracted from the implemented statistical database. We present some results obtained from judgments about corruption in order to demonstrate the advantages of linking textual databases (textual analyses on judgments) and numerical databases (from ISTAT). The proposed methodology can benefit different stakeholders, such as researchers, policymakers, and other enforcement actors. It is independent of the specific software used and remains valid when applied to other illegal activities (e.g., organized crime, tax crime, and money laundering). Furthemore, the results may be even more effective if the institutional actors involved have access to judgments at all levels, thus overcoming potential privacy concerns. The methodology could also be used to support evidence-based policy in the fight against crime and illegal activities.

Question–Answer Methodology for Vulnerable Source Code Review via Prototype-Based Model-Agnostic Meta-Learning

In cybersecurity, identifying and addressing vulnerabilities in source code is essential for maintaining secure IT environments. Traditional static and dynamic analysis techniques, although widely used, often exhibit high false-positive rates, elevated costs, and limited interpretability. Machine Learning (ML)-based approaches aim to overcome these limitations but encounter challenges related to scalability and adaptability due to their reliance on large labeled datasets and their limited alignment with the requirements of secure development teams. These factors hinder their ability to adapt to rapidly evolving software environments. This study proposes an approach that integrates Prototype-Based Model-Agnostic Meta-Learning(Proto-MAML) with a Question-Answer (QA) framework that leverages the Bidirectional Encoder Representations from Transformers (BERT) model. By employing Few-Shot Learning (FSL), Proto-MAML identifies and mitigates vulnerabilities with minimal data requirements, aligning with the principles of the Secure Development Lifecycle (SDLC) and Development, Security, and Operations (DevSecOps). The QA framework allows developers to query vulnerabilities and receive precise, actionable insights, enhancing its applicability in dynamic environments that require frequent updates and real-time analysis. The model outputs are interpretable, promoting greater transparency in code review processes and enabling efficient resolution of emerging vulnerabilities. Proto-MAML demonstrates strong performance across multiple programming languages, achieving an average precision of 98.49%, recall of 98.54%, F1-score of 98.78%, and exact match rate of 98.78% in PHP, Java, C, and C++.

Recommending Variable Names for Extract Local Variable Refactorings

Extract local variable is one of the most popular refactorings. It is frequently employed to replace occurrences of a complex expression with simple accesses to a newly introduced variable that is initialized by the original complex expression. Consequently, most IDEs and refactoring tools provide automated support for this refactoring, e.g., to suggest names for the newly extracted variables. However, we find approximately 70% of the names recommended by these IDEs are different from what developers manually constructed, adding additional renaming burdens to developers and providing limited assistance. In this paper, we introduce VarNamer , an automated approach designed to recommend variable names for extract local variable refactorings. Through a large-scale empirical study, we identify key contexts, such as variable initializations and homogeneous variables (variables whose initializations are identical to that of the newly extracted variable), that are useful for composing variable names. Leveraging these insights, we developed a set of heuristic rules through program static analysis techniques, e.g., lexical analysis, syntax analysis, control flow analysis, and data flow analysis, and employ data mining techniques, i.e., FP-growth algorithm, to recommend variable names effectively. Notably, some of our heuristic rules have been successfully integrated into Eclipse , where they are now distributed with the latest releases of the IDE. Evaluation of VarNamer on a dataset of 27,158 real-world extract local variable refactorings in Java applications demonstrates its superiority over state-of-the-art IDEs. Specifically, VarNamer significantly increases the chance of exact match by 52.6% compared to Eclipse and 40.7% compared to IntelliJ IDEA . We also evaluated the proposed approach with real-world extract local variable refactorings conducted in C++ projects, and the results suggest that the approach can achieve comparable performance on programming languages besides Java. It may suggest the generalizability of VarNamer . Finally, we designed and conducted a user study to investigate the impact of VarNamer on developers’ productivity. The results of the user study suggest that our approach can speed up the refactoring by 27.8% and reduce 49.3% edits on the recommended variable names.

Evaluating urban intersections: a comparative study of roundabouts versus protected U-turns—which is better?

Effective urban traffic management is a significant challenge in rapidly urbanizing areas with increased vehicular usage, impacting both congestion and safety. Busy cities, like many urban centers, face these issues. Roundabouts, known for their ability to reduce severe crashes, represent a traditional approach to intersection design. However, in high-traffic conditions, roundabouts can become less efficient, leading to increased congestion. Alternatively, protected U-turns, which are a relatively modern approach, are designed to handle high traffic volumes more effectively. They potentially reduce waiting times and improve overall traffic flow but may pose different safety and longitudinal challenges. This study aims to explore these complexities by analyzing the effectiveness of roundabouts and protected U-turns in the context of the unique urban landscape of the Hail City in Saudi Arabia. PTV VISSIM was used for dynamic traffic simulation, providing a realistic representation of traffic patterns in Hail City. Parameters such as vehicle queuing lengths, wait times, and overall intersection capacity are comprehensively evaluated. The simulation settings are adjusted to reflect different traffic densities and patterns, thus ensuring a thorough understanding of the performance of each intersection type under varied conditions. Additionally, various statistical analysis techniques were used to better understand the relationship between the independent and dependent variables. Results showed scenarios of high traffic volume, roundabouts led to longer vehicle queues and increased wait times. On the other hand, protected U-turns exhibited a notable capability to manage high-volume traffic, effectively reducing congestion and improving intersection throughput. This research may contribute to urban traffic management literature, offering insightful guidelines for future intersection design and traffic flow optimization in rapidly urbanizing cities.

The Effect of Recruitment, Selection, and Training on Employee Performance at PT Darium Abadi

The purpose of the study was to determine the effect of Recruitment, Selection, and Training on Employee Performance at PT Darium Abadi, partially or simultaneously. Research using associative methods with a quantitative approach. The sample was 94 people. Data collection techniques and tools with questionnaires. Inferential statistical analysis techniques for hypothesis testing. Data processing with SPSS version 25. Research results (1) Recruitment has a positive and significant impact on employee performance. A good recruitment process helps companies find employees who fit their needs, thereby improving overall performance. (2) Selection shows a positive and significant influence on employee performance. A careful selection process ensures only candidates who meet the criteria are accepted, thereby increasing work effectiveness and productivity. (3) Training exerts the most positive influence on employee performance. Structured training helps improve employees' technical skills, efficiency, and motivation, which has a direct impact on work quality and productivity. (4) Simultaneously, the three variables have a positive and significant effect on employee performance. This research confirms the importance of integrated HR management to achieve optimal employee performance. Recruitment, selection, and training are three key elements that are interrelated in influencing employee performance at PT Darium. These three processes work in concert.

Decoding Dominant Narratives on Montenegro in the German Press - A Corpus-Driven Analysis (2016-2023)

In the light of the EU’s revised approach to Balkan enlargement, with Montenegro being a frontrunner in the process, media coverage of Montenegro in the German press has become more present in recent years. This research aimed to explore representations of Montenegro in four prominent German newspapers. The contribution provides a corpus-driven approach applied to a dataset comprised of articles focused on Montenegro in the timeframe 2016-2023. Through statistical text analysis techniques such as z-score and co-occurrences calculations, the paper explores the complex interplay of socio-political, historical, and international factors influencing the country’s portrayal in the German press. The results reveal significant variations in perspectives and levels of representation across newspapers, indicative of their ideological stance. Despite its relatively modest geographical size, Montenegro attracts a moderate level of attention in these German newspapers, as evidenced by linguistic markers identified through statistical analysis. As a pioneering study, this paper contributes to a deeper understanding of how the image and perception of countries like Montenegro are shaped within foreign press contexts.

AI-Based Energy Management and Prediction System for Smart Cities

In recent years, there has been an increasing interest in smart city concepts in response to a growing demand for more sustainable and efficient urban spaces. In this paper, an in-depth exploration of load forecasting in an innovative city framework is carried out through the building of a software application with a graphical user interface (GUI) and prediction using long short-term memory (LSTM). To fulfill their role in enabling intelligent cities, accurate load forecasting is needed to enable energy management, reduce peak demand, and allow for well-informed decisions and energy distribution optimization. Specifically, in this work, we consider the application of LSTM to load forecasting systems within the context of a smart city. Since LSTM has a high capability in capturing complicated temporal structures, it is indeed an efficient approach to load forecasting, especially considering long-term dependencies. We focus on the strength of LSTM in comparison with the traditional statistical analysis and other machine learning techniques, most notably, LSTM’s ability to handle nonlinear and dynamic load behavior typical in innovative city energy systems. The GUI is the system's front end where the user enters the data, and at this point, we have the city officials, energy managers, and the community people and get their customized load forecast. The discussion of the outcomes of the experiment with the cross-platform GUI application and the improved LSTM forecasting model is given. Based on the evaluation of the model in terms of accuracy and performance, the research is done in an accepted manner to predict the complex load pattern in a smart city environment by proving the efficiency of the LSTM model. The effectiveness of different decisions in various cases is evaluated to consider the influence on decision-making processes and energy optimization; various cases demonstrate how the proposed GUI application is helpful in facilitating better management of energy. This research will, therefore, endeavor to create knowledge in the area of energy management to foster the development of effective intelligent cities.

Effect of anxiety on students’ academic performance at secondary school level researchers

Anxiety has numerous hindering factors that distract students from achieving satisfactory results in their examinations. The purpose of this study was to explore factors anxiety and their effects on students' academic performance at the secondary school level. For this purpose, three research objectives were made. The quantitative research design was used for this study and a convenient sampling technique was used to select the sample of 200 public secondary school teachers from Killa Saif Ullah and Muslim Bagh. The questionnaire comprised 36 close-ended statements developed on 5 5-point Likert-type scale. The reliability of the questionnaire was found at 0.81. The data were analyzed by using analysis and descriptive statistical techniques. The data were analyzed using descriptive and inferential statistics and an independent t-test was used through SPSS to find out the difference between genders' views. The findings of the study indicate that factors of anxiety (academic pressure, exams and assessments, parental expectations, competition, absent-mindedness, learning difficulties, and teacher-student relationships) exist and have a great effect on the academic performance of students. However, the study suggested a few strategies through which students can overcome their anxiety. The study recommended that students should keep themselves busy in productive activities, they should think positively about things and keep themselves focused.

Navigating Equality in Schools: The Sociological Impact of Leadership on Student Success

In the pursuit of a just and equitable society, the role of education is vital. One of the key determinants of this success lies in the quality of leadership within educational institutions. The study aims to analyze the multifaceted impact of leadership characteristics, bias mitigation (BM) efforts, and community engagement (CE) on academic achievement (AA) within the school context. This article aims to examine the sociological impact of leadership in schools and its direct influence on promoting equality and ensuring the success of all students. The study employed a mixed-method research approach, utilizing structured questionnaires and interviews to collect data from diverse stakeholders, including 26 school leaders, 154 educators, 1,750 students, and 32 community members across 7 purposively selected schools. PLS-SEM statistical analysis techniques were employed to analyze the collected data. The findings suggest that inclusive and equity-focused leadership is positively correlated with higher academic achievement among diverse student populations. Mitigating biases among educators enhances educational outcomes. Community engagement by school leaders enhances fairness in the education system. Based on the existing body of knowledge, this research contributes by validating the critical role of inclusive leadership in enhancing AA across diverse student populations. It innovates by integrating strategies to mitigate biases among educators and promoting active CE as novel approaches to bolster equitable educational outcomes. The implications of policymakers prioritizing leadership training in diversity and equity include schools adopting bias awareness programs and enhancing community partnerships to improve educational equity and student support.

Перцептивный портрет Беловежской пущи в региональном языковом сознании

The article presents the results of an experimental study on the perceptual embodiment of the BELOVEZHSKAYA PUSHCHA concept as a fragment of regional worldview. The research aimed to characterize the way this concept is expressed in the linguistic consciousness of residents of the Brest region by modelling and analysing its perceptual component. The main research method applied in the paper is the anthropometric method, combined with statistical and contextual analysis techniques. The material consists of associative reactions to the stimulus phrase Belovezhskaya Pushcha, obtained by processing 300 respondent questionnaires. All the participants in the experiment are residents of the Brest region and represent three age groups: schoolchildren, students, and the working population. The perceptual features are arranged from the most to the least frequent. As a result of quantitative processing of the reactions, the following images of the concept were identified: chromatological, auditory, and olfactory. In each of these images, dominant and peripheral units were recorded. The analysis established that the boundaries between the modes are unclear. In a number of cases, to describe Belovezhskaya Pushcha respondents used a symbiosis of several modes, based on which tactile-visual, gustatory-olfactory, and visual-auditory images were recorded. The phenomenon of synaesthesia serves as a means of conveying one’s emotions and impressions in connection with the visit to Belovezhskaya Pushcha. The analysis of the data obtained showed that the BELOVEZHSKAYA PUSHCHA concept is communicatively relevant in the perception of residents of the Brest region: in the regional worldview, not only the conceptual component is updated, but also a multi-level perceptual image filled with unique connotations is modelled.

Growth and Liquidity on Firm Value: The Role of Mediating Profitability

Companies in the food and beverage subsector are one of the industries that attract investors and play a significant role in driving Indonesia's economic growth. Company value provides an overview of the company's ability to operate the assets owned by the company. This study aims to determine the effect of Growth and liquidity on firm value with the role of profitability as a mediating variable in food and beverage sub-sector companies listed on the Indonesia Stock Exchange for the period 2014 - 2023. The data collection method used in this study is purposive sampling of food and beverage sub-sector companies listed on the Indonesia Stock Exchange for 2014 - 2023, with a selected sample of 11 companies. Data processing was done using the SPSS application, which used descriptive statistical data analysis techniques and inferential statistics. The results of this study indicate that Growth has no significant effect on firm value. Liquidity has a significant negative effect on firm value. Profitability has a significant positive impact on firm value. Growth has a significant positive impact on profitability. Liquidity has a significant positive impact on profitability. Profitability can mediate the effect of Growth and liquidity on firm value.

Development of Blockchain Technology in Financial Accounting

This study investigates the transformative potential of blockchain technology in financial accounting by examining its applications, challenges, and implications. The study begins with a review of blockchain’s origins and its ability to address inefficiencies, fraud risks, and transparency limitations in traditional accounting. A mixed-methods approach was employed, combining qualitative thematic analysis and quantitative statistical techniques. The qualitative analysis involved thematic coding of data from case studies and organizational reports, while the quantitative analysis assessed financial data using descriptive and inferential statistical methods. Eight organizations from diverse industries—including banking, retail, and technology—were purposively sampled to capture varied experiences and applications of blockchain technology. Key findings reveal blockchain’s ability to enhance transparency, efficiency, and security in financial transactions, offering significant advantages for financial reporting and auditing. However, challenges such as regulatory uncertainties, scalability concerns, and technical complexities remain barriers to its widespread adoption. This research provides actionable recommendations to overcome these challenges and maximize blockchain’s benefits in financial accounting. By integrating theoretical insights with empirical evidence, this study contributes to advancing the understanding of blockchain’s role in transforming financial practices, offering practical guidance for academia and industry practitioners alike.

Mental toughness of badminton athletes: the role of coach leadership type and coach-athlete relationship

The coach is someone who must know about all the basic needs for the fulfilment of conditions where athletes have the opportunity to achieve achievement. The relationship between coaches of athletes who are fostered must be a relationship that reflects a common view in realising what is aspired to. This study aims to determine (1) the effect of coach leadership type on the mental toughness of badminton athletes. (2) the influence of the coach-athlete relationship on the mental toughness of badminton athletes. (3) the effect of coach leadership type and coach-athlete relationship on the mental toughness of badminton athletes. This research is quantitative with an ex post facto approach. The subjects in the study were badminton athletes in clubs in Sleman Regency Yogyakarta totalling 73 athletes (male 43, female 30). The measuring instrument with qustioner. The statistical analysis technique used the Statistical Package for Social Science (SPSS) version 21 software. The level of statistical significance was set at p-value <0.05. Based on the results of data analysis, it can be concluded that: (1) There is a significant relationship between the coach's leadership type and mental toughness in badminton athletes (t-value 4.453, p-value 0.000<0.05). (2) There is a significant relationship between coach-athlete relationship on mental toughness in badminton athletes, (t-value 2.373, p-value 0.020 <0.05). (3) There is a significant relationship between the coach's leadership type and the coach-athlete relationship on the mental toughness of badminton athletes, (F-value 40.791, p-value 0.00 <0.05) and a contribution of 52.50%. It is recommended for coaches to be able to provide enthusiasm and encouragement for athletes to continue to fight and never give up in facing every match faced in order to achieve maximum achievement and results. Develop leadership as a coach and develop interaction skills in building relationships with athletes.

DIFFERENTIAL MUELLER-MATRIX MAPPING OF THE POLYCRYSTALLINE COMPONENT OF BIOLOGICAL TISSUES OF HUMAN ORGANS

The article presents the materials of diagnostic application of the method of differential Mueller-matrix mapping of optically anisotropic architectonics of the layers of soft matter of the female reproductive sphere – histological sections of uterine biopsy. The efficiency and accuracy of differential diagnostics of benign and precancerous conditions of endometrial tissue are considered using statistical analysis of algorithmically reproduced maps of average values of linear and circular birefringence and dichroism parameters of optically anisotropic architectonics of representative samples of native histological sections of the uterine wall. The values of the balanced accuracy of differential diagnostics are presented by using the technique of statistical analysis of coordinate distributions of the mean values of the optical anisotropy parameters.