Statistical Data Analysis

Lift-off velocity of saltating sand particles in wind-blown sand located at 1.0 mm above the sand bed surface was measured using a phase Doppler particle analyzer in a wind tunnel. The results show that the probability distribution of lift-off velocity can be expressed as a lognormal function, while that of lift-off angle follows an exponential function. The probability distribution of lift-off angle conditioned for each lift-off velocity also follows an exponential function, with a slope that becomes steeper with increasing lift-off velocity. This implies that the probability distribution of lift-off velocity is strongly dependent on the lift-off angle. However, these lift-off parameters are generally treated as an independent joint probability distribution in the literature. Numerical simulations were carried out to investigate the effects of conditional versus independent joint probability distributions on the vertical sand mass flux distribution. The simulation results derived from the conditional joint probability distribution agree much better with experimental data than those from the independent ones. Thus, it is better to describe the lift-off velocity of saltating sand particles using the conditional joint probability distribution. These results improve our understanding of saltation processes in wind-blown sand.

Copulas have become an important analytic tool for characterizing multivariate distributions and dependence. One is often interested in simulating data from copula estimates. The process can be analytically and computationally complex and usually involves steps that are unique to a given parametric copula. We describe an alternative approach that uses \probability{proportional{to{size" (PPS) random sampling with weights formed from the copula likelihood function. The method is flexible and can be applied to parametric and nonparametric marginal density estimates. The precision of the simulation can be calibrated by adjusting the density of the multidimensional grid used in the simulation process. The approach is fully transparent to any copula function with continuous random variables. An example evaluates a number of goodness-of-fit criteria and provides strong support for the validity and practicality of the method.

Introduction. Effects produced by occupational exposures on health of workers employed at the chemical productions have not been given sufficient attention.The aim of this study was to establish and perform hygienic assessment of specific development of occupational and somatic diseases in workers employed at ammonia / carbamide production.Materials and methods. We examined two hundred forty four workers employed at an ammonia/carbamide producing enterprise; the observation group was made of 165 production workers (aged 42.2±1.6 years; work records equaled 11.6±1.5 years) and the reference group included 79 administrative workers (42.9±2.8 years and 10.5±1.6 years accordingly, р=0.32–0.58). The groups were divided by age, work records, and occupations (р=0.13–0.97). Working conditions were assessed using sanitary-hygienic, chemical-analytical, and instrumental methods; workers’ health was assessed relying on the results obtained by medical-social surveys, general clinical, functional, instrumental, and laboratory tests.Results. Adverse working conditions at the analyzed production are determined by exposure to noise level beyond their safe limits; ammonia, nitrogen and carbon oxides, aromatic hydrocarbons, and carbamide dust in workplace air; work intensity. Working conditions at administrative workplaces belong to the hazard category 1–2 (permissible). Diseases of the ear are a priority occupational pathology; priority somatic diseases include those of the endocrine, respiratory, and cardiovascular system. Main production workers face almost tenfold risk of diseases against administrative staff. Morbidity among auxiliary production personnel includes the same occupational and somatic diseases; however, their likelihood is twofold lower. Prevalence of priority pathologies is almost two times higher among main production workers aged younger than 45 years among their peers involved in auxiliary operations; the difference reaches 2.6 times in the older age group.Limitations. The study results can be extrapolated only on workers employed in similar working conditions at ammonia/carbamide productions.Conclusion. Longer work records have an apparent adverse effect on prevalence of occupational and work-related diseases in workers employed at main ammonia/carbamide production since they increase likelihood of such pathologies by up to 20 times when reaching 10 years and by up to 13 times when exceeding 10 years.Compliance with ethical standards. The study was approved by the local ethics committee of Federal Scientific Center for Medical and Preventive Health Risk Management Technologies (the meeting report No.4 dated January 10, 2024) and accomplished in full conformity with the conventional research principles stated in the WMA Declaration of Helsinki (2013 edition). All participants gave informed voluntary written consent to participate in the study.Contributions: Ustinova О.Yu. – study design and writing the text; Kostarev V.G., Nurislamova Т.V. – study design, data collection; Vorobyeva А.А., Nosov А.Е. – writing the text, statistical data analysis; Babina S.V. – statistical data analysis; Ponomarev М.D. – data collection; Ponomarev А.L. – data collection and statistical analysis. All authors are responsible for the integrity of all parts of the manuscript and approval of the manuscript final version.Conflict of interest. The authors declare no conflict of interest.Funding. The study had no sponsorship.Received: September 23, 2025 / Revised: November 7, 2025 / Accepted: December 2, 2025 / Published: January 15, 2026

The aerial radiometric identification of granitic plutons by statistical analysis

Investigating potential risk factors for seasonal variation: an example using graphical and spectral analysis methods based on the production of milk components in dairy cattle

Military operations in Ukraine are a significant obstacle to the growth of investment rates and income from investment. According to the results of statistical analysis, most scientists conclude that there is a certain dependence on the trends of the volumes of these indicators. However, there is an objective need to prove the interdependence of these processes using economic and mathematical models. Therefore, the purpose of the article is to determine the relationship between investment volumes and income from them in Ukraine from both a phenomenon and a process perspective during the period of military operations and to identify the main areas for improving the investment climate of the Ukrainian economy. The object of the study is the processes of investment and income from investments. The subject of the study is to generalize the theoretical support and practical recommendations on the issue of interdependence of investment processes and the formation of income from them in the difficult economic conditions of military operations in Ukraine. The main research methods used are theoretical generalization, system analysis, analysis, synthesis – to substantiate the object, subject, factors that hinder investment, and to form directions for improving the investment climate in Ukraine, generating conclusions and directions for further research; statistical analysis of initial data, graphical method – to visualize the results of investment processes and obtaining income from them; regression analysis, forecasting method by growth curves, canonical analysis – to prove the existence of a close relationship and the presence of interdependence of investment processes and processes of obtaining investment income. The article presents the following main results. The prevailing opinions of scientists on the main trends in changes in investment volumes and income from their investment in wartime are summarized. Using statistical data, graphs of investment volumes and income were constructed quarterly for the period of military operations in Ukraine from 2022 to 2024, and pairwise regression models of the dependence of these variables were constructed, which proved the presence of a dependence between them in the period under study. Using growth curves, the relationship between investment volumes and income for the fourth quarter of 2024 and the first and second quarters of 2025 was predicted, and a system of equations between them was constructed using canonical analysis, which proved the existence of a significant interdependence between investment volumes and income from their investment from the standpoint of a process approach. The constructed economic and mathematical models proved the existence of a significant interdependence between investment volumes and income received from these processes for Ukraine during the war period. On this basis, factors inhibiting investment processes were identified, and directions for improving the investment climate in Ukraine were proposed. Keywords: wartime, investments, income, regression analysis, forecasting, growth curves, canonical analysis, factors inhibiting investment processes, directions for improving the investment climate.

The fatigue life of wind turbine cast components, such as the main shaft in a drivetrain, is generally determined by defects from the casting process. These defects may reduce the fatigue life and they are generally distributed randomly in components. The foundries, cutting facilities and test facilities can affect the verification of properties by testing. Hence, it is important to have a tool to identify which foundry, cutting and/or test facility produces components which, based on the relevant uncertainties, have the largest expected fatigue life or, alternatively, have the largest reliability to be used for decision-making if additional cost considerations are added. In this paper, a statistical approach is presented based on statistical hypothesis testing and analysis of covariance (ANCOVA) which can be applied to compare different groups (manufacturers, suppliers, test facilities, etc.) and to quantify the relevant uncertainties using available fatigue tests. Illustrative results are presented as obtained by statistical analysis of a large set of fatigue data for casted test components typically used for wind turbines. Furthermore, the SN curves (fatigue life curves based on applied stress) for fatigue assessment are estimated based on the statistical analyses and by introduction of physical, model and statistical uncertainties used for the illustration of reliability assessment.

Normally, the safety evaluation starts at a phase 1 study using healthy subjects, since the main purpose of phase 1 studies is to find out maximum tolerated doses and side effects. Sometimes, a multiple-dose study may be necessary to further understand the side effects of a treatment, since a specific side effect has been identified during the post-market safety evaluation after drugs are repeatedly administered to patients for treating a disease for a long time. Particularly, Phase 1 trial of interest here evaluates the effect of multiple doses of a drug on a renal function via measurement of a metabolite clearance in healthy subjects. In general, we know that the renal function will be adversely impaired by the drug intake. We propose a non-inferiority analysis allowing a small decrease in the renal function after the drug is taken. In this study, we discuss the design, power and sample size, and statistical data analysis.

International research collaborations access and integrate data collected in different countries. For different reasons, e.g., legislation, data owners need to control who has access to and how their data are analyzed. The analysis of data is performed in statistical software, which is usually called on top of a data management system, e.g., a database management system (DBMS). Therefore access to data is controlled by the DBMS, while statistical analyses are usually controlled by another system. To improve security we propose a novel architecture for executing statistical analysis on data stored in a DBMS. In the proposed architecture the statistical software is called from a DBMS. The architecture allows control of both data retrieval and statistical data analysis from one system, i.e., DBMS. We implemented a prototype for executing analysis programs by calling statistical software SAS from a relational DBMS IBM DB2 over data stored in DB2 database. This paper describes the proposed architecture and the implemented prototype.

Whether an experiment yields qualitative or quantitative outcomes, methods of statistical analysis require that we focus on certain numerical aspects of the data (such as a sample proportion x/n, mean \( \bar{x} \), or standard deviation s). The concept of a random variable allows us to pass from the experimental outcomes themselves to a numerical function of the outcomes. There are two fundamentally different types of random variables—discrete random variables and continuous random variables. In this chapter, we examine the basic properties and discuss the most important examples of discrete variables. Chapter 4 focuses on continuous random variables.KeywordsNegative BinomialSingle SpikeletBinomial ExperimentUnacceptable VoltageAcceptance Sampling PlansThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In order to properly manage the depletion of a petroleum reservoir one must understand its physical characteristics and their relationship to production performance. Often the challenge is to interpret an extensive amount of available information. This paper presents an approach to this common petroleum engineering problem using statistical analysis. Statistical analysis can provide an improved understanding of any reservoir. Beginning with the compilation of a comprehensive database, intrinsic relationships among physical parameters and production are characterized. Mapping of statistically processed data allows field-wide interpretation and visual comparison with the geological model. Probabilities of production success can be mathematically determined and used to focus optimization efforts on areas of the reservoir with favourable characteristics. The techniques provide an unbiased appraisal of the available data that can lead to re-examination of assumptions about the underlying mechanisms governing production behaviour. Technical hypotheses can be tested for consistency by determining if expected correlations are present in the data. A recent evaluation of the performance of a heavy oil waterflood at Golden Lake, Saskatchewan, is discussed to illustrate the application of the method. Modification of well completion practices and the reservoir depletion strategy have resulted from the study, and favourable areas for infill drilling have been identified. Introduction The initial challenge when analysing the performance of a petroleum reservoir is often to marshal and interpret an overwhelming amount of raw data. Technological advances in reservoir characterization and data processing have provided access to an abundant supply of numerical data. Coincidentally, the importance of understanding reservoir heterogeneity to maximize depletion efficiency is increasingly recognized. Complex variation of fluid and rock properties within reservoirs is the norm, demanding more detailed description and sophisticated technical analysis. Techniques for processing large databases and distilling critical knowledge are therefore of growing interest to the petroleum engineer. The data overload problem may be addressed by arbitrarily discarding information or by working with averaged values. A compromise between these shortcuts and utilization of all of the data available is usually necessary; however, the quality of technical analysis may be sacrificed by over simplification. A method of extracting the important information concealed in an extensive dataset is required. Determination of the significant factors controlling production success will usually suggest the proper course to optimize the field. Statistical analysis techniques are well suited to achieving these objectives. Statistical analysis of data is best regarded as a supplement to, not a replacement for, the engineering analysis typically undertaken to solve technical problems. As an integrated component of a study, it can generate ideas, provide direction, and confirm that the data actually supports the theoretical conclusions. The value added by statistical analysis comes from ensuring no important trends hidden in the data are overlooked, and from the improved level of confidence in the results. Petro-Canada initiated a comprehensive technical review of the Golden Lake heavy oil field in 1994. The scope of the work included standard waterflood analysis, laboratory experiments, field tests, and numerical simulation in conjunction with the statistical analysis discussed in this paper.

In the context of the digitalization of economies, new technologies and financial solutions that have emerged, contribute significantly to the development of the financial system. In this context, financial payment services register a remarkable evolution, contributing to solving the problems of the population and the economic agents in the field of payments. It is interesting to what extent the Republic of Moldova has advanced in the assimilation of new financial services offered by providers in the last period and what are the possibilities for their development in the future. In this article we aim: to analyze the payments market in the Republic of Moldova from a marketing perspective, to identify factors that limit the development of the payments market, and development opportunities. The research methods used are: analysis of the legislation in force governing the payment system, statistical data analysis, graphical method, analysis and synthesis. The results of the research revealed the tendency of: competition increase in this field, the necessitiy for payment service providers to market orientation, proliferation and efficient cooperation between partners in the financial ecosystem, financial education of customers, approach to customer according to the segmentation, enhance social and ethical responsibility in relation to clients.

Continuous Random Variables and Probability Distributions

The integration of generative AI into statistical analysis is revolutionizing medical research, offering unprecedented opportunities for enhancing data interpretation and educational methodologies. This paper explores the challenges and potential misuse of AI-generated outputs in statistical data analysis, specifically focusing on t-tests. Through a detailed examination, we highlight the pitfalls associated with over-reliance on AI and propose a robust framework for using AI tools like Julius AI in hypothesis testing. Our findings demonstrate that while AI can significantly aid in understanding and applying statistical tests, it is essential to use these tools correctly to avoid erroneous outputs and misinterpretations. By adopting a step-by-step approach, educators can empower medical students and researchers to leverage AI effectively, thereby improving their analytical skills and critical thinking. This framework not only enhances the learning experience but also ensures accurate and reliable data analysis, ultimately contributing to more robust and valid conclusions in medical research.

Experimental design and statistical data analyses are fundamental components of animal science research. Proper design of experiments and adequate sampling permits testing hypotheses raised by researchers and sets the stage for collecting required data and subsequent statistical analysis. When designing experiments, researchers should respect rules of randomization of treatments to avoid statistical bias and permit proper inference to be drawn. Use of sample sizes that result in adequate statistical power to identify the hypothesized differences among factor levels of interest is key and should be driven by formal processes determining such. Best practices for data collection should be performed to obtain high quality data by reducing collection (e.g., mislabeling, improper technique) and measurement errors. With sound data, appropriate and optimal statistical methods should be used to generate valid results. The statistical method deployed should be chosen based on assumptions about residuals (e.g., normality, correlation, and homogeneity) and on the type of data (e.g., quantitative continuous or categorical). The appropriate statistical model used should also be consistent with the experimental design to validate the respective test statistics. The science of statistics is changing rapidly. With the development of high-throughput technologies, the generation of large datasets, high performance and sophisticated models and the interest in Big Data, the training of animal science graduate students in data management and rigorous statistical analyses is more important than ever. In order to meet the demands of current trends, animal science graduate students must be trained in several complex statistical and computational skills to meet the challenges imposed by these complicated, sophisticated and nuanced analytical methods. The livestock production sector will benefit from improved training, use of advanced and appropriate experimental designs, and collection and analysis of quality data in research.