Statistical Testing Methods Research Articles

The Impact of Ownership Authority on Cash Conversion Cycle in Public Hospitals: A Comparative Analysis of Provincial and County Hospitals

(1) Background: The aim of the paper is to identify and assess if there are differences concerning the cash conversion cycle (CCC) according to different types of ownership authority of hospitals. The main research hypothesis developed for the purpose of this study assumes that the ownership authority is of great importance for the short-term financial management analyzed through the prism of the cash conversion cycle in hospitals. (2) Methods: A statistical hypothesis testing method is employed in the empirical part of the study, analyzing differences both in the values of the cash conversion cycle itself as well as in the values of its components, i.e., inventory conversion period, receivables conversion period, and payables conversion period. The research sample consists of public hospitals subordinated to different types of ownership authorities, i.e., provinces and counties. (3) Results: It turns out that there are indeed statistically significant differences between the provincial and county hospitals as far as the cash conversion cycle and its components are analyzed, which supports the research hypothesis. (4) Conclusions: The county hospitals are characterized by a better financial situation as compared to their provincial counterparts concerning CCC management.

Analysis Brand Image, Product Quality, And Brand Ambassador On Customer Purchase Decision For Scarlett Whitening Product In Sidoarjo

The tight business competition between companies today means that all companies want to be the best to attract the attention of consumers. Scarlett Whitening is a company that reached a sales record of 1 billion in 4 hours when collaborating with Exo. EXO is used as a strategy to attract consumers to buy this product. However, Scarlett Whitening consumers do not only look at the type of product or quality, consumers also look at the advantages in terms of the brands that are most popular. This research aims to examine the influence of Brand Image, Product Quality, and Brand Ambassador on Consumer Purchasing Decisions in Sidoarjo. This type of research is quantitative using descriptive statistical data analysis methods and testing methods including: Validity Test, Reliability Test, Classical Assumption Test, and Hypothesis Test. Data collection took the form of distributing questionnaires which produced valid data for 100 samples. The results show that the brand image variable has an insignificant positive influence. Meanwhile, the product quality and brand ambassador variables have a positive and significant effect.

Empowering Women in Saudi Workforce: HR, Job Satisfaction, and Policies for Work–Life Balance

The present study investigates the sustainable influence of human resources (HR) support on job satisfaction and work–life balance among female employees in Saudi Arabia, underlining its significance in advancing social sustainability within the workforce. It seeks to analyze the role of job satisfaction as a mediator and organizational policies as moderators in these relationships. This study analyzed data from a simple random sample of 145 employed women in different sectors in Saudi Arabia, utilizing exploratory and confirmatory factor analyses, as well as inferential statistical methods for hypotheses testing. We conducted an evaluation of mediating and moderating effects in the investigated relationships using the PROCESS macro for SPSS. The findings show a strong positive relationship between HR support, work–life balance, and job satisfaction in Saudi Arabia’s female workforce. The mediation function of job satisfaction in the relationship between HR support and work–life balance emphasizes the critical role of positive working experiences in achieving work–life balance. Furthermore, this research highlights organizational policies as a crucial mediator, indicating that implementing supportive and progressive policies might amplify the beneficial impacts of HR practices on work–life balance and job satisfaction. The present study enhances the existing body of knowledge by offering empirical findings on the importance of culturally customized sustainable HR practices and the crucial function of organizational policies in advancing gender equality and achieving a healthy work–life balance.

A realistic benchmark for differential abundance testing and confounder adjustment in human microbiome studies

BackgroundIn microbiome disease association studies, it is a fundamental task to test which microbes differ in their abundance between groups. Yet, consensus on suitable or optimal statistical methods for differential abundance testing is lacking, and it remains unexplored how these cope with confounding. Previous differential abundance benchmarks relying on simulated datasets did not quantitatively evaluate the similarity to real data, which undermines their recommendations.ResultsOur simulation framework implants calibrated signals into real taxonomic profiles, including signals mimicking confounders. Using several whole meta-genome and 16S rRNA gene amplicon datasets, we validate that our simulated data resembles real data from disease association studies much more than in previous benchmarks. With extensively parametrized simulations, we benchmark the performance of nineteen differential abundance methods and further evaluate the best ones on confounded simulations. Only classic statistical methods (linear models, the Wilcoxon test, t-test), limma, and fastANCOM properly control false discoveries at relatively high sensitivity. When additionally considering confounders, these issues are exacerbated, but we find that adjusted differential abundance testing can effectively mitigate them. In a large cardiometabolic disease dataset, we showcase that failure to account for covariates such as medication causes spurious association in real-world applications.ConclusionsTight error control is critical for microbiome association studies. The unsatisfactory performance of many differential abundance methods and the persistent danger of unchecked confounding suggest these contribute to a lack of reproducibility among such studies. We have open-sourced our simulation and benchmarking software to foster a much-needed consolidation of statistical methodology for microbiome research.

Depth for samples of sets with applications to testing equality in distribution of two samples of random sets

This paper introduces several depths for random sets with possibly non-convex realisations, proposes ways to estimate the depths based on the samples and compares them with existing ones. The depths are further applied for the comparison between two samples of random sets using a visual method of DD-plots and statistical testing. The advantage of this approach is identifying sets within the sample that are responsible for rejecting the null hypothesis of equality in distribution and providing clues on differences between distributions. The method is justified using a simulation study and applied to real data consisting of histological images of mastopathy and mammary cancer tissue.

Pengaruh Debt To Equity Ratio, Net Profit Margin, Current Ratio dan Total Asset Turn Over Terhadap Pertumbuhan Laba: pada Perusahaan Manufaktur Sub Sektor Farmasi yang Terdaftar di Bursa Efek Indonesia Tahun 2014-2023

The purpose of this study is to ascertain the impact of the following factors on earning growth in pharmaceutical manufacturing companies listed on the Indonesia Stock Exchange between 2014 and 2023: debt to equity ratio, net profit margin, current ratio, and total asset turnover. This study employs a quantitative methodology that combines a verification and descriptive strategy. Traditional assumption testing, multiple linear regression testing, Pearson correlation coefficient testing, determination coefficient testing, and hypothesis testing with partial and simultaneous tests (t and f tests) are some of the statistical testing methods used for verification. Sixty financial reports from businesses in the Pharmaceutical Manufacturing Companies provide the sample data. employing nonprobability purposive sampling for sampling. Version 26.0 of SPSS is utilized as a data processing tool. The results of the partial study obtained Debt To Equity Ratio has no effect on Earning growth, Net Profit Margin has an effect on Earning growth, Current Ratio has an effect on Earning growth and total asset turnover has no effect on Earning growth. The results of the simultaneous study showed that Debt to Equity Ratio, Net Profit Margin, Current Ratio and Total Asset Turnover had a significant influence on Earning growth.

Pengaruh Pengetahuan Pajak Dan Modernisasi Administrasi Perpajakan Terhadap Kepatuhan Wajib Pajak Orang Pribadi Dengan Sosialisasi Perpajakan Sebagai Variabel Moderasi (Studi Kasus Wajib Pajak Orang Pribadi Non Karyawan di DKI Jakarta)

This study aims to test and analyze Tax Knowledge and Modernization of Tax Administration on Individual Taxpayers’ Compliance with Tax Socialization as a Moderation Variable with Non-Employee Individual Taxpayers residing in DKI Jakarta as the object of research. The theory used in this study is Theory of Planned Behavior. The sampling method in this study was carried out using the Nonprobability Sampling method using Accidental Sampling. The population in this study is 1.838.096 Non-Employee Individual Taxpayers residing in DKI Jakarta. Meanwhile, the sample used in this study was as many as 100 respondents who were determined using the Slovin formula. Data collection was carried out using a questionnaire. The statistical testing method uses the Smart Partial Least Square (Smart-PLS) application version 4.1.0.3. The results of this study show that Tax Knowledge has an effect on Taxpayer Compliance, Modernization of Tax Administration has an effect on Taxpayer Compliance, Tax Socialization in not able to moderate the influence of Tax Knowledge on Taxpayer Compliance, and Tax Socialization is not able to moderate the influence of Modernization of Tax Administration on Taxpayer Compliance.

Pengaruh Komunikasi dan Lingkungan Kerja Terhadap Kinerja Pegawai pada Dinas Pendidikan

The problem of employee performance at the Malinau Regency Education Office is a decline in performance in the last 3 years, allegedly related to ineffective communication and a work environment that is not fully conducive. This study aims to analyse the effect of communication and environment on employee performance. The theories used in this study, namely Communication variables (VX1), Work Environment Variables (VX2), and Y Performance Variables. This study uses a quantitative approach with a statistical test method using multiple regression hypotheses (T test and F test), with the Independent variables namely VX1 (Communication Effect), VX2 (Work Environment Effect), and Denpenden Variable Y (Performance). The instrument used is a questionnaire. The population totalled 66 employees with saturated sampling technique. The results of descriptive analysis, communication variables (X1) based on the classification are included in the good category (B), but the horizontal communication indicator 64.24 based on the classification is included in the less good category (KB) and is below the percent value of the average score of communication variables. As for the work environment variable (X2), the work environment is included in the less good category (KB). While the performance variable (Y) is included in the less good category (KB). There is a positive influence between communication on employee performance based on the calculation of Thitung 3.892 greater than Ttable 1.669 and the influence of the Work Environment on employee performance obtained Thitung value 6.054 greater than Ttable 1.669. Based on the f test research simultaneously shows the influence of communication and work environment on performance obtained Fcount value 53.524 greater than Ftable 3.145. The coefficient of determination shows that the independent variables (VX1 and VX2) are 61.8% and the remaining 38.2% is explained by other variables not examined in this study.

The Buildings’ Reliability Calculating Method Using a Simple Seismic Impact Model

Non-canonical spectral representation of seismic activity is employed to assess the reliability of nonlinearly modeled buildings. Seismic impact is modeled using a random process, represented by simple functions with random parameters. We consider random processes with correlation functions expressed as a sum of cosine-exponential terms. Reliability, defined as the probability of failure-free operation, is determined using statistical testing methods. The reliability calculation algorithm is implemented in MATLAB. As an illustrative example, we calculate the reliability of a section of a one-story industrial building frame modeled by a nonlinear system. Failure is defined as exceeding experimentally determined permissible displacement limits. Our calculations involve up to 2000 realizations of the random process. We analyze histograms, empirical distribution functions, and reliability values of maximum fragment movements. We find that using 100 realizations of the random process yields satisfactory accuracy in determining reliability. This reliability calculation method is recommended for rapid reliability estimates across various structure types, including those employing seismic isolation systems. We also observe a correlation between displacement magnitudes calculated under accelerograms and a random process represented in a non-canonical form. Thus, we recommend this method for reliability assessments in multi-story buildings. Doi: 10.28991/CEJ-2024-010-08-019 Full Text: PDF

Characteristics of the Basic Socio-Economic Factors of the Development of Small Towns in Russia

Currently, small towns in Russia are going through a difficult period due to changes in the economic situation, demographic trends and financial constraints. These factors have a significant impact on the lives of citizens and require in-depth study and analysis. The purpose of the study is to identify the general features of small forms of settlement as part of the regional socio-economic system for the further development of proposals for the formation of specific measures for the socio-economic development of small towns. The research was conducted on a sample of data from 82 regions of Russia for 2018–2022 according to 11 statistical hypotheses. The work used modern methods of statistical analysis, including testing the normality of distribution (ShapiroWilk statistics), equality of variances (Livigne criterion) and the method of statistical testing of hypotheses based on the Mann-Whitney test. Calculations were performed in the Python software environment using the NumPy and Pandas libraries for data preprocessing and SciPy and XlsxWriter for statistical analysis and export results. The following socio-economic indicators are considered: average per capita cash income of the population, gross regional product and investment in fixed capital per capita, average turnover of organizations and the share of the population with cash income below the subsistence level. It is shown that, within the framework of the existing administrative structure of regional governance, regions with different levels of urbanization have statistically significant differences in these indicators. At the same time, no such differences were found in relation to indicators such as expenditures and revenues of consolidated budgets per person, the total area of residential premises per inhabitant on average, the fertility rate, the population maturity index and the dynamics of population change over 10 years (2012-2022 inclusive).

Exploring emotional experiences and dataset construction in the era of short videos based on physiological signals

ObjectiveThis study aims to uncover the intrinsic links between emotional experiences and physiological responses as users watch short videos on social media platforms, with a particular focus on using changes in physiological signals to identify and understand different emotional states. MethodsTo achieve this objective, a simulated experiment was designed to browse short videos and induce and record physiological signals under seven typical emotional states. The recorded signals included electroencephalogram, galvanic skin response, skin temperature, and heart rate, resulting in the creation of a dataset. Machine learning algorithms were employed to classify emotions and evaluate the dataset’s effectiveness. Statistical testing methods were used to analyze signal feature changes and distributions across different emotional states, exploring trends and their statistical significance. ResultsThe study successfully constructed an emotion-physiological signal dataset. Statistical tests revealed significant changes in physiological signal characteristics across different emotional states, providing extensive data support for understanding how emotions specifically affect physiological responses. ConclusionThe research not only confirmed the practicality of the constructed dataset in emotion recognition tasks but also provided empirical evidence of how emotions influence physiological responses through detailed analysis of physiological signals. SignificanceThe findings of this study hold significant value for emotional science, psychological research, and the entertainment industry. They not only facilitate a deeper exploration of individual emotional physiological mechanisms but also provide a scientific basis for optimizing content recommendation systems, intelligent entertainment technologies, and affective-aware applications.

The Influence of Working Capital and Sales Growth on Net Profit at PT Sido Muncul Tbk (Case Study on the Indonesian Stock Exchange for the 2018-2023 Period)

Working capital is a tool to measure a company's ability to create value for investors through the profits it produces. Net profit is a ratio to determine the amount of net profit that has been obtained by the company. Based on this, the author gave the title of this research, namely: "The influence of capital work and sales growth towards net profit for the 2018-2023 period". This research aims to: (1) how working capital, sales growth and net profit at PT Sido TBK appear for the 2018-2023 period. (2) what is the effect of working capital on net profit at PT Sido appears TBK for the 2018-2023 period. (3) how does sales growth affect net profit at PT Sido appears TBK for the 2018-2023 period. (4) what is the influence of working capital and sales growth on net profit at PT Sido appears TBK 2018-2023. The method used in this research is descriptive - associative with an ex-fost facto approach. The data collection method used is documentation. and sampling using non-probability sampling. The data source used is secondary data with the type of data used is quantitative data. The statistical data testing method uses the t statistical test with a significance level of 0.05 and the f test with a significance of 0.010 using the SPSS 26 for Windows program. The research results show that working capital has a negative effect on net profit with a calculated t value of 0.550 and a significance value of 0.05 and a correlation value of 0.079, which means the level of relationship has a negative effect

Impact of Learning Motivation and Physical Condition on Physical Education Learning Outcomes in Junior High School Students Aged 13-15 Years: A Analysis studies

The purpose of the study. to determine and evaluate the relative contributions of physical fitness and learning motivation to the learning outcomes of students in the 7–9 age range. Materials and methods. The purpose of correlation approaches is to quantitatively ascertain if and to what extent two or more variables are correlated. Data is analyzed using the statistical techniques of correlation and multiple regression utilizing the SPSS (Statistical Product and Service Solution Version 25) software. Before the advent of statistical methods for hypothesis testing. Use the Kolmogorov-Smirnov test (uji K-S) to check for data normality. Results. The coefficient of correlation for rx12y = 0.49 is indicated. Koefisisen korelasi signifikansi diperoleh F hitung 4,37>t tabel 3,35, dengan distribusi t dengan α = 0,05 dan k = 2 sebagai pembilang dan (n–k–1) sebagai dk penyebut. In other words, there is a positive correlation between the Jasmani Motivasi Belajar and the Penjasorkes Learning Outcomes, which are 0,492 x 100%, or 0,24 x 100%, or 24%. Conclusions. Students' willingness and excitement to participate in physical education learning activities will be influenced by their motivation for learning. A learner who understands effective learning motivation would attempt to inspire themself to learn well. Since achieving satisfactory learning outcomes is one of the goals of the processes and activities that take place in the classroom, teachers are crucial in helping students develop motivation for learning and improving their physical fitness.

Automatic Diagnosis of Lung Diseases (Pneumonia, Cancer) with given Reliabilities on the Basis of an Irradiation Images of Patients

The article proposes algorithms for the automatic diagnosis of human lung diseases pneumonia and cancer, based on images obtained by radiation irradiation, which allow us to make decisions with the necessary reliability, that is, to restrict the probabilities of making possible errors to a pre-planned level. Since the information obtained from the observation is random, Wald’s sequential analysis method and Constrained Bayesian Method (CBM) of statistical hypothesis testing are used for making a decision, which allow us to restrict both types of possible errors. Both methods have been investigated using statistical simulation and real data, which fully confirmed the correctness of theoretical reasoning and the ability to make decisions with the required reliability using artificial intelligence. The advantage of CBM compared to Wald’s method is shown, which is expressed in the relative scarcity of observation results needed to make a decision with the same reliability. The possibility of implementing the proposed method in modern computerized X-ray equipment due to its simplicity and promptness of decision-making is also shown.

Research on Momentum Analysis of Tennis Match Based on Machine Learning

This study aims to quantify momentum changes in tennis matches by building a machine learning model and investigate their impact on match outcomes. Firstly, a momentum model is established to quantify the momentum value of players by defining basic points, break points, guaranteed points and continuous points. Then, the momentum curve of players is calculated using real match data, and its high match with the match trend is verified. Then, the correlation between momentum value and match result is tested by statistical test method. The results show that there is a significant correlation between momentum value and match victory. Further, the displacement test verifies that the change of momentum value is not random, but reflects the actual trend of the game. This study proves that momentum plays an important role in tennis matches, and its change has a significant impact on match results, which provides an important reference for further research and application of momentum.

Testing the Significance of Regression Coefficients in Liu Type Estimators

In the linear regression model, the multicollinearity problem arises when there is a linear relationship between independent variables. This situation causes the variance of the estimations of the model parameters obtained by the Least Squares Estimator method to increase and move away from the true value, resulting in unstable and incorrect results. Biased Estimator methods are developed to eliminate the adverse effects caused by multicollinearity. In this study, a test statistic is obtained to test the significance of the model coefficients for the Liu-Type Estimator using the test statistic method suggested in the study of Halawa and El-Bassiouni (2000). With a simulation study, the significance of the model coefficients of the Ridge, Liu and Liu type biased estimators in different situations is tested, the type I errors and power values of the estimators calculated and the results compared. In addition, a real data application performed to better understand the test procedure.

PENGARUH BELANJA MODAL DAN FISCAL STRESS TERHADAP KINERJA KEUANGAN PEMERINTAH DAERAH

The aim of this research is to determine and analyze the influence of capital expenditure and fiscal pressure on the financial performance of local governments in districts/cities of North Sumatra Province. The population of this research is districts/cities in North Sumatra Province which consists of 33 districts/cities with observation years from 2014 to 2021. The sample type uses a saturated sample where the population is a sample. The data was processed using the panel data regression statistical test method. The results of this research prove that fiscal pressure has a positive and significant effect on the financial performance of local governments in districts/cities of North Sumatra Province. Meanwhile, capital expenditure has no effect on the financial performance of district/city governments in North Sumatra Province.

ВИКОРИСТАННЯ ЗАСОБІВ ШТУЧНОГО ІНТЕЛЕКТУ ДЛЯ ПІДТРИМКИ ГРУПОВОЇ ДИНАМІКИ ПРОЄКТНИХ КОМАНД: ВПЛИВ НА САМОЕФЕКТИВНІСТЬ ТА ЗАЛУЧЕНІСТЬ СТУДЕНТІВ

The issue of maintaining group dynamics in flexible remote teams is currently in the focal point of attention not only of commercial companies and government agencies as a response to the changing conditions of work organisation, including those associated with the effects of the COVID-19 pandemic, but also of higher education institutions that train future professionals. Since intragroup dynamics is a key factor affecting team performance, this paper is the result of the study conducted as part of the Group Dynamics and Communications course at National University of Life and Environmental Sciences of Ukraine (NULES) to determine the potential of using AI tools as tools to support the group dynamics of self-organised teams during the implementation of educational projects. Based on the results of the free choice of project and team management tools, we categorised three groups among the 56 participants of the experiment according to the use or non-use of AI tools to enhance group dynamics, followed by self-assessment of the effectiveness of the selected tools in increasing their levels of self-efficacy and social engagement. The survey was based on a Likert scale measurement, and non-parametric methods of analysis and statistical hypothesis testing were used to process the results. The self-assessment confirmed that the incorporation of AI tools in the implementation of a team-based learning project did not influence the improvement of students' social engagement and the dynamics of self-efficacy for both effective and ineffective students. The authors identified the positive impact of using AI tools on its development for students with the average level of self-efficacy. We also presented an example of the use of Notion AI for enhancing group dynamics at each stage of team development according to B. Tuckman. The authors suggest that the impact of using AI tools in the process of implementing a group learning project depends more on personal characteristics of team members than on the choice of a specific AI model. Determination of application specifics of individual AI tools in the process of training future specialists in the implementation of various types of educational activities, particularly ethical considerations is recognised as an area for further research.

Effect of various surface coating methods on surface roughness, micromorphological analysis and fluoride release from contemporary glass ionomer restorations.

To evaluate the effect of various surface coating methods on surface roughness, micromorphological analysis and fluoride release from contemporary resin-modified and conventional glass ionomer restorations. A total of 72 permanent human molars were used in this study. The teeth were randomly assigned into 2 groups according to type of restorative materials used; resin modified glass ionomer cement and conventional glass ionomer (SDI Limited. Bayswater Victoria, Australia). Each group was subdivided into 3 subgroups according to the application of coat material; Sub-group1: without application of coat; Sub-group2: manufacturer recommended coat was applied and sub-group3: customized (vaseline) coat was applied. Each group was then subdivided into two divisions according to the time of testing; immediate (after 24h) and delayed (after 6 months of storage). Three specimens from each sub-group were selected for surface roughness test (AFM) and another 3 specimens for the micromorphological analysis using scanning electron microscope (SEM). For the fluoride release test, a total of 60 cylindrical discs were used (n = 60). The discs were randomly split into 2 groups according to type of restorative materials used (n = 30); resin modified glass ionomer cement and conventional glass ionomer. Each group was subdivided into 3 subgroups (n = 10) according to the application of the coat material; Sub-group1: without application of coat; Sub-group2: with the manufacturer recommended coat and sub-group3: with application of customized (vaseline) coat. Data for each test was then collected, tabulated, were collected, tabulated, and tested for the normality with Shapiro-Wilk test. Based on the outcome of normality test, the significant effects of variables were assessed using appropriate statistical analysis testing methods. Regarding the data obtained from surface roughness test, Shapiro-Wilk test showed normal distribution pattern of all values (p > 0.05). Accordingly, Two-way ANOVA outcome showed that the 'type of restoration' or 'test time' had statistically significant effect on the AFM test (p < 0.05). Regarding Fluoride specific ion electrode test 2-way ANOVA followed by Least Significant Difference (LSD) Post-hoc test revealed significant difference among the groups (p < 0.05). It showed that SDI GIC group after 14 days of measurement had the highest mean of fluoride release (36.38 ± 3.16 PPM) and SDI RMGIC after 30 days of measurement had the second highest mean of fluoride release (43.28 ± 1.89 PPM). Finally, regarding the micromorphological analysis using SEM, a slight difference was observed between the studied groups. Based on the results of this study, various coatings enhance surface roughness in the initial 24h of restoration insertion. Different coat types seems that have no influence on fluoride release and the micromorphological features of the restoration/dentin interface.

Mobile network traffic analysis based on probability-informed machine learning approach

The paper proposes an approach to the joint use of statistical and machine learning (ML) models to solve the problems of the precise reconstruction of historical events, real-time detection of ongoing incidents, and the prediction of future quality of service-related occurrences for prospective development of the modern networks. For forecasting, a regression version of the deep Gaussian mixture model (DGMM) is introduced. First, the preliminary clustering based on the finite normal mixtures is performed. This information is then used as an input for some supervised ML algorithm. It is the basic concept of the probability-informed ML approach in the field of telecommunications networks. Using the real-world datasets from a Portuguese mobile operator as well as public cellular traffic data, the article compares this approach with methods such as random forests, support vector machine regression, gradient boosting and LSTM. Vector autoregression, informed by the parameters of the generalized gamma (GG) distribution, which has also been successfully used to reconstruct past traffic patterns, is also used as a benchmark. We demonstrate that DGMM-based regression is 6.82–22.8 times faster than LSTM for the dataset. Moreover, DGMM-based regression can achieve better results for the most important traffic characteristics (average and total traffic, the number of users). For metrics MAPE and RMSE, it surpasses the results of statistical methods up to 46.7% (RMSE) and 91.5% (MAPE) (median increases are 28.0% and 80.1%, respectively), as well as for ML methods up to 13.0% (RMSE) and 35.7% (MAPE) (median increases are 0.39% and 2.5%, respectively). Thus, the use of a probability-informed approach for telecommunication data seems optimal for the computational speed and accuracy trade-off. Also, we introduce a novel statistical hypothesis testing method based on GG distribution for detecting suspected anomalies in traffic.