Coefficient Of Skewness Research Articles

Corporate Voluntary Codes of Conduct for Preventing Human Trafficking through Corporate Social Responsibility Approach : An Empirical Study

This study explores the effectiveness of Corporate Voluntary Codes of Conduct(CVCC) for preventing human trafficking through a Corporate Social Responsibility (CSR) approach. The research involved a diverse sample size of 543 participants, including corporate governance professionals, international justice authorities, human trafficking combating establishments, and non- governmental organizations (NGOs). Both descriptive and inferential statistics were employed to test hypotheses, utilizing a range of statistical methods such as, Mean, Standard Deviation, Coefficient of Variation, Skewness and Kutosis and t-test, ANOVA, Correlation, Multiple Regression, and Confirmatory Factor Analysis (CFA ) respectively. The study utilized a 5-point Likert scale to assess 125 factors reduced to 15 combined factors out 77 factors, and 48 factors become redundant by administering data reduction technique related to preventing human trafficking, encompassing Corporate Voluntary Codes of Conduct and CSR. The findings were employed to develop a comprehensive frameworks to hold corporations accountable for human rights violations. Human Trafficking Controlling and Prevention Model. The results highlight the significance of CVCC inconjuction with CSR initiatives in addressing human trafficking issues beyond the confines of corporate codes. This research contributes to the evolving discourse on corporate responsibility and its potential impact on preventing human trafficking, emphasizing the need for a holistic approach that extends beyond regulatory frameworks

Poisson XRani Distribution: An Alternative Discrete Distribution for Overdispersed Count Data

In this article, a one‐parameter probability mass function called Poisson XRani is proposed by combining the Poisson and the XRani distributions. A wide range of distributional properties such as the shape of the probability mass function, probability generating function, factorial moments, moment generating function, characteristic function, raw moments, dispersion index, mean, variance, coefficient of skewness, and coefficient of kurtosis are investigated. It is proven that the proposed probability mass function can easily handle overdispersed and right‐skewed count observations with heavy tails. Both maximum likelihood and Bayesian estimation techniques are used to estimate the unknown parameter of the proposed distribution and a simulation study was conducted to examine and analyze the performance of the maximum likelihood estimation procedure based on sample size, absolute bias, relative absolute bias, and root mean square error. The usefulness of the proposed distribution is assessed using two distinctive real datasets. These applications reveal that the new distribution provides an adequate fit compared to the other eight discrete distributions.

STREAMFLOW GENERATION OF RIVER NIGER BASIN KOGI USING MODIFIED THOMAS FIERRING MODEL

There is a concerted effort towards protection of environment as a result of incessant change of climate and its resultant effects on human live and property. Studies have shown that flooding is ranked the most devastating natural disaster in the world; and it is a common natural environmental hazard in various parts of Nigeria. In recent times, flooding has been reported to constitute a major challenge to about 1.5 billion people or 19% of the world population; whereas 70 million people globally exposed to flooding every year while over 800 million people are living in flood-prone areas. The 2022 destructive flooding in River Niger Basin Lokoja Nigeria has attracted attention of experts with a view to curtailing the future reoccurrence. Based on this rationale, this study aimed to determine the trend in the hydrometric dataset and to provide extensive time series analysis of the streamflow in the study area using Modified Thomas Fierring Model (MTFM). In order to achieve the objectives of the study, the dataset for 20 years was sourced from the NIWA (National Inland Waterway Authority) whereby the daily and monthly data was extensively analyzed and the (Thomas Fierring) T-F model the data for the “n” years was divided into 12 sections while the data of each month was re-aggressed to the previous month so that there were 12 linear regressions. Also, MTFM was used to predict the monthly flows from River Niger from 2000 to 2019 so as to compute 36 parameters (12) means (), standard deviations (), and Lag-1 autocorrelation coefficients (r1)). After the estimation of the parameters of the MTFM, the series of historical monthly flows for 20 years were generated. These series were compared with the recorded monthly flows of River Niger for the hydrological period of 20 years (2000-2019), with the use of basic statistical characteristics like mean, standard deviation, maximum, minimum, coefficient of variation, skewness and Kurtosis as shown in Table 1 and Figure 2. The findings revealed that monthly discharge time series of River Niger Basin from 2000 to 2019 are consistent with slightly variation in the trends after modified modified Thomas Fierring Model application; and the study further underscored the relevance of MTFM for analyzing streamflow of River Niger Basin Lokoja. Among others, the study recommended up-to-date hydrometric data of River Niger Basin for optimum control and management of streamflow in the study area; and concluded that up-to-date information in this direction will help future researcher to consolidate on the existing body of knowledge in the field.

Analysis of Teaching Capacity Building of College Teachers’ Innovation and Entrepreneurship Education under Comprehensive Multiple Fuzzy Evaluation System

Abstract How can we effectively promote and improve the quality of innovation and entrepreneurship education in colleges and universities? The key lies in enhancing the capacity of teachers to impart innovative entrepreneurial education. The article takes the requirements of innovation and entrepreneurship education and the connotation of teaching ability as the orientation and establishes the evaluation index system of dual innovation education teaching ability of college teachers by combining the CIPP model. The IAHP method and the improved CRITIC method, respectively, calculate the subjective and objective weights of each index in the index system, and then they introduce the game theory idea to amalgamate and assign weights to the index system Based on the gray fuzzy mathematical theory, a fuzzy comprehensive evaluation model of the teaching ability of innovation and entrepreneurship education among college teachers was established. Using University Y as the research object, data analysis was carried out to determine the differences between teachers’ dual entrepreneurship teaching abilities, comprehensive evaluation, and the application effectiveness of the evaluation system. The maximum mean value for teachers’ innovative entrepreneurship education teaching ability is 4,085, and the minimum skewness coefficient is only −0.475, suggesting a trend towards correct skewness. Teachers’ mastery of dual-creation theory is proportionately weighted at 25.52%, and the fuzzy comprehensive evaluation score is 81.97, which is a good grade. The evaluation index system, combined with the fuzzy comprehensive evaluation model, can help college teachers clarify the problems of dual-creation education, which is conducive to improving their dual-creation teaching ability.

Machine Recognition of DDoS Attacks Using Statistical Parameters

As part of the research in the recently ended project SANET II, we were trying to create a new machine-learning system without a teacher. This system was designed to recognize DDoS attacks in real time, based on adaptation to real-time arbitrary traffic and with the ability to be embedded into the hardware implementation of network probes. The reason for considering this goal was our hands-on experience with the high-speed SANET network, which interconnects Slovak universities and high schools and also provides a connection to the Internet. Similar to any other public-facing infrastructure, it is often the target of DDoS attacks. In this article, we are extending our previous research, mainly by dealing with the use of various statistical parameters for DDoS attack detection. We tested the coefficients of Variation, Kurtosis, Skewness, Autoregression, Correlation, Hurst exponent, and Kullback–Leibler Divergence estimates on traffic captures of different types of DDoS attacks. For early machine recognition of the attack, we have proposed several detection functions that use the response of the investigated statistical parameters to the start of a DDoS attack. The proposed detection methods are easily implementable for monitoring actual IP traffic.

Interpretable Single-dimension Outlier Detection (ISOD): An Unsupervised Outlier Detection Method Based on Quantiles and Skewness Coefficients

A crucial area of study in data mining is outlier detection, particularly in the areas of network security, credit card fraud detection, industrial flaw detection, etc. Existing outlier detection algorithms, which can be divided into supervised methods, semi-supervised methods, and unsupervised methods, suffer from missing labeled data, the curse of dimensionality, low interpretability, etc. To address these issues, in this paper, we present an unsupervised outlier detection method based on quantiles and skewness coefficients called ISOD (Interpretable Single dimension Outlier Detection). ISOD first fulfils the empirical cumulative distribution function before computing the quantile and skewness coefficients of each dimension. Finally, it outputs the outlier score. This paper’s contributions are as follows: (1) we propose an unsupervised outlier detection algorithm called ISOD, which has high interpretability and scalability; (2) massive experiments on benchmark datasets demonstrated the superior performance of the ISOD algorithm compared with state-of-the-art baselines in terms of ROC and AP.

Analyzing the effects of suction and injection Reynolds number on the transport process in a hydromagnetic flow through a channel of reactive porous walls

The present paper analyzes the effects of suction and injection Reynolds number on the dispersion process of solute in an incompressible magneto-hydrodynamics flow through a channel. The walls of the channel are permeable and a first order heterogeneous reaction is applied on it. Also, an inclined magnetic field is employed at both walls of the channel. The unsteady convection-diffusion equation is solved semi-analytically to find out the coefficient of dispersion, skewness and kurtosis of the solute distribution for all time period. Using Hermite polynomial representation, the distribution of mean concentration is obtained. The effects of various parameters such as suction Reynolds number (R), injection Reynolds number (R′), boundary absorption parameter (β), inclination angle of the magnetic field (α), Hartmann number (M) and dispersion time (t) on dispersion process of the solute are examined. It is shown that with the increment of suction Reynolds number, the peak of the mean concentration of solute enhances prominently. But, when injection Reynolds number increases, the amplitude of the mean concentration decreases. The current research gives an insight to understand the complex behavior of transport process of the solute in a hydro-magnetic channel flow with reactive porous walls.

An Extension of the Akash Distribution: Properties, Inference and Application

In this article we introduce an extension of the Akash distribution. We use the slash methodology to make the kurtosis of the Akash distribution more flexible. We study the general probability density function of this new model, some properties, moments, skewness and kurtosis coefficients. Statistical inference is performed using the methods of moments and maximum likelihood via the EM algorithm. A simulation study is carried out to observe the behavior of the maximum likelihood estimator. An application to a real data set with high kurtosis is considered, where it is shown that the new distribution fits better than other extensions of the Akash distribution.

توزيع رايلي باريتو , الخصائص والتقدير

Abstract In this paper , the Rayleigh-Pareto distribution was presented, which is an expansion of the Rayleigh distribution. Many statistical properties were studied, such as moments, mean, variance, median, skewness coefficient, kurtosis coefficient. In addition, the parameters of the new distribution were estimated using maximum likelihood method . Simulation experiment showed the efficiency of maximum likelihood method . Among the most important conclusions was the efficiency of the maximum likelihood method in estimating the parameters of the new distribution .

Board Diversity and Efficiency of Universities Registered in Kenya: The Role of Funding Sources

The board diversity of institutions plays an integral role in minimizing uncertainty, augmenting knowledge sharing, improving resource utilization, and crafting overall institutional strategy to enhance optimal efficiency. Institutions with more heterogeneous boards are characterized by their ability to attract finances from multiple sources; hence, they are better positioned to be more efficient in their operations. The main aim of this study was to assess the role of funding sources in the association between board diversity and the efficiency of universities registered in Kenya. The study was supported by the agency theory, the human capital theory, the stewardship theory, and the theory of pecking order. The positivist research paradigm anchored the study. A census study of 75 public and private universities in Kenya was conducted using a descriptive longitudinal research approach. The descriptive statistics included calculating the counts, standard deviation, mean, minimum and maximum values, coefficient of variation, kurtosis, and skewness. The fixed effect model was used as the primary estimation technique in inferential statistics. The results established that funding sources partially mediate the association between board diversity and efficiency. The study recommends that for universities to increase efficiency, the boards must make a greater effort to support board diversity, establish the dimensions within the board diversity relevant to efficiency enhancement, and establish multiple funding sources to remain afloat in their operations.

On Power Chris-Jerry Distribution: Properties and Parameter Estimation Methods

In this study, we introduce the "Power Chris-Jerry" distribution, conducting a comprehensive analysis of its fundamental mathematical characteristics and an extensive exploration of various crucial aspects. These encompass investigations into its mode, quantile function, moments, coefficient of skewness, kurtosis, moment generating function, stochastic ordering, distribution of order statistics, reliability analysis, and mean past lifetime. Furthermore, we provide an in-depth assessment of four distinct parameter estimation methodologies: maximum likelihood estimation (MLE), Least Squares (LS), maximum product spacing method (MPS), and the Method of Cram`er-von-Mises (CVM). Our investigation uncovers a consistent pattern wherein the MLE, LS, and CVM approaches consistently yield underestimated parameter values. Intriguingly, we observe a consistent trend of decreasing Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and BIAS across all estimation techniques as sample sizes increase. Remarkably, our simulation results consistently favor the Maximum Product Spacing (MPS) method, highlighting its superiority in generating estimates with smaller MSE values across a broad spectrum of parameter values and sample sizes. These findings emphasize the robustness and dependability of the MPS estimator, offering valuable insights and practical guidance for both practitioners and researchers engaged in probability distribution modeling.

On approximation of linear regression disturbance distribution

Inference on the parameters of a linear regression usually requires the assumption that the disturbances are normally distributed. But sometimes this assumption does not correspond to the residual distribution. Thus, there is a need to infer the disturbances of the regression model from its residuals. In this article, the analysis is mainly focused on estimation of moments of the disturbances based on residuals. The main goal of this article is derivation of the unbiased estimators of the third and forth central moments of the linear regression disturbances. They are applied to estimation parameters of the random component distribution based on empirical data. In addition to the normal distribution of the random component, the gamma distribution and the class of Pearson curves are considered. An example of skewness and kurtosis coefficient estimation is shown. An example of estimating the dominant of the gamma distribution is shown. Finally, different types of point and interval forecasts of the examined variable are presented.

A Heavy-Tailed Distribution Based on the Lomax–Rayleigh Distribution with Applications to Medical Data

In this paper, we extend the Lomax–Rayleigh distribution to increase its kurtosis. The construction of this distribution is based on the idea of the Slash distribution, that is, its representation is based on the quotient of two independent random variables, one being a random variable with a Lomax–Rayleigh distribution and the other a beta(q,1). Based on the representation of this family, we study its basic properties, such as moments, coefficients of skewness, and kurtosis. We perform statistical inference using the methods of moments and maximum likelihood. To illustrate this methodology, we apply it to two real data sets.

Use of Statistical Analysis Tools for ECG Processing

The purpose of the article is to study the algorithms of statistical and intellectual data analysis and their use for processing and analysing electrocardiograms (ECG). The methods and algorithms that form the basis of statistical data processing and analysis are considered. The research methods are based on the application of statistical methods and algorithms for the analysis and pre-processing of medical data. Pre-processing is a necessary step in data processing, which makes it possible to analyze more efficiently, build more accurate models and reduce their dimensionality. Scientific novelty. The article analyses classical statistical methods used at the first stage of data processing. Their effectiveness and necessity in medical data analysis are proved. The results of the work are demonstrated on real data processing, namely, on electrocardiogram processing. Conclusions. The application of statistical analysis methods for pre-processing medical data is considered on the example of ECG processing and analysis. The main statistical characteristics were calculated: mean, variance, standard deviation, mode, median, skewness coefficient, kurtosis coefficient, and coefficient of variation. The obtained data is used to estimate the law of data distribution, test hypotheses about the laws of distribution, and normalize data. A correlation matrix is constructed for the ECG observation matrix, eigenvalues and eigenvectors are calculated, and principal components are determined on their basis. The use of principal components makes it possible to reduce the dimensionality of the data for a deeper analysis. In this study, the amount of data was reduced by a factor of four. A discrete Fourier transform was performed. The analysis of the Fourier’s transform results made it possible to isolate high-frequency electromagnetic interference transmitted through the cable from the power supply network to the device, and the frequency of the interference was determined. Motion artefacts associated with the patient’s breathing were detected. The frequency of such interference is in the range from 1 to 4 Hz. The ECG points were classified and an ECG module was built for further analysis. The classification results made it possible to identify a set of points in the vicinity of R-peaks. This makes it possible to localize QRS complexes without using a complex mathematical apparatus. The results of medical data pre-processing allow to reduce the data dimensionality, identify the presence or absence of linear relationships, and evaluate the frequency characteristics of the data. Based on the pre-processing, it is possible to plan further research and build better models for data mining.

What should be the measure of conformity to normal distribution (normality) test in Likert type digital and face-to-face survey data?

In statistics, it is assumed that the data suitable for parametric tests exhibit a normal distribution. In other words, before running parametric tests, it is essential to check whether the data set follows a normal distribution. Parametric tests can only be applied to data that adhere to a normal distribution. The Shapiro-Wilk and Kolmogorov-Smirnov (Lilliefors modification) tests are among the most commonly utilised methods for testing normality. However, these tests were originally developed for rational numbers. The use of these tests for Likert-type digital or face-to-face survey data has always been a topic of discussion. Even if the measurement tools consist of at least three items, the data sets are dominated by repeated values. Again, there are different opinions on the acceptable range for kurtosis and skewness values. With this study, it was determined that the Kolmogorov-Smirnov (Lilliefors modification) test is not suitable for testing the normality of Likert-type digital or face-to-face survey data. In addition, the coefficients of kurtosis and skewness within the range of (-1, +1) range are acceptable for normality. For this reason, it is recommended not to use goodness-of-fit tests such as Shapiro-Wilk or Kolmogorov-Smirnov for testing the normality of interval scale data collected through questionnaires.

Application of descriptive statistics methods to Kazakhstan stock market

The article discusses the methods of descriptive statistics and its application for the most liquid shares of the Kazakhstan stock market KASE (trading codes: HSBK, KZTK, CCBN, KZTO, KCEL, KEGC, KZKAK) from 2007 to 2022. Descriptive statistics are calculated for each stock, such as average return, variance, sample range, standard deviation, coefficient of variation, kurtosis, and skewness. The coefficients of the regression equation and the coefficient of determination R2 are estimated, which show the relationship between the market return and the return on an individual stock. The comparative analysis of index growth of stocks was carried out from 2013 to 2022. The histogram of the frequency distribution of KASE market returns based on annual and monthly periods and its empirical distribution function have been constructed. The hypothesis about the normal distribution of returns according to the Pearson criterion was tested. The distributions of returns for many stocks do not fully correspond to the normal distribution and have a positive skewness, i.e. have a long tail on the right side. The market curve has been constructed that characterizes the dependence of stock returns on the level of risk. According to this curve, it is established that HSBK stocks are the most risky of all the considered stocks, undervalued and attractive investments for investors using an aggressive investment strategy. KEGC stocks are less risky and very attractive investments for investors who use a more conservative investment strategy in the Kazakhstani stock market.

Improving the Resolution of Ghost Imaging via the Coefficient of Skewness

Improving the Resolution of Ghost Imaging via the Coefficient of Skewness

Estimation of population variance using the actual ranks and values: Properties and application

Background: Of all measures of variability, the variance is by far the most popular. This paper argues an alternative index of variability, shares many properties with the variance, but can be more informative about the properties of distributions that depart from normality. Procedures: An estimator for the population variance is proposed. The proposed estimator is based, in its calculations, on the actual variable values and their ranks. A simulation study is employed to compare the performance of the proposed estimator with that of eight other estimators appearing in the literature using the mean square error criterion. The influence of sample size, normality, and non-normality is investigated. We suggest a new measure of skewness based on the new proposed estimator for the population variance. The mean value, median, and the new estimator for the population variance are the only three summary statistics that may be effectively used to derive the suggested coefficient of skewness. A limited Monte Carlo simulation is used to examine the potential of the suggested statistic to identify skewness. The covariance between a random variable and its rankings is how the new estimator is stated. This format is used to derive new correlation coefficients. Results and conclusion: The simulation study shows that the proposed estimator, under conditions of normality, performs better than most estimators considered, whereas it fails to give better results than some others. In cases of non-normality considered, the proposed estimator was superior to eight competitors. According to preliminary results, the new coefficient of skewness performs well overall in a limited simulation. The new correlation coefficient outperforms Pearson’s correlation for the non-normal model, as demonstrated by simulation analysis. Furthermore, it has been demonstrated that the new estimator is a useful tool for extracting a straightforward method to obtain novel skewness and correlation coefficients.

Different responses of event-based flood to typhoon and non-typhoon rainstorms under land use change in Xixi Basin of southeastern China

There are two major types of rainstorms in the Asia-Pacific region –typhoon induced rainstorm and non-typhoon rainstorm. This study investigated and separated the different flood responses to these two types of rainstorms, based on a scheme that combined the classification of typhoon and non-typhoon rainstorm floods, the simulation from a distributed hydrological model, and a method to forward restore the impact of land use change on flood frequency analysis. For flood events in a typical basin on the southeast coast of China, the flood characteristics in response to typhoon and non-typhoon rainstorms were significantly different in three aspects: (1) The key parameters in the HEC-HMS model were systematically different. The initial loss and wave velocity of the model were apparently larger for the typhoon rainstorm flood than for the non-typhoon rainstorm flood. (2) The impacts of land use change were different, with smaller effect on typhoon induced floods than on non-typhoon floods under the same magnitude of flood peak flow. (3) Classification of two types of floods resulted in more reasonable frequency analysis affected with the land use change because the annual maximum flood series is the mixture of typhoon and non-typhoon rainstorms. Frequency analysis for the forward-restored mixture flood series showed that the land use change with the decrease in the area of forest land and the increase of garden and construction land resulted in the increase of the mean maximum flow from 2266.6 to 2510.19 m3/s, but the values of coefficient of variation were slightly altered, the ratio of skewness coefficient to variation coefficient were unchanged. The results provide new ideas and methods for a deeper understanding of the impact of land use change on flooding in typhoon and non-typhoon co-action regions.

The Effect of Self-Leadership on Self-Efficacy and University Commitment: A Study on Preservice Music Teachers

The aim of this study was to find out the relationship among variables self-leadership, self-efficacy and university commitment and their effects on each other. Accordingly, correlational survey model was used as the relationships among variable. It examines conceptual models created by researchers. The population included 1390 preservice music teachers. There were 349 preservice music teacher in the sample of the research. The research’s sample consisted of 10 universities. A simple random sample was chosen for the participation of the students. Structural equation model was created with the hypotheses. Confirmatory factor analysis was used to find out the factor and item numbers of the them. The data were evaluated in terms of extreme values and 16 data were excluded according to mahalonobis values. The skewness and kurtosis coefficients of the sub-dimensions were examined to determine whether the univariate normality assumption of the data was met. According to the first result, music teacher candidates' self-leadership behaviors positively and significantly affected their self-efficacy. According to second result, preservice music teachers’ self-leadership behaviors positively and significantly affected their university engagement. According to the third result, it was concluded that the self-efficacy of the preservice music teacher affected their university commitment positively and significantly. Self-leadership was an important predictor of self-efficacy and university commitment, and self-efficacy was an important predictor of university commitment.