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One of the conditions for the efficient operation of business is the use in economic activities of various social media services - both in terms of the economic activities types and in the nomenclature of services. To form information resources in the management system, economic entities use different types of social media, such as social networks, multimedia websites, blogs or microblogs, and other means to exchange knowledge. The purpose of using social media is to present the enterprises’ businesses in the market, receive feedback from customers, attract customers, collaborate with partners, hire employees and exchange views on important business issues. The classification features of social media as a type of mass communication are defined in the theoretical studies of many researchers and in the applied statistics on the respective classifiers. Applied statistics uses four classifiers to characterize the sphere of social media. Analysis of the content of the classifiers shows that they are not unified both in terms of the nomenclature of social media services and in possible economic activities in this area. Further development of social media services in Ukraine in the context of the growth of their role in business is impossible without the formation of reliable information about the results of economic management in this sphere in the accountance system, especially with regard to the activity processes as to the Account 23 «Producing». The main instrument for overcoming these problems should be the unification of the content of statistical classifiers on the range of services and the types of social media, as well as the construction on this basis of the information system in accountance.

In some settings, it may be difficult to differentiate between a confounder and a mediator. For instance, the observed association of self-reported childhood psychological abuse (CPA) with onset of chronic obstructive pulmonary disease (COPD) and migraine may be confounded by current mood/psychological state (e.g., the subjective evaluation of one's own affective state), as well as mediated by an individual's psychopathological symptoms. In this study, we propose the "independence hypothesis," which could prove meaningful to explore in data that lack prospective or objective indices of CPA. We used cross-sectional data from wave VI (2007-2008) of the Tromsø Study, Norway (N = 12,981). The associations between CPA and COPD and migraine were assessed with Poisson regression models. CPA was associated with a 46% increased risk of COPD (relative risk [RR] = 1.46, 95% confidence interval [CI]: [1.02, 1.90]) and a 28% increased risk of migraine in adulthood (RR = 1.28, 95% CI: [1.04, 1.53]), independent of age, sex, parental history of psychiatric problems/asthma/dementia, smoking, respondent's mood/psychological state, and mental health. These findings suggest that the association between retrospectively reported CPA and COPD and migraine is not driven entirely by respondent's mood/psychological state and mental health. Assessing the independent effect of self-reported CPA on COPD and migraine in retrospective studies may prove more meaningful than exploring the mediating role of mental health. Here, we provide the analytical rationale for assessing the independent effect in settings where it is difficult to differentiate between a confounder and a mediator. Moreover, we provide a theoretical rationale for assessing the independent effect of retrospectively reported childhood adversity on health and well-being. [ABSTRACT FROM AUTHOR]

Abstract—Topical aspects of modern computer science in their relation to the issues of analysis of technical and economic information and econometric research have been investigated. The feasibility of abandoning the methods of classical statistics and applying robust and nonparametric data processing procedures in real conditions of the heterogeneity of observations and significant deviations of empirical distributions from the normal law is justified. The possibilities and areas of application of econometric models, methods of nonparametric statistics, science metrics, and multidimensional data analysis, for production of information-analytical products and services are shown. The prospects of analytical postprocessing of techno-economic information in the structure of big data technologies are considered. The purpose of this article was to show the potential and systemic role of analytical post-processing of technical and economic information in the formation of a new digital information environment. [ABSTRACT FROM AUTHOR]

We propose a new multiple dependent state sampling plan which comprises the features of existing multiple dependent state sampling plan and repetitive group sampling plan. We develop this new sampling plan for the inspection of measurable quality characteristics. The proposed plan is developed based on the process capability index Cpk, where the quality characteristic under investigation follows the normal distribution with unknown mean and unknown variance. For the practical application of the proposed new sampling plan industries by, tables are constructed to determine the optimal parameters through a non-linear optimization problem. The advantages of this proposed sampling plan are also discussed. [ABSTRACT FROM AUTHOR]

Summary: In any other circumstance, it might make sense to define the extent of the terrain (Data Science) first, and then locate and describe the landmarks (Principles). But this data revolution we are experiencing defies a cadastral survey. Areas are continually being annexed into Data Science. For example, biometrics was traditionally statistics for agriculture in all its forms but now, in Data Science, it means the study of characteristics that can be used to identify an individual. Examples of non‐intrusive measurements include height, weight, fingerprints, retina scan, voice, photograph/video (facial landmarks and facial expressions) and gait. A multivariate analysis of such data would be a complex project for a statistician, but a software engineer might appear to have no trouble with it at all. In any applied‐statistics project, the statistician worries about uncertainty and quantifies it by modelling data as realisations generated from a probability space. Another approach to uncertainty quantification is to find similar data sets, and then use the variability of results between these data sets to capture the uncertainty. Both approaches allow 'error bars' to be put on estimates obtained from the original data set, although the interpretations are different. A third approach, that concentrates on giving a single answer and gives up on uncertainty quantification, could be considered as Data Engineering, although it has staked a claim in the Data Science terrain. This article presents a few (actually nine) statistical principles for data scientists that have helped me, and continue to help me, when I work on complex interdisciplinary projects. [ABSTRACT FROM AUTHOR]

The main purpose of this study is to determine the interaction between the sociodemographic characteristics of individuals and their perspectives on the participation and place of women in working life. Secondary data were used in the study. The data set used is the 2014 data of the World Values Survey Turkey. Nonlinear Canonical Correlation Analysis, which is one of the Optimal Scaled multivariate analysis techniques, was applied on the data in accordance with the purpose of study. The key finding; there is a moderate relationship between the socio-demographic characteristics of individuals and their perspectives on the place of women in working life. Then, Two Step Cluster analysis was applied on the data set in order to aggregate the individuals in Turkey according to their socio-demographic characteristics and their perspectives of women in working life. According to the findings of analysis, it can be said that housewives have a more discriminatory perspective whereas the fulltime employees have a more egalitarian one. In addition, for those with a secondary level of education, it was determined that the perspective on the subject changed from discriminatory to egalitarian as the level of education increases. As a result, education level and participation of women in working life should be increased. [ABSTRACT FROM AUTHOR]

To evaluate the impact of a multidisciplinary, project-based course in introductory statistics, this exploratory study examined learning experiences, feelings of confidence, and interest in future experiences with data for undergraduate students in Ghana, West Africa. Students completed a onesemester, introductory statistics course utilizing the Passion-Driven Statistics curriculum. Results showed more than half of the students put more effort into the course and found the material more challenging compared to other courses, while nearly three-quarters reported interest in one or more follow-up courses. Importantly, students also reported increased confidence in a variety of applied statistical skills. These findings demonstrate the positive impact of a multidisciplinary, project-based curriculum on undergraduate students in Ghana, West Africa and demonstrate the potential for its global portability. [ABSTRACT FROM AUTHOR]

Measuring workplace performance is important to emergency department management. If an unreliable model is used, the results will be inaccurate. Use of inaccurate results to make decisions, such as how to distribute the incentive pay, will lead to rewarding the wrong people and will potentially demoralize top performers. This article demonstrates a statistical model to reliably measure the work accomplished, which can then be used as a performance measurement. [West J Emerg Med.2012;13(2):176–180.]

Robust design is widely recognised as a leading method for reducing variability and improving quality. Most of the engineering statistics literature focuses on finding point estimates of the optimum operating conditions for robust design. Various procedures have been considered for calculating point estimates of the optimum operating conditions. Although the point estimation procedure is important for continuous quality improvement, the immediate question is ‘‘how accurate are these optimum operating conditions?” The answer is to consider interval estimation for a single variable or joint confidence regions for multiple variables. In this paper, using the bootstrap technique, we develop procedures for obtaining joint confidence regions for the optimum operating conditions. Two different procedures, using Bonferroni and multivariate normal approximation, are introduced. The proposed methods are illustrated and substantiated using a numerical example. [ABSTRACT FROM PUBLISHER]

When it comes to manufacturing processes, the question of process quality is omnipresent. Statistical methods for monitoring quality measures are widespread and control charts are well established. Instead of checking parameters such as and , there are control charts to monitor the percent defective. While control charts under sampling by attributes are already illustrated in the literature, this paper develops and modifies different approaches of control charts for under sampling by variables. The results for a modified exponentially moving average chart, EWMA , are compared to those of several control charts. The new scheme is well suited for imperfect in-control situations, that is, the pre-run sample mean differs from a certain natural or target mean value of the monitored parameter. To calculate the most popular control chart performance measure, the average run length (), both the Markov chain approach and the collocation method are used. It turns out that the EWMA chart has properties similar to the usual EWMA and the CUSUM charts. Thus, it can be a helpful utility for practical applications. [ABSTRACT FROM AUTHOR]

Abstract An inequality is being proved which is connected to cost-effective numerical density estimation of the hyper-gamma probability distribution. The left-hand side of the inequality is a combination of two in the third parameter distinct versions of the hypergeometric function at the point one. All three parameters are functions of the distribution’s terminal shape. The first and second are equal. The distinct third parameters of the two hypergeometric functions depend on terminal and initial shape. The other side of the inequality is determined by the quotient of two infinite series, which are related to the first derivatives with respect to terminal shape of the hypergeometric functions which appear in its left-hand side.

Purpose – This paper aims to use a data-driven approach to identify the factors and metrics that provide the best indicators of academic attrition in the Korean language program at the Defense Language Institute Foreign Language Center. Design methodology approach – This research develops logistic regression models to aid in the identification of at-risk students in the Defense Language Institute’s Korean language school. Findings – The results from this research demonstrates that this methodology can detect significant factors and metrics that identify students at-risk. Additionally, this research shows that school policy changes can be detected using logistic regression models and stepwise regression. Originality value – This research represents a real-world application of logistic regression modeling methods applied to the problem of identifying at-risk students for the purpose of academic intervention or other negative outcomes. By using logistic regression, the authors are able to gain a greater understanding of the problem and identify statistically significant predictors of student attrition that they believe can be converted into meaningful policy change.

Abstract Slope stability monitoring and evaluating play vital role in the risk management of open cast mines. Generally, Issue of slope failure occurs at open cast mines due to undisciplined mining, impacts of weather conditions. Slope stability radar provide slope stability warning impending failure and also it has used for setting out threshold value. This threshold value obtained from parameter value, some previous scan data of radar at open cast mines and applied statistical analysis. After that it has carried out accurate result of slope stability monitoring area and remotely scanning region slopes to continuously measure any surface movements.

The 5th generation (5G) wireless networks propose to address a variety of usage scenarios, such as enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low-latency communications (URLLC). Due to the exponential increase in the user equipment (UE) devices of wireless communication technologies, 5G and beyond networks (B5G) expect to support far higher user density and far lower latency than currently deployed cellular technologies, like long-term evolution-Advanced (LTE-A). However, one of the critical challenges for B5G is finding a clever way for various channel access mechanisms to maintain dense UE deployments. Random access channel (RACH) is a mandatory procedure for the UEs to connect with the evolved node B (eNB). The performance of the RACH directly affects the performance of the entire network. Currently, RACH uses a uniform distribution-based (UD) random access to prevent a possible network collision among multiple UEs attempting to access channel resources. However, in a UD-based channel access, every UE has an equal chance to choose a similar contention preamble close to the expected value, which causes an increase in the collision among the UEs. Therefore, in this paper, we propose a Poisson process-based RACH (2PRACH) alternative to a UD-based RACH. A Poisson process-based distribution, such as exponential distribution, disperses the random preambles between two bounds in a Poisson point method, where random variables occur continuously and independently with a constant parametric rate. In this way, our proposed 2PRACH approach distributes the UEs in a probability distribution of a parametric collection. Simulation results show that the shift of RACH from UD-based channel access to a Poisson process-based distribution enhances the reliability and lowers the network’s latency.

The multiple regression analysis method is used for quantitative estimation of the complex of factors affecting the mechanical properties of the thermomechanically strengthened rebars; this method permits selecting the most valuable quantities characterizing σy, σuts, and δ and assessing the degree of influence of separate elements on these properties within the standards and technological parameters of strengthening. A mathematical model of formation of the strength and plastic characteristics of thermomechanically strengthened rebars is developed, which allows predicting their final mechanical properties. The analysis of the derived relations shows that the established character of the dependences, determined by the signs of coefficients, responds to the physical meaning of a technological factor at consideration. [ABSTRACT FROM AUTHOR]

Blended learning refers to a method of teaching and learning in which some form of online learning is used in addition to the traditional on-campus/face-to-face learning experience in an integrated manner. Postgraduate Applied Statistics programs at Swinburne University of Technology, Australia adopted the blended learning paradigm almost a decade ago to accommodate an increasingly diverse student population. This paradigm allows for flexibility in design approaches, and accommodates the range of blended learning capabilities and experience of teachers and learners. The blended learning design adopted in these programs has involved the thoughtful integration of learning and teaching approaches in both on-campus, face-to-face and online/virtual learning environments by utilising the benefits of each of these environments. These programs focus on designing learning interactions across formal teaching spaces, informal learning spaces and online learning and teaching spaces. This flexible approach has been well accepted among both online and on-campus students. This paper describes the medium impact blended learning model adopted in these courses and the feedback received from students during recent study periods. The authors make the case that implementation of a successful blended learning model can enhance students' learning experience for a mixed cohort of participants. [ABSTRACT FROM AUTHOR]

This paper aims to quantify the amount of suitable rooftop area for photovoltaic (PV) energy generation in the continental United States (US). The approach is data-driven, combining Geographic Information Systems analysis of an extensive dataset of Light Detection and Ranging (LiDAR) measurements collected by the Department of Homeland Security with a statistical model trained on these same data. The model developed herein can predict the quantity of suitable roof area where LiDAR data is not available. This analysis focuses on small buildings (1000 to 5000 square feet) which account for more than half of the total available rooftop space in these data (58%) and demonstrate a greater variability in suitability compared to larger buildings which are nearly all suitable for PV installations. This paper presents new results characterizing the size, shape and suitability of US rooftops with respect to PV installations. Overall 28% of small building roofs appear suitable in the continental United States for rooftop solar. Nationally, small building rooftops could accommodate an expected 731 GW of PV capacity and generate 926 TWh/year of PV energy on 4920  of suitable rooftop space which equates to 25% the current US electricity sales. [ABSTRACT FROM AUTHOR]