Estimation of finite population mean using double sampling under probability proportional to size sampling in the presence of extreme values.

Values that are too large or small enough can be found in many data sets. Therefore, the estimator can yield ambiguous findings if several of the incredible deals are picked for the sample. When such extreme values occur, we propose improved estimators to determine the finite population means using double sampling based on probability proportional to size sampling (PPS). The properties of estimators are obtained up to the first order of approximations. When the size of the units varies widely, the PPS sampling technique may be employed. To determine the values of Pi when using PPS, we must be acquainted with the aggregate of the auxiliary variable Xi. However the designs and estimation techniques we have looked at so far are unsuccessful and are less effective when this information is difficult to locate or when other information is missing. The two-phase approach is preferable and more feasible in these kinds of circumstances. To demonstrate how effectively the recommended estimators performed, we used three actual data sets. We show mathematically and theoretically that the suggested estimators outperform alternative estimators.

Comparative study of artificial neural network versus parametric method in COVID-19 data analysis.

Since the previous two years, a new coronavirus (COVID-19) has found a major global problem. The speedy pathogen over the globe was followed by a shockingly large number of afflicted people and a gradual increase in the number of deaths. If the survival analysis of active individuals can be predicted, it will help to contain the epidemic significantly in any area. In medical diagnosis, prognosis and survival analysis, neural networks have been found to be as successful as general nonlinear models. In this study, a real application has been developed for estimating the COVID-19 mortality rates in Italy by using two different methods, artificial neural network modeling and maximum likelihood estimation. The predictions obtained from the multilayer artificial neural network model developed with 9 neurons in the hidden layer were compared with the numerical results. The maximum deviation calculated for the artificial neural network model was -0.14% and the R value was 0.99836. The study findings confirmed that the two different statistical models that were developed had high reliability.

The impact of Sports Activities on Economic Survival, Social Life of Women, and What do Big Data Analysis reveals about Social Life of Sports Women / El impacto de las actividades deportivas en la supervivencia económica, la vida social de las mujeres y qué revela el análisis de Big Data sobre la vida social de las mujeres deportistas

Economic Survival and Social Support in Sports activities is a multifaceted area of research interest. Active participation of women in sports activities has a significant contribution to the growth and success of the sports industry. This article seeks to discuss the impact of sports activities on sportswomen's economic survival and social life. This is done using questionnaire themed around issues such lack of availability of time, limitations of knowledge, family-related and financial difficulties, and nature of activities, all of which can potentially impact the social life of sportswomen. The study shows that economic survival is a significant part of sport's activities as well as the social life of sportswomen. Economic survival supports the social life of sportswomen. The current article highlights the findings from big data analysis pertaining to the social life of sportswomen. The paper uses Big Data Analysis to examine the social life of sportswomen. However, BDA is a complex process resulting from data sources' diverse and unstructured nature. Big Data Analysis (BDA) is critical in the sports field, as it covers the data at the macro-level (such as the national sports industry, teams, and individuals). It was analyzed using the AMOS 23v and SPSS 26v after being collected from female sports players belonging to a geographically diverse region. The study found that there is a significant association between sports activities, economic survival, and the social life of sportswomen.(AU)

Nexus between Basketball Players' Advertisement and Psychological Attraction of Food Brands: A Study of Consumer Behavior towards Food Product / Nexo entre la publicidad de los jugadores de baloncesto y la atracción psicológica de las marcas de alimentos: un estudio del comportamiento del consumidor hacia los productos alimenticios

It is vital for technology to advance and for the brand to obtain public recognition to achieve competitive supremacy. It is impossible to deploy without an adequate marketing, awareness, and execution strategy, as well as structured advertising approaches. As a result, advertising is an important strategy for marketing products to consumers. This research article makes a case for examining the psychological appeal of food brands associated with basketball players and customer behavior while making product purchases. If a well-known celebrity appears in a commercial, people will strongly influence the brand or product. Psychological Attraction (PA) and Food Brand Advertisement (FBA) were independent variables in the research framework. Customer Behavior (CB) was used as the Independent Variable. Nonetheless, data was gathered from 100 customers. SEM PLS 3 was used to analyze the collected data. The results suggested that the association between PA à CB is a non-significant link; however, the relationship between FBA àCB is significant.(AU)