The quasi-xgamma frailty model with survival analysis under heterogeneity problem, validation testing, and risk analysis for emergency care data.

Frailty models are important for survival data because they allow for the possibility of unobserved heterogeneity problem. The problem of heterogeneity can be existed due to a variety of factors, such as genetic predisposition, environmental factors, or lifestyle choices. Frailty models can help to identify these factors and to better understand their impact on survival. In this study, we suggest a novel quasi xgamma frailty (QXg-F) model for the survival analysis. In this work, the test of Rao-Robson and Nikulin is employed to test the validity and suitability of the probabilistic model, we examine the distribution's properties and evaluate its performance in comparison with many relevant cox-frailty models. To show how well the QXg-F model captures heterogeneity and enhances model fit, we use simulation studies and real data applications, including a fresh dataset gathered from an emergency hospital in Algeria. According to our research, the QXg-F model is a viable replacement for the current frailty modeling distributions and has the potential to improve the precision of survival analyses in a number of different sectors, including emergency care. Moreover, testing the ability and the importance of the new QXg-F model in insurance is investigated using simulations via different methods and application to insurance data.

A new Topp-Leone Kumaraswamy Marshall-Olkin generated family of distributions with applications.

We aim in this paper to propose a novel class of distributions that was created by merging the Topp-Leone distribution and the Generated families of Kumaraswamy and Marshall-Olkin. Its cumulative distribution function characterizes it and includes rational and polynomial functions. In particular, the following desirable properties of the new family are presented: Shannon entropy, order statistics, the quantile power series, and several associated measures and functions. Then, using a specific family member identified before, we create a parametric statistical model with the basic distribution being the inverse exponential distribution. Finally, a thorough investigation has been made to implement this new distribution with three data sets: the glass fibers data set, the glass Alumina data set and the hailing times data set. In comparison to six prominent competitors, the new model performs favorably on all statistical tests and criteria that were examined.

A new approach for estimating variance of a population employing information obtained from a stratified random sampling.

In this article, we suggest an enhanced estimator for the estimation of finite population variance using twofold auxiliary variable under stratified random sampling. The numerical expressions for the bias and MSE are determined up to the first order of approximation. In order to effectively validate the theoretical findings, three actual data sets are included. Additionally, the application of the suggested estimators is demonstrated using a simulation study. Results of an empirical comparison among the suggested and existing estimators were investigated. To determine how good the suggested estimator, in comparison to the preliminary estimators, the MSE criterion is used. The suggested estimator has a smaller MSE and better PRE than existing estimators, according to numerical results utilizing actual data sets and a simulation analysis.

The Influence of Social Media on the Decision of Rhinoplasty Among Adults in the Western and Southern Regions of Saudi Arabia: A Comparative Cross-Sectional Study.

Introduction Rhinoplasty, a cosmetic surgical procedure aimed at altering the appearance of the nose, has gained immense popularity worldwide. Patients undergo this procedure for various reasons, ranging from aesthetic concerns to functional impairments. Social media, being a ubiquitous platform for sharing and consuming visual content, has emerged as a potential influencer for individuals contemplating rhinoplasty. This study aims to investigate the impact of social media on the prevalence of rhinoplasty among individuals residing in the southern and western regions of Saudi Arabia. Methods A cross-sectional study was conducted through an online self-administered questionnaire, targeting male and female adults aged 18 years or older, residing in the western and southern regions of Saudi Arabia. The questionnaire comprised 17 questions, categorized into two sections. The first section sought demographic information, including age, gender, education, and other relevant characteristics. The second section focused on the influence of social media on the decision-making process related to rhinoplasty. Results A total of 1645 participants responded to the survey, with 96.80% being Saudi citizens. The majority of respondents were females (69.11%); 58.52% of the respondents were from the western region of Saudi Arabia, while 41.48% lived in the southern region. Most participants (64.27%) were aged between 18 and 30 years. The study revealed that Snapchat (Snap Inc., Santa Monica, California, United States) was the most influential social media platform, with 43.41% of respondents reporting it as the key influencer for their decision to undergo rhinoplasty. Twitter (Twitter, Inc., San Francisco, California, United States) and Instagram (Meta Platforms, Inc., Menlo Park, California, United States) followed at 22.97% and 12.09%, respectively. Interestingly, 28.42% of respondents acknowledged that social media played a significant role in their decision to undergo rhinoplasty, particularly when promoted by celebrities or trusted figures. Comparing responses from the western and southern regions, the study showed that individuals from the southern region were relatively more influenced by social media, with 27.8% and 29.3% of respondents reporting the influence from the two regions, respectively. Out of the total respondents, only 38.75% reported dissatisfaction with their nose's appearance and condition, while 23.60% expressed a tendency towards undergoing rhinoplasty. Conclusion The study's findings underscore the critical role of social media in influencing patients' decisions to undergo rhinoplasty, particularly in the southern region of Saudi Arabia. Snapchat emerged as the most influential social media platform, with celebrities' pictures before and after the procedure being the leading factor in motivating patients to undergo rhinoplasty. The study highlights the need for further research to explore the potential risks and benefits associated with the influence of social media on patients' decision-making regarding rhinoplasty.

Influence of COVID-19 vaccination on the dynamics of new infected cases in the world.

The initial COVID-19 vaccinations were created and distributed to the general population in 2020 thanks to emergency authorization and conditional approval. Consequently, numerous countries followed the process that is currently a global campaign. Taking into account the fact that people are being vaccinated, there are concerns about the effectiveness of that medical solution. Actually, this study is the first one focusing on how the number of vaccinated people might influence the spread of the pandemic in the world. From the Global Change Data Lab "Our World in Data", we were able to get data sets about the number of new cases and vaccinated people. This study is a longitudinal one from 14/12/2020 to 21/03/2021. In addition, we computed Generalized log-Linear Model on count time series (Negative Binomial distribution due to over dispersion in data) and implemented validation tests to confirm the robustness of our results. The findings revealed that when the number of vaccinated people increases by one new vaccination on a given day, the number of new cases decreases significantly two days after by one. The influence is not notable on the same day of vaccination. Authorities should increase the vaccination campaign to control well the pandemic. That solution has effectively started to reduce the spread of COVID-19 in the world.

Public Knowledge of Osteoarthritis in Al-Qunfudah Governorate, Saudi Arabia.

BACKGROUND: Joint pain is one of the most frequent complaints among adults and older people in primary healthcare settings worldwide. There are many causes for joint pain, osteoarthritis (OA) is so far the most prevalent form of arthritis that causes joint pain. It can attack almost any joint, but the most frequently affected joints are the hands, knees, hips, and spine. This study aimed to identify public knowledge of OA and its associative variables in Al-Qunfudah governorate, Saudi Arabia. MATERIALS AND METHODS: A cross-sectional descriptive community-based study was carried out among the general population in the Al-Qunfudah governorate. The research data were collected over two months, from November to December 2022, via an Arabic version of a self-administrated online survey of 29 items. RESULTS: A total of 746 respondents were included in this study. The majority of them were females (78%). The age group 18-29 was predominant. In terms of education, 69.9% were holding university degrees. The overall participants' knowledge of OA was poor at 36.1%, fair at 36.8%, and good at 26.9%. The associative variables with better participants' knowledge were; holding university degrees (P=0.021), being a student (P<0.001) and living in urban areas (P=0.020), having normal BMI (P=0.018), and depending on the school topics as a source of information (P<0.001). Good knowledge was significantly higher among healthy individuals and non-smokers (P<0.001) for each variable. CONCLUSION:  This study reveals the lack of knowledge of osteoarthritis among the general population in Al-Qunfudah governorate, Saudi Arabia. Being a student, university educated, from urban areas, and having a normal BMI, all were associative factors with good knowledge. Therefore, this study highlights the necessity for providing awareness and educational campaigns for the public, focusing on the rural population.

Parameter Estimation in Step Stress Partially Accelerated Life Testing under Different Types of Censored Data.

A long testing period is usually required for the life testing of high-reliability products or materials. It is possible to shorten the testing process by using ALTs (accelerated life tests). Due to the fact that ALTs test products in harsher settings than are typical use conditions, the life expectancy of the objects they evaluate is reduced. Censored data in which the specific failure timings of all units assigned to test are not known, or all units assigned to test have not failed, may arise in ALTs for a variety of reasons, including operational failure, device malfunction, expense, and time restrictions. In this paper, we have considered the step stress partially accelerated life test (SSPALT) under two different censoring schemes, namely the type-I progressive hybrid censoring scheme (type-I PHCS) and the type-II progressive censorship scheme (type-II PCS). The failure times of the items are assumed to follow NH distribution, while the tampered random variable (TRV) model is used to explain the effect of stress change. In order to obtain the estimates of the unknown parameters, the maximum likelihood estimation (MLE) approach is adopted. Furthermore, based on the asymptotic theory of MLEs, the approximate confidence intervals (ACIs) are also constructed. The point estimates under two censoring schemes are compared in terms of root mean squared errors (RMSEs) and relative absolute biases (RABs), while ACIs are compared in terms of their lengths and coverage probabilities (CPs). The performance of the estimators has been evaluated and compared under two censoring schemes with various sample sizes through a simulation study. Simulation results show that estimates with type-I PHCS outperform estimates with type-II PCS in terms of RMSEs, RABs, lengths, and CPs. Finally, a real-world numerical example of insulating fluid failure times is presented to show how the approaches will work in reality.

Brand Awareness via Online Media: An Evidence Using Instagram Medium with Statistical Analysis.

Online marketing refers to the practices of promoting a company's brand to its potential customers. It helps the companies to find new venues and trade worldwide. Numerous online media such as Facebook, YouTube, Twitter, and Instagram are available for marketing to promote and sell a company's product. However, in this study, we use Instagram as a marketing medium to see its impact on sales. To carry out the computational process, the approach of linear regression modeling is adopted. Certain statistical tests are implemented to check the significance of Instagram as a marketing tool. Furthermore, a new statistical model, namely a new generalized inverse Weibull distribution, is introduced. This model is obtained using the inverse Weibull model with the new generalized family approach. Certain mathematical properties of the new generalized inverse Weibull model such as moments, order statistics, and incomplete moments are derived. A complete mathematical treatment of the heavy-tailed characteristics of the new generalized inverse Weibull distribution is also provided. Different estimation methods are discussed to obtain the estimators of the new model. Finally, the applicability of the new generalized inverse Weibull model is established via analyzing Instagram advertising data. The comparison of the new distribution is made with two other models. Based on seven analytical tools, it is observed that the new distribution is a better model to deal with data in the business, finance, and management sectors.

The Role of Twitter Medium in Business with Regression Analysis and Statistical Modelling.

Marketing refers to the strategies a company undertakes to promote its brands to its potential audience. Advertising provides useful venues for marketing to promote a company's survives/goods to the audience. It has a positive impact on the sale of services or products. In this study, we consider a well-known online medium called Twitter (the fourth most popular social media platform used by marketers) to check its impact on sales. For this purpose, the simple linear regression modeling approach is implemented to test the significance and usefulness of Twitter advertising on sale. Statistical tests such as t-test and correlation test are adopted to test the hypothesis of the "impact of Twitter advertising on sales." Based on the findings of this study, it is observed that Twitter advertising has a positive impact on sales. Furthermore, a new statistical model called the exponential T-X exponentiated exponential is introduced. The proposed model is very interesting and possesses heavy-tailed characteristics which are useful in finance and other related sectors. Finally, the applicability of the new model is illustrated by considering the sales data.

Stress-Strength Parameter Estimation under Small Sample Size: A Testing Hypothesis Approach.

In this paper, uniformly most powerful unbiased test for testing the stress-strength model has been presented for the first time. The end of the paper is recommending a method which is appropriate for no large data where a normal asymptotic distribution is not applicable. The previous methods for inference on stress-strength models use almost all the asymptotic properties of maximum likelihood estimators. The distribution of components is considered exponential and generalized logistic. A corresponding unbiased confidence interval is constructed, too. We compare presented methodology with previous methods and show the method of this paper is logically better than other methods. Interesting result is that our recommended method not only uses from small sample size but also has better result than other ones.

Impact of YouTube Advertising on Sales with Regression Analysis and Statistical Modeling: Usefulness of Online Media in Business.

Computer technology plays a prominent role in almost every aspect of daily life including education, health care, online shopping, advertising, and even in homes. Computers help to make daily tasks much easier and convenient. Among social media, YouTube is a well-known social sharing networking service. As more and more people join social media and become everyday users, brands have also increased their online engagement. However, it is still unclear how to effectively measure value and return on advertising using social media. As of 2021, more than 31 million YouTube channels around the globe have been opened. In this paper, we consider YouTube advertising to check its effectiveness and benefits gained. Certain statistical tools are adopted to measure the extent of advertising benefits and their correlation in creating effective advertising campaigns on YouTube. Simple linear regression analysis is performed on the data representing the YouTube advertising budget of a company and the sales data of that company. Furthermore, we develop a new statistical distribution to provide the best description of the YouTube advertising data. The result of this research shows that YouTube is an effective medium for advertising and has a strong relationship with sales.

Impact of Facebook and Newspaper Advertising on Sales: A Comparative Study of Online and Print Media.

Marketing means the strategies and tactics an organization undertakes for attracting consumers to promote the buying or selling of a product or service. Active marketing is about receiving messages from potential buyers to create ways to influence their purchasing decisions. Advertising is one of the most prominent marketing strategies to promote products to consumers. It is well known that advertisement has a significant impact on the sale of certain goods or services. In this paper, we consider two mediums of advertisement, such as Facebook (which is an online medium) and Newspaper (which is a printed medium). We consider a dataset representing the advertising budget (in hundreds of US dollars) of an electronic company and the sales of that company. We apply the quantitative research approach, and the data which are used in this research are secondary data. For analysis purposes, we consider a statistical tool called simple linear regression modeling. To check the significance of the advertising on sale, definite statistical tests are applied. Based on the findings of this research, it is observed that advertising has a significant impact on sales. It is also showed that spending money on advertising through Facebook has better sales than newspapers. The finding of this research shows that the use of computer-based technologies and online mediums has a brighter future for advertising. Furthermore, a new statistical model is introduced using the Z family approach. The proposed model is very interesting and possesses heavy-tailed properties. Finally, the applicability of the proposed model is illustrated by considering the financial dataset.