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.

A pandemic caused by the novel coronavirus (COVID‐19) is causing an unprecedented situation for the world’s health services. Health, local communities, and government are adversely affected by the COVID‐19 pandemic. Moreover, on January 21, 2020, the WHO Emergency Committee declared a global health emergency because of increasing numbers of COVID‐19 case notifications from countries overseas. While the pandemic caused grave damage to socioeconomic phenomena and multiple global phenomena such as commodity prices, remittances, trade, tourism, significant job loss, and drastically lower wages, the pandemic has also negatively affected multiple global phenomena. The advent of technology has spurred significant changes in many aspects of our lives and improved the exchange of information, the presentation of data, and the management of medical resources through telemedicine. In this article, we present a mathematical data visualization approach for analyzing pandemic data behaviors, such as exponential growth and deviations using the data related to COVID‐19 events. Furthermore, this article will include studies on the implications of the COVID‐19 pandemic on finance sector.

The spread of the COVID‐19 epidemic, since December 2019, has caused much damage around the world, disturbed every aspect of daily life, and has become a serious health threat. The COVID‐19 epidemic impacted nearly 150 countries around the globe between December 2019 and March 2020. Since December 2019, researchers have been trying to develop new suitable statistical models to adequately describe the behavior of this deadly pandemic. In this paper, a flexible statistical model has been proposed that can be used to model the lifetime events associated with this deadly pandemic. The new distribution is derived from the combination of an extended Weibull distribution and a trigonometric strategy referred to as the arcsine‐X approach. Hence, the new model may be referred to as the arcsine new flexible extended Weibull model. The proposed model is capable of capturing five different behaviors of the hazard rate function. The model parameters are estimated via the maximum likelihood approach. Furthermore, a Monte Carlo study is conducted to assess the behavior of the estimators. Finally, the applicability of the new model is demonstrated using the data of fifty‐three patients taken from a hospital in China.

In this article, we compare autometrics and machine learning techniques including Minimax Concave Penalty (MCP), Elastic Smoothly Clipped Absolute Deviation (E-SCAD), and Adaptive Elastic Net (AEnet). For simulation experiments, three kinds of scenarios are considered by allowing the multicollinearity, heteroscedasticity, and autocorrelation conditions with varying sample sizes and the varied number of covariates. We found that all methods show improved their performance for a large sample size. In the presence of low and moderate multicollinearity and low and moderate autocorrelation, the considered methods retain all relevant variables. However, for low and moderate multicollinearity, excluding AEnet, all methods keep many irrelevant predictors as well. In contrast, under low and moderate autocorrelation, along with AEnet, the Autometrics retain less irrelevant predictors. Considering the case of extreme multicollinearity, AEnet retains more than 93 percent correct variables with an outstanding gauge (zero percent). However, the potency of remaining techniques, specifically MCP and E-SCAD, tends towards unity with augmenting sample size but capturing massive irrelevant predictors. Similarly, in case of high autocorrelation, E-SCAD has shown good performance in the selection of relevant variables for a small sample, while in gauge, Autometrics and AEnet are performed better and often retained less than 5 percent irrelevant variables. In the presence of heteroscedasticity, all techniques often hold all relevant variables but also suffer from overspecification problems except AEnet and Autometrics which circumvent the irrelevant predictors and establish the true model precisely. For an empirical application, we take into account the workers remittance data for Pakistan along its twenty-seven determinants spanning from 1972 to 2020 for Pakistan. The AEnet selected thirteen relevant covariates of workers remittance while E-SCAD and MCP suffered from an overspecification problem. Hence, the policymakers and practitioners should focus on the relevant variables selected by AEnet to improve workers' remittance in the case of Pakistan. In this regard, the Pakistan government has devised policies that make it easy to transfer remittances legally and mitigate the cost of transferring remittances from abroad. The AEnet approach can help policymakers arrive at relevant variables in the presence of a huge set of covariates, which in turn produce accurate predictions.