We investigate the generation of electromagnetic and gravitational radiation in the vicinity of a perturbed Schwarzschild black hole. The gravitational perturbations and the electromagnetic field are studied by solving the Teukolsky master equation with sources, which we take to be locally charged, radially infalling, matter. Our results show that, in addition to the gravitational wave generated as the matter falls into the black hole, there is also a burst of electromagnetic radiation. This electromagnetic field has a characteristic set of quasinormal frequencies, and the gravitational radiation has the quasinormal frequencies of a Schwarzschild black hole. This scenario allows us to compare the gravitational and electromagnetic signals that are generated by a common source.

The Global Fear Index (GFI) is a measure of fear/panic based on the number of people infected and deaths due to COVID-19. This paper aims to examine the interconnection or interdependencies between the GFI and a set of global indexes related to the financial and economic activities associated with natural resources, raw materials, agribusiness, energy, metals, and mining, such as: the S&P Global Resource Index, the S&P Global Agribusiness Equity Index, the S&P Global Metals and Mining Index, and the S&P Global 1200 Energy Index. To this end, we first apply several common tests: Wald exponential, Wald mean, Nyblom, and Quandt Likelihood Ratio. Subsequently, we apply Granger causality using a DCC-GARCH model. Data for the global indices are daily from 3 February 2020 to 29 October 2021. The empirical results obtained show that the volatility of the GFI Granger causes the volatility of the other global indices, except for the Global Resource Index. Moreover, by considering heteroskedasticity and idiosyncratic shocks, we show that the GFI can be used to predict the co-movement of the time series of all the global indices. Additionally, we quantify the causal interdependencies between the GFI and each of the S&P global indices using Shannon and Rényi transfer entropy flow, which is comparable to Granger causality, to confirm directionality more robustly The main conclusion of this research is that financial and economic activity related to natural resources, raw materials, agribusiness, energy, metals, and mining were affected by the fear/panic caused by COVID-19 cases and deaths.

The relationship between three different groups of COVID-19 news series and stock market volatility for several Latin American countries and the U.S. are analyzed. To confirm the relationship between these series, a maximal overlap discrete wavelet transform (MODWT) was applied to determine the specific periods wherein each pair of series is significantly correlated. To determine if the news series cause Latin American stock markets’ volatility, a one-sided Granger causality test based on transfer entropy (GC-TE) was applied. The results confirm that the U.S. and Latin American stock markets react differently to COVID-19 news. Some of the most statistically significant results were obtained from the reporting case index (RCI), A-COVID index, and uncertainty index, in that order, which are statistically significant for the majority of Latin American stock markets. Altogether, the results suggest these COVID-19 news indices could be used to forecast stock market volatility in the U.S. and Latin America

Recently, statistical reasoning has been of vital importance not only in quantitative analysis but also in the interpretation of graphs at all educational levels. There are students that can make calculations almost immediately but are not able to interpret or present their ideas graphically. In this way, the present study seeks to conduct a diagnostic of the problems that economic-administrative students have when reading and interpreting graphs in their statistics courses. For this, a Spanish version of the test Comprehensive Assessment of Outcomes in Statistics (CAOS) was administered. This instrument allows for the determination of reasoning applied to different types of statistical graphs and in some cases to determine what type of calculation is required to do it. The instrument was applied to 138 undergraduate students from the economic-administrative area of the University of Guadalajara during January-June 2018. The results show that a large percentage of students confuse a normal distribution with a uniform one and that they are unable to distinguish that a bias can be determined from the measures of central tendency and dispersion, as well as other statistical reasoning difficulties. This may be as a result of a deficiency that exists in statistical teaching, an insufficient mathematical preparation on the part of the students, among other factors.

The objective of this study is to analyze the ability of students of the University Center for the Economic Administrative Sciences which forms part of the University of Guadalajara from different economic-administrative undergraduate programs, to solve distinct problems in the area of probability, applying a multiple-choice instrument aligned to the learning objectives via a qualitative-descriptive methodology. The study comprised a sample of 251 students from 14 different undergraduate degree programs who were enrolled in the same statistics course. Multivariate tests were conducted in order to identify any differences in performance related to undergraduate degree program and sex, while a RASCH model was applied to provide validity evidence for the assessment. The results show that the students do not have a good level of ability for solving conditional probability problems, which they confused with the formulation of independent event problems although they did show a satisfactory level of ability for solving other types of problems.