R es una herramienta informática (específicamente, un lenguaje computacional) sumamente potente para realizar distintos cálculos científicos, numéricos y estadísticos, así como para crear gráficas y figuras de gran calidad. R, de hecho, tiene hoy en día gran popularidad entre la comunidad académica y entre distintos profesionales. Esto no es casual puesto que R es un excelente producto que, entre una gama de virtudes, es gratuito, relativamente fácil de operar y cuenta con una gran comunidad de internet que contribuye a resolver dudas y problemas, sin costo alguno. La finalidad de este artículo es mostrar algunas de las principales características de R y dar una breve historia de él, mencionar algunas de sus aplicaciones y comentar acerca de su posicionamiento relativo respecto a otras herramientas informáticas similares. Finalmente, se invita al lector al uso de R para que sea una útil herramienta para resolver sus propios problemas.

The official statistics generated by government offices and ministries are fundamental to guide actions in the decisionmaking process within a country. For example, official statistics could be used to promote and allocate public goods like local, regional and national budgets. Moreover, due to the high quality standard required to generate official data, such information is ideal to promote a better understanding about the important facts in economic, sociodemographic, geographic, environmental and governmental management issues. However, it has been observed that many undergraduate students do not use such data in an extensive manner, and they reveal limitations in accessing, analyzing and extrapolating such information to their benefit, both in academic and personal settings. The aim of this work is to describe to what extent Mexican undergraduate students use official statistics as part of their academic assignments, and to identify the main obstacles they encounter when trying to access and use official statistics.

Summary We present a proposal for helping students to cope with statistical word problems related to the classification of different cases of confidence intervals. The proposal promotes an environment where students can explicitly discuss the reasons underlying their classification of cases.

One of the aims of this work is to highlight the need for connecting the practice and theoretical studies of industrial engineers. One reason for this need is the fact that students tend to graduate without proper preparation for practice, spreading thus a bad reputation of statistics and its potential, and even affecting students’ dispositions and motivation towards the study and applications of statistics. This paper presents the results of a study conducted at two higher-education institutions in Mexico. The industrial engineering students who participated were introduced to a multivariate statistics course, one in a traditional way and the other through a problem-solving approach embedded within an industrial environment. The didactic intervention in both groups and the problems used to evaluate them are described. The results show that the experimental students had a significant increase in their qualifications and alower variance in their performance. From our study we can suggest that a university education in close connection with applications in an industrial environment significantly improves the students’ education. This teaching experiment provides students with opportunities to experience the genuine character of statistics as an applied field, giving meaning to the statistical techniques learnt in the classroom. It is one way to make the education in statistics more apt to the demand from outside and by the same time it enables the students to really understand statistics. First published February 2020 at Statistics Education Research Journal Archives

In the implementation of a linear regression model, it is crucial to carry out significance tests to assure the proper inclusion of parameters. In addition, in order to perform such a hypothesis test, students should verify the properties of the residuals and the most important is undoubtedly the normality of the residuals. However, it is clear that there is, in general, an important gap between the hypothesis testing and residuals verification stages. This paper presents an empirical study about the most probable confusions and misunderstandings students have in relating these two mental statistical constructs in a course of Administrative Statistics in a Mexican higher education institute. One of the main results is that the order in which these two concepts are taught is essential to guarantee that there is not a disconnection between the two stages, and perhaps it would be better to first analyze the properties of the residuals and then to engage in the hypothesis testing phase.