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In several sporting events, the winner is chosen on the basis of a subjective score. These sports include gymnastics, ice skating, and diving. Unlike for other subjectively judged sports, diving competitions consist of multiple rounds in quick succession on the same apparatus. These multiple rounds lead to an extra layer of complexity in the data, and allow the introduction of graphical constructs and interrater-agreement methods to statistics students. The data are sufficiently easy to understand for students in introductory statistics courses, yet sufficiently complex for upper level students. In this article, I present data from a high-school diving competition that allows for investigation in graphical methods, data manipulation, and interrater agreement methods. I also provide a list of questions for exploration at the end of the document to suggest how an instructor can effectively use the data with students. These questions are not meant to be exhaustive, but rather generative of ideas for an instructor using the data in a classroom setting. Supplementary materials for this article are available online.

Much has been made of the flipped classroom as an approach to teaching, and its effect on student learning. The volume of material showing that the flipped classroom technique helps students better learn and better retain material is increasing at a rapid pace. Coupled with this technique is active learning in the classroom. There are many ways of "flipping the classroom." The particular realization of the flipped classroom that we discuss in this article is based on a method called "Just-in-Time Teaching (JiTT)." However, JiTT, in particular, and the flipped classroom, in general, is not just about watching videos before class, or doing activities during class time. JiTT includes assigning short, web-based questions to students based on previously viewed material. Typically, Internet-based questions are constructed to elicit common misunderstandings from the students, so that the instructor can correct such misunderstandings immediately in the next class period, hence the name, "Just-in-Time Teaching." The addition of these pre-class questions is what separates JiTT from a general flipped classroom model. Even as the research on the superiority of JiTT as a learner-centered pedagogical method mounts, aids for the instructor have not, at least not as quickly. This article is focused on the instructor--with aids to help the instructor begin using the JiTT flipped classroom model in statistics courses.

This paper introduces flowthrough centrality, a node centrality measure determined from the hierarchical maximum concurrent flow problem (HMCFP). Based upon the extent to which a node is acting as a hub within a network, this centrality measure is defined to be the fraction of the flow passing through the node to the total flow capacity of the node. Flowthrough centrality is compared to the commonly-used centralities of closeness centrality, betweenness centrality, and flow betweenness centrality, as well as to stable betweenness centrality to measure the stability (i.e., accuracy) of the centralities when knowledge of the network topology is incomplete or in transition. Perturbations do not alter the flowthrough centrality values of nodes that are based upon flow as much as they do other types of centrality values that are based upon geodesics. The flowthrough centrality measure overcomes the problem of overstating or understating the roles that significant actors play in social networks. The flowthrough centrality is canonical in that it is determined from a natural, realized flow universally applicable to all networks. [ABSTRACT FROM AUTHOR]

Conventional wisdom dispersed by fans and coaches in the stands at almost any high school track meet suggests female athletes typically peak around 10th grade or earlier (15 years of age), particularly for distance runners, and male athletes continuously improve. Given that universities in the United States typically recruit track and field athletes from high school teams, it is important to understand the age of peak performance at the high school level. Athletes are often recruited starting in their sophomore year of high school and individuals develop at different rates during adolescence; however, the individual development factor is usually not taken into account during recruitment. In this study, we curate data on event times for high school track and field athletes from the years 2011 to 2019 to determine the trajectory of fastest times for male and female athletes in the 200m, 400m, 800m, and 1600m races. We show, through visualizations and models, that, for most athletes, the sophomore peak is a myth. Performance is mostly dependent on the individual athlete. That said, the trajectories cluster into four or five types, depending on the race distance. We explain the significance of the types for future recruitment. [ABSTRACT FROM AUTHOR]

Abstract Background In microarray experiments with small sample sizes, it is a challenge to estimate p-values accurately and decide cutoff p-values for gene selection appropriately. Although permutation-based methods have proved to have greater sensitivity and specificity than the regular t-test, their p-values are highly discrete due to the limited number of permutations available in very small sample sizes. Furthermore, estimated permutation-based p-values for true nulls are highly correlated and not uniformly distributed between zero and one, making it difficult to use current false discovery rate (FDR)-controlling methods. Results We propose a model-based information sharing method (MBIS) that, after an appropriate data transformation, utilizes information shared among genes. We use a normal distribution to model the mean differences of true nulls across two experimental conditions. The parameters of the model are then estimated using all data in hand. Based on this model, p-values, which are uniformly distributed from true nulls, are calculated. Then, since FDR-controlling methods are generally not well suited to microarray data with very small sample sizes, we select genes for a given cutoff p-value and then estimate the false discovery rate. Conclusion Simulation studies and analysis using real microarray data show that the proposed method, MBIS, is more powerful and reliable than current methods. It has wide application to a variety of situations.

Gareth JANACEK, London: Arnold Publishers, 2001. ISBN 0-340-71999-0. xv + 151 pp. $34.95 (P). The field of time series is in need of a small, applications-oriented text accessible to both [...]

The abstraction augmented Markov model (AAMM) is an extension of a Markov model that can be used for the analysis of genetic sequences. It is developed using the frequencies of all possible consecutive words with same length ( p-mers). This article will review the theory behind AAMM and apply the theory behind AAMM in metagenomic classification. [ABSTRACT FROM AUTHOR]

Flow cytometry (FCM) is a fluorescence-based single-cell experimental technology that is routinely applied in biomedical research for identifying cellular biomarkers of normal physiological responses and abnormal disease states. While many computational methods have been developed that focus on identifying cell populations in individual FCM samples, very few have addressed how the identified cell populations can be matched across samples for comparative analysis. This article presents FlowMap-FR, a novel method for cell population mapping across FCM samples. FlowMap-FR is based on the Friedman-Rafsky nonparametric test statistic (FR statistic), which quantifies the equivalence of multivariate distributions. As applied to FCM data by FlowMap-FR, the FR statistic objectively quantifies the similarity between cell populations based on the shapes, sizes, and positions of fluorescence data distributions in the multidimensional feature space. To test and evaluate the performance of FlowMap-FR, we simulated the kinds of biological and technical sample variations that are commonly observed in FCM data. The results show that FlowMap-FR is able to effectively identify equivalent cell populations between samples under scenarios of proportion differences and modest position shifts. As a statistical test, FlowMap-FR can be used to determine whether the expression of a cellular marker is statistically different between two cell populations, suggesting candidates for new cellular phenotypes by providing an objective statistical measure. In addition, FlowMap-FR can indicate situations in which inappropriate splitting or merging of cell populations has occurred during gating procedures. We compared the FR statistic with the symmetric version of Kullback-Leibler divergence measure used in a previous population matching method with both simulated and real data. The FR statistic outperforms the symmetric version of KL-distance in distinguishing equivalent from nonequivalent cell populations. FlowMap-FR was also employed as a distance metric to match cell populations delineated by manual gating across 30 FCM samples from a benchmark FlowCAP data set. An F-measure of 0.88 was obtained, indicating high precision and recall of the FR-based population matching results. FlowMap-FR has been implemented as a standalone R/Bioconductor package so that it can be easily incorporated into current FCM data analytical workflows. © 2015 International Society for Advancement of Cytometry [ABSTRACT FROM AUTHOR]

Much has been made of the flipped classroom as an approach to teaching, and its effect on student learning. The volume of material showing that the flipped classroom technique helps students better learn and better retain material is increasing at a rapid pace. Coupled with this technique is active learning in the classroom. There are many ways of “flipping the classroom.” The particular realization of the flipped classroom that we discuss in this article is based on a method called “Just-in-Time Teaching (JiTT).” However, JiTT, in particular, and the flipped classroom, in general, is not just about watching videos before class, or doing activities during class time. JiTT includes assigning short, web-based questions to students based on previously viewed material. Typically, Internet-based questions are constructed to elicit common misunderstandings from the students, so that the instructor can correct such misunderstandings immediately in the next class period, hence the name, “Just-in-Time Teaching.” The addition of these pre-class questions is what separates JiTT from a general flipped classroom model. Even as the research on the superiority of JiTT as a learner-centered pedagogical method mounts, aids for the instructor have not, at least not as quickly. This article is focused on the instructor—with aids to help the instructor begin using the JiTT flipped classroom model in statistics courses. [ABSTRACT FROM AUTHOR]