Statistical tests and underlying data used to generate the graphs.

Main features of empirical graphs: Order (number of nodes), size (number of edges), and edge density (ratio between the size and the graph maximum size).

Each empirical graph is associated with an estimated p-value of being an outcome of an Erdős-Rényi, Fitness scale-free model, a Watts-Strogatz small word or a Geometric model. As in Table 1, empirical graphs are sorted according to their order.

Djibouti Imports of Other made textile articles, sets, worn clothing was US$10.99 Million during 2023, according to the United Nations COMTRADE database on international trade. Djibouti Imports of Other made textile articles, sets, worn clothing - data, historical chart and statistics - was last updated on November of 2025.

Utilizing Richly Attributed Graphs to Reason from Heterogeneous Data - Part 1

Raw data for statistical analysis of data from human cells and from Drosophila melanogaster. Data for graphs.

How do statistical regression results compare to intuitive, visually fitted results? Fitting lines by eye through a set of points has been explored since the 20th century. Common methods of fitting trends by eye involve maneuvering a string, black thread, or ruler until the fit is suitable, then drawing the line through the set of points. In 2015, the New York Times introduced an interactive feature, called “You Draw It,” where readers were asked to input their own assumptions about various metrics and compare how these assumptions relate to reality. In this article, we validate “You Draw It” as a method for graphical testing, comparing results to the less technological method used in Mosteller et al. and extending that study with formal statistical analysis methods. Results were consistent with those found in the previous study; when shown points following a linear trend, participants tended to fit the slope of the principal axis over the slope of the least-squares regression line. This trend was most prominent when shown data simulated with larger variances. This study reinforces the differences between intuitive visual model fitting and statistical model fitting, providing information about human perception as it relates to the use of statistical graphics. Supplementary materials for this article are available online.

Because of the “curse of dimensionality,” high-dimensional processes present challenges to traditional multivariate statistical process monitoring (SPM) techniques. In addition, the unknown underlying distribution of and complicated dependency among variables such as heteroscedasticity increase the uncertainty of estimated parameters and decrease the effectiveness of control charts. In addition, the requirement of sufficient reference samples limits the application of traditional charts in high-dimension, low-sample-size scenarios (small n, large p). More difficulties appear when detecting and diagnosing abnormal behaviors caused by a small set of variables (i.e., sparse changes). In this article, we propose two change-point–based control charts to detect sparse shifts in the mean vector of high-dimensional heteroscedastic processes. Our proposed methods can start monitoring when the number of observations is a lot smaller than the dimensionality. The simulation results show that the proposed methods are robust to nonnormality and heteroscedasticity. Two real data examples are used to illustrate the effectiveness of the proposed control charts in high-dimensional applications. The R codes are provided online.

Abstract In order to verify the kinematics graphs comprehension for a group of high school students, it is recommended to use an instrument that has a content valid by experts and a statistical validation. In this sense, was chosen the updated version of Test of Understanding Graphs in Kinematics (TUG-K), proposed in 2017 by Zavala and originally created by Beichner, in 1994. The TUG-K was elaborated to measure the understanding of graphs in kinematics of university students, mostly. Therefore, for this test to be used in basic education, it is necessary to know if it has statistical validity in this context. Consequently, a statistical analysis of the test was performed, after being applied at two different moments, with upper secondary level students of a federal school in Rio de Janeiro. The measured parameters were the same used by Zavala. The main result of this article was to demonstrate that TUG-K validity in this group. As a complement, it was shown that Hake's normalized learning gain from this group of students exposed to kinematics lectures was 17%, a value that is expected to be in traditional teaching. The underlying perspective is to disseminate and encourage the use of TUG-K in high school.

Descriptive statistics and correlations.

Statistical estimators of the triangles between ROIs.

Diseasomics Edges Statistics.