R Scripts contain statistical data analisys for streamflow and sediment data, including Flow Duration Curves, Double Mass Analysis, Nonlinear Regression Analysis for Suspended Sediment Rating Curves, Stationarity Tests and include several plots.

The various performance criteria applied in this analysis include the probability of reaching the ultimate target, the costs, elapsed times and system vulnerability resulting from any intrusion. This Excel file contains all the logical, probabilistic and statistical data entered by a user, and required for the evaluation of the criteria. It also reports the results of all the computations.

United States - Statistical discrepancy was -140486.00000 Mil. of $ in January of 2024, according to the United States Federal Reserve. Historically, United States - Statistical discrepancy reached a record high of 146227.00000 in January of 2009 and a record low of -140486.00000 in January of 2024. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Statistical discrepancy - last updated from the United States Federal Reserve on December of 2025.

Data sheet and statistical analysis used to build graphs.

Discover how AI code interpreters are revolutionizing data visualization, reducing chart creation time from 20 to 5 minutes while simplifying complex statistical analysis.

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly. While it was estimated at ***** zettabytes in 2025, the forecast for 2029 stands at ***** zettabytes. Thus, global data generation will triple between 2025 and 2029. Data creation has been expanding continuously over the past decade. In 2020, the growth was higher than previously expected, caused by the increased demand due to the coronavirus (COVID-19) pandemic, as more people worked and learned from home and used home entertainment options more often.

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.

Learn how to create professional data visualizations using R and ggplot2. A step-by-step guide for startup founders and analysts to build publication-quality charts.

Sheet 1 (Raw-Data): The raw data of the study is provided, presenting the tagging results for the used measures described in the paper. For each subject, it includes multiple columns: A. a sequential student ID B an ID that defines a random group label and the notation C. the used notation: user Story or use Cases D. the case they were assigned to: IFA, Sim, or Hos E. the subject's exam grade (total points out of 100). Empty cells mean that the subject did not take the first exam F. a categorical representation of the grade L/M/H, where H is greater or equal to 80, M is between 65 included and 80 excluded, L otherwise G. the total number of classes in the student's conceptual model H. the total number of relationships in the student's conceptual model I. the total number of classes in the expert's conceptual model J. the total number of relationships in the expert's conceptual model K-O. the total number of encountered situations of alignment, wrong representation, system-oriented, omitted, missing (see tagging scheme below) P. the researchers' judgement on how well the derivation process explanation was explained by the student: well explained (a systematic mapping that can be easily reproduced), partially explained (vague indication of the mapping ), or not present.

Tagging scheme:
Aligned (AL) - A concept is represented as a class in both models, either

with the same name or using synonyms or clearly linkable names; Wrongly represented (WR) - A class in the domain expert model is incorrectly represented in the student model, either (i) via an attribute, method, or relationship rather than class, or (ii) using a generic term (e.g., user'' instead ofurban planner''); System-oriented (SO) - A class in CM-Stud that denotes a technical implementation aspect, e.g., access control. Classes that represent legacy system or the system under design (portal, simulator) are legitimate; Omitted (OM) - A class in CM-Expert that does not appear in any way in CM-Stud; Missing (MI) - A class in CM-Stud that does not appear in any way in CM-Expert.

All the calculations and information provided in the following sheets

originate from that raw data.

Sheet 2 (Descriptive-Stats): Shows a summary of statistics from the data collection,

including the number of subjects per case, per notation, per process derivation rigor category, and per exam grade category.

Sheet 3 (Size-Ratio):

The number of classes within the student model divided by the number of classes within the expert model is calculated (describing the size ratio). We provide box plots to allow a visual comparison of the shape of the distribution, its central value, and its variability for each group (by case, notation, process, and exam grade) . The primary focus in this study is on the number of classes. However, we also provided the size ratio for the number of relationships between student and expert model.

Sheet 4 (Overall):

Provides an overview of all subjects regarding the encountered situations, completeness, and correctness, respectively. Correctness is defined as the ratio of classes in a student model that is fully aligned with the classes in the corresponding expert model. It is calculated by dividing the number of aligned concepts (AL) by the sum of the number of aligned concepts (AL), omitted concepts (OM), system-oriented concepts (SO), and wrong representations (WR). Completeness on the other hand, is defined as the ratio of classes in a student model that are correctly or incorrectly represented over the number of classes in the expert model. Completeness is calculated by dividing the sum of aligned concepts (AL) and wrong representations (WR) by the sum of the number of aligned concepts (AL), wrong representations (WR) and omitted concepts (OM). The overview is complemented with general diverging stacked bar charts that illustrate correctness and completeness.

For sheet 4 as well as for the following four sheets, diverging stacked bar

charts are provided to visualize the effect of each of the independent and mediated variables. The charts are based on the relative numbers of encountered situations for each student. In addition, a "Buffer" is calculated witch solely serves the purpose of constructing the diverging stacked bar charts in Excel. Finally, at the bottom of each sheet, the significance (T-test) and effect size (Hedges' g) for both completeness and correctness are provided. Hedges' g was calculated with an online tool: https://www.psychometrica.de/effect_size.html. The independent and moderating variables can be found as follows:

Sheet 5 (By-Notation):

Model correctness and model completeness is compared by notation - UC, US.

Sheet 6 (By-Case):

Model correctness and model completeness is compared by case - SIM, HOS, IFA.

Sheet 7 (By-Process):

Model correctness and model completeness is compared by how well the derivation process is explained - well explained, partially explained, not present.

Sheet 8 (By-Grade):

Model correctness and model completeness is compared by the exam grades, converted to categorical values High, Low , and Medium.

Nominal Statistical Discrepancy for United States was 0.00000 Domestic Currency in January of 2021, according to the United States Federal Reserve. Historically, Nominal Statistical Discrepancy for United States reached a record high of 5000.00000 in July of 1951 and a record low of -100.00000 in October of 1950. Trading Economics provides the current actual value, an historical data chart and related indicators for Nominal Statistical Discrepancy for United States - last updated from the United States Federal Reserve on November of 2025.

The Statistical Atlases published by the Census Bureau in the late 1800s utilized a number of novel methods for displaying data. In this paper, we examine the use of framed spine and mosaic plots used in two plates of the Statistical Atlas of 1870. We use forensic statistics to recreate the data using available census information, and then use that data to create framed charts using modern plotting methods. We then examine the effectiveness of the framed charts compared to other alternatives with a user study. The data and code for this study are available online.

The data set has been used to generate the visual presentation using graphs and charts of the techniques for the current research trends within 6 years (from years 2013 to 2018).

Historical dataset showing UAE tourist spending by year from 1995 to 2020.

Historical dataset showing El Salvador crime rate per 100K population by year from 1994 to 2021.

Time series data for the statistic Compilation of government finance statistics and country Uruguay. Indicator Definition:Compilation of government finance statistics refers to the Government Finance Statistics Manual (GFSM) in use for compiling the data. It provides guidelines on the institutional structure of governments and the presentation of fiscal data in a format similar to business accounting with a balance sheet and income statement plus guidelines on the treatment of exchange rate and other valuation adjustments. The latest manual GFSM2014 is harmonized with the SNA2008.

NASS Data Visualization provides a dynamic web query interface supporting searches by Commodity (e.g. Cotton, Corn, Farms & Land, Grapefruit, Hogs, Oranges, Soybeans, Wheat), Statistic type (automatically refreshed based upon choice of Commodity - e.g. Inventory, Head, Acres Planted, Acres Harvested, Production, Yield) to generate chart, table, and map visualizations by year (2001-2016), as well as a link to download the resulting data in CSV format compatible for updating databases and spreadsheets. Resources in this dataset:Resource Title: NASS Data Visualization web site. File Name: Web Page, url: https://nass.usda.gov/Data_Visualization/index.php Query interface with visualization of results as charts, tables, and maps.

Source data assessment of statistical capacity (scale 0 - 100) in Nepal was reported at 80 in 2020, according to the World Bank collection of development indicators, compiled from officially recognized sources. Nepal - Source data assessment of statistical capacity (scale 0 - 100) - actual values, historical data, forecasts and projections were sourced from the World Bank on October of 2025.

Time series data for the statistic Statistical performance indicators (SPI): Pillar 3 data products score (scale 0-100) and country Fiji. Indicator Definition:The data products overall score is a composite score measureing whether the country is able to produce relevant indicators, primarily related to SDGs. The data products (internal process) pillar is segmented by four topics and organized into (i) social, (ii) economic, (iii) environmental, and (iv) institutional dimensions using the typology of the Sustainable Development Goals (SDGs). This approach anchors the national statistical system’s performance around the essential data required to support the achievement of the 2030 global goals, and enables comparisons across countries so that a global view can be generated while enabling country specific emphasis to reflect the user needs of that country.The indicator "Statistical performance indicators (SPI): Pillar 3 data products score (scale 0-100)" stands at 78.52 as of 12/31/2023, the highest value at least since 12/31/2006, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an increase of 4.99 percent compared to the value the year prior.The 1 year change in percent is 4.99.The 3 year change in percent is 13.77.The 5 year change in percent is 43.28.The 10 year change in percent is 31.74.The Serie's long term average value is 62.65. It's latest available value, on 12/31/2023, is 25.35 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2019, to it's latest available value, on 12/31/2023, is +48.34%.The Serie's change in percent from it's maximum value, on 12/31/2023, to it's latest available value, on 12/31/2023, is 0.0%.

Time series data for the statistic Compilation of government finance statistics and country Eritrea. Indicator Definition:Compilation of government finance statistics refers to the Government Finance Statistics Manual (GFSM) in use for compiling the data. It provides guidelines on the institutional structure of governments and the presentation of fiscal data in a format similar to business accounting with a balance sheet and income statement plus guidelines on the treatment of exchange rate and other valuation adjustments. The latest manual GFSM2014 is harmonized with the SNA2008.

Source data assessment of statistical capacity (scale 0 - 100) in Bolivia was reported at 60 in 2020, according to the World Bank collection of development indicators, compiled from officially recognized sources. Bolivia - Source data assessment of statistical capacity (scale 0 - 100) - actual values, historical data, forecasts and projections were sourced from the World Bank on November of 2025.