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.

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.

dataset and Octave/MatLab codes/scripts for data analysis Background: Methods for p-value correction are criticized for either increasing Type II error or improperly reducing Type I error. This problem is worse when dealing with thousands or even hundreds of paired comparisons between waves or images which are performed point-to-point. This text considers patterns in probability vectors resulting from multiple point-to-point comparisons between two event-related potentials (ERP) waves (mass univariate analysis) to correct p-values, where clusters of signiticant p-values may indicate true H0 rejection. New method: We used ERP data from normal subjects and other ones with attention deficit hyperactivity disorder (ADHD) under a cued forced two-choice test to study attention. The decimal logarithm of the p-vector (p') was convolved with a Gaussian window whose length was set as the shortest lag above which autocorrelation of each ERP wave may be assumed to have vanished. To verify the reliability of the present correction method, we realized Monte-Carlo simulations (MC) to (1) evaluate confidence intervals of rejected and non-rejected areas of our data, (2) to evaluate differences between corrected and uncorrected p-vectors or simulated ones in terms of distribution of significant p-values, and (3) to empirically verify rate of type-I error (comparing 10,000 pairs of mixed samples whit control and ADHD subjects). Results: the present method reduced the range of p'-values that did not show covariance with neighbors (type I and also type-II errors). The differences between simulation or raw p-vector and corrected p-vectors were, respectively, minimal and maximal for window length set by autocorrelation in p-vector convolution. Comparison with existing methods: Our method was less conservative while FDR methods rejected basically all significant p-values for Pz and O2 channels. The MC simulations, gold-standard method for error correction, presented 2.78±4.83% of difference (all 20 channels) from p-vector after correction, while difference between raw and corrected p-vector was 5,96±5.00% (p = 0.0003). Conclusion: As a cluster-based correction, the present new method seems to be biological and statistically suitable to correct p-values in mass univariate analysis of ERP waves, which adopts adaptive parameters to set correction.

App Data Statistics Tool Market Outlook

The global app data statistics tool market size was valued at approximately USD 5.3 billion in 2023 and is projected to reach USD 11.9 billion by 2032, growing at a CAGR of 9.2% during the forecast period. Several growth factors, including the escalating demand for data-driven decision-making and the rise in mobile app usage, are driving this market. As organizations increasingly recognize the value of data analytics in enhancing user engagement and optimizing app performance, the adoption of app data statistics tools is expected to surge significantly.

The growth of the app data statistics tool market is primarily fueled by the exponential increase in mobile app usage worldwide. With billions of smartphone users generating vast amounts of data daily, companies are leveraging app data statistics tools to gain actionable insights. These tools help in understanding user behavior, tracking app performance, and identifying areas for improvement. Furthermore, the growing emphasis on personalized user experiences has led to an increased demand for sophisticated analytics tools, thereby driving market growth.

Another critical growth factor is the rising importance of data-driven decision-making in various industries. Organizations across sectors such as BFSI, healthcare, retail, and media are increasingly relying on app data statistics tools to make informed decisions. These tools enable businesses to analyze large datasets, uncover trends, and optimize their strategies. The adoption of analytics tools is also propelled by the need to improve customer satisfaction and loyalty, as companies strive to offer tailored experiences to their users. The integration of artificial intelligence and machine learning in analytics tools further enhances their efficiency and accuracy, contributing to market growth.

Moreover, the market is benefitting from technological advancements and the increasing availability of advanced analytics tools. Innovations such as real-time analytics, predictive analytics, and big data analytics are enhancing the capabilities of app data statistics tools. These advancements enable organizations to gain deeper insights and make faster, more accurate decisions. Additionally, the proliferation of cloud-based solutions is making analytics tools more accessible and affordable for businesses of all sizes. Cloud deployment offers scalability, flexibility, and cost-efficiency, which are particularly attractive to small and medium enterprises (SMEs).

The role of Product Analytics Software is becoming increasingly significant in the realm of app data statistics tools. These software solutions are designed to help businesses understand how users interact with their products, providing insights that are crucial for enhancing user experience and driving product development. By analyzing user data, companies can identify trends and patterns that inform strategic decisions, such as feature enhancements and marketing strategies. The integration of Product Analytics Software with app data statistics tools enables businesses to gain a comprehensive view of user behavior, facilitating more informed decision-making and ultimately leading to improved product offerings.

Regionally, North America holds the largest market share, driven by the presence of numerous tech giants and a high adoption rate of advanced technologies. However, the Asia Pacific region is expected to witness the fastest growth during the forecast period. The rapid digitization, increasing smartphone penetration, and the rising number of app developers in countries like China and India are driving the demand for app data statistics tools. Europe also presents significant growth opportunities, with increasing investments in technology and data analytics across various industries. Latin America and the Middle East & Africa are emerging markets with growing awareness and adoption of analytics tools.

Component Analysis

The app data statistics tool market is segmented by components into software and services. Software components dominate the market, driven by the demand for sophisticated analytics solutions that can process vast amounts of data. These software tools are designed to collect, analyze, and visualize data, enabling organizations to derive meaningful insights. The growing adoption of artificial intelligence and machine learning technologies in software solutions further enhances their capabilities, making them indispensable for

International Data & Economic Analysis (IDEA) is USAID's comprehensive source of economic and social data and analysis. IDEA brings together over 12,000 data series from over 125 sources into one location for easy access by USAID and its partners through the USAID public website. The data are broken down by countries, years and the following sectors: Economy, Country Ratings and Rankings, Trade, Development Assistance, Education, Health, Population, and Natural Resources. IDEA regularly updates the database as new data become available. Examples of IDEA sources include the Demographic and Health Surveys, STATcompiler; UN Food and Agriculture Organization, Food Price Index; IMF, Direction of Trade Statistics; Millennium Challenge Corporation; and World Bank, World Development Indicators. The database can be queried by navigating to the site displayed in the Home Page field below.

Unsupervised exploratory data analysis (EDA) is often the first step in understanding complex data sets. While summary statistics are among the most efficient and convenient tools for exploring and describing sets of data, they are often overlooked in EDA. In this paper, we show multiple case studies that compare the performance, including clustering, of a series of summary statistics in EDA. The summary statistics considered here are pattern recognition entropy (PRE), the mean, standard deviation (STD), 1-norm, range, sum of squares (SSQ), and X4, which are compared with principal component analysis (PCA), multivariate curve resolution (MCR), and/or cluster analysis. PRE and the other summary statistics are direct methods for analyzing datathey are not factor-based approaches. To quantify the performance of summary statistics, we use the concept of the “critical pair,” which is employed in chromatography. The data analyzed here come from different analytical methods. Hyperspectral images, including one of a biological material, are also analyzed. In general, PRE outperforms the other summary statistics, especially in image analysis, although a suite of summary statistics is useful in exploring complex data sets. While PRE results were generally comparable to those from PCA and MCR, PRE is easier to apply. For example, there is no need to determine the number of factors that describe a data set. Finally, we introduce the concept of divided spectrum-PRE (DS-PRE) as a new EDA method. DS-PRE increases the discrimination power of PRE. We also show that DS-PRE can be used to provide the inputs for the k-nearest neighbor (kNN) algorithm. We recommend PRE and DS-PRE as rapid new tools for unsupervised EDA.

Exploratory Data Analysis (EDA) Tools play a pivotal role in the modern data-driven landscape, transforming raw data into actionable insights. As businesses increasingly recognize the value of data in informing decisions, the market for EDA tools has witnessed substantial growth, driven by the rapid expansion of dat

About this dataset

Context

The dataset tabulates the Gate household income by age. The dataset can be utilized to understand the age-based income distribution of Gate income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Gate, OK annual median income by age groups dataset (in 2022 inflation-adjusted dollars)
• Age-wise distribution of Gate, OK household incomes: Comparative analysis across 16 income brackets

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Gate income distribution by age. You can refer the same here

Spatial analysis and statistical summaries of the Protected Areas Database of the United States (PAD-US) provide land managers and decision makers with a general assessment of management intent for biodiversity protection, natural resource management, and recreation access across the nation. The PAD-US 3.0 Combined Fee, Designation, Easement feature class (with Military Lands and Tribal Areas from the Proclamation and Other Planning Boundaries feature class) was modified to remove overlaps, avoiding overestimation in protected area statistics and to support user needs. A Python scripted process ("PADUS3_0_CreateVectorAnalysisFileScript.zip") associated with this data release prioritized overlapping designations (e.g. Wilderness within a National Forest) based upon their relative biodiversity conservation status (e.g. GAP Status Code 1 over 2), public access values (in the order of Closed, Restricted, Open, Unknown), and geodatabase load order (records are deliberately organized in the PAD-US full inventory with fee owned lands loaded before overlapping management designations, and easements). The Vector Analysis File ("PADUS3_0VectorAnalysisFile_ClipCensus.zip") associated item of PAD-US 3.0 Spatial Analysis and Statistics ( https://doi.org/10.5066/P9KLBB5D ) was clipped to the Census state boundary file to define the extent and serve as a common denominator for statistical summaries. Boundaries of interest to stakeholders (State, Department of the Interior Region, Congressional District, County, EcoRegions I-IV, Urban Areas, Landscape Conservation Cooperative) were incorporated into separate geodatabase feature classes to support various data summaries ("PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.zip") and Comma-separated Value (CSV) tables ("PADUS3_0SummaryStatistics_TabularData_CSV.zip") summarizing "PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.zip" are provided as an alternative format and enable users to explore and download summary statistics of interest (Comma-separated Table [CSV], Microsoft Excel Workbook [.XLSX], Portable Document Format [.PDF] Report) from the PAD-US Lands and Inland Water Statistics Dashboard ( https://www.usgs.gov/programs/gap-analysis-project/science/pad-us-statistics ). In addition, a "flattened" version of the PAD-US 3.0 combined file without other extent boundaries ("PADUS3_0VectorAnalysisFile_ClipCensus.zip") allow for other applications that require a representation of overall protection status without overlapping designation boundaries. The "PADUS3_0VectorAnalysis_State_Clip_CENSUS2020" feature class ("PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.gdb") is the source of the PAD-US 3.0 raster files (associated item of PAD-US 3.0 Spatial Analysis and Statistics, https://doi.org/10.5066/P9KLBB5D ). Note, the PAD-US inventory is now considered functionally complete with the vast majority of land protection types represented in some manner, while work continues to maintain updates and improve data quality (see inventory completeness estimates at: http://www.protectedlands.net/data-stewards/ ). In addition, changes in protected area status between versions of the PAD-US may be attributed to improving the completeness and accuracy of the spatial data more than actual management actions or new acquisitions. USGS provides no legal warranty for the use of this data. While PAD-US is the official aggregation of protected areas ( https://www.fgdc.gov/ngda-reports/NGDA_Datasets.html ), agencies are the best source of their lands data.

Multienvironment trials (METs) enable the evaluation of the same genotypes under a v ariety of environments and management conditions. We present META (Multi Environment Trial Analysis), a suite of 31 SAS programs that analyze METs with complete or incomplete block designs, with or without adjustment by a covariate. The entire program is run through a graphical user interface. The program can produce boxplots or histograms for all traits, as well as univariate statistics. It also calculates best linear unbiased estimators (BLUEs) and best linear unbiased predictors for the main response variable and BLUEs for all other traits. For all traits, it calculates variance components by restricted maximum likelihood, least significant difference, coefficient of variation, and broad-sense heritability using PROC MIXED. The program can analyze each location separately, combine the analysis by management conditions, or combine all locations. The flexibility and simplicity of use of this program makes it a valuable tool for analyzing METs in breeding and agronomy. The META program can be used by any researcher who knows only a few fundamental principles of SAS.

Analysis of ‘2019 NYC Open Data Plan: FOIL Summary Statistics’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/51923e05-70be-4a4c-acb6-8094a9d79e2e on 26 January 2022.

--- Dataset description provided by original source is as follows ---

Local Law 7 of 2016 requires agencies to “review responses to freedom of information law [FOIL] requests that include the release of data to determine if such responses consist of or include public data sets that have not yet been included on the single web portal or the inclusion” on the Open Data Portal. Additionally, each City agency shall disclose “the total number, since the last update, of such agency’s freedom of information law responses that included the release of data, the total number of such responses determined to consist of or include a public data set that had not yet been included on the single web portal and the name of such public data set, where applicable, and the total number of such responses that resulted in voluntarily disclosed information being made accessible through the single web portal.”

--- Original source retains full ownership of the source dataset ---

Statistical mirroring-based ordinalysis (SM-based ordinalysis) measures the proximity or deviation of an individual's composite set of ordinal assessment scores from the highest positive ordinal scale point [3]. Within the framework of Kabirian-based optinalysis [1] and statistical mirroring [2], Statistical mirroring-based ordinalysis is conceptualized as the isoreflectivity (isoreflective pairing) of the composite set of ordinal assessment scores of an individual to the highest positive ordinal scale point of an established ordinal assessment scale, under customized and optimized choice of parameters. This represents the underlying assumption of statistical mirroring-based ordinalysis.

The process of Statistical mirroring-based ordinalysis comprises three distinct phases: a) Adaptive customization and optimization phase [3]: This phase represents the core of the methodology. This involves the adaptive customization and optimization of parameters to suit the requirements for statistical mirroring estimation in the given task [3]. b) Statistical mirroring computation phase [2]: This involves applying the adopted statistical mirroring type based on the phase 1 adaption. c) Optinalytic model calculation phase [1]: This phase is focused on computing estimates (such as the Kabirian coefficient of proximity, the probability of proximity, and the deviation) based on Kabirian-based isomorphic optinalysis models.

References: [1] K.B. Abdullahi, Kabirian-based optinalysis: A conceptually grounded framework for symmetry/asymmetry, similarity/dissimilarity, and identity/unidentity estimations in mathematical structures and biological sequences, MethodsX 11 (2023) 102400. https://doi.org/10.1016/j.mex.2023.102400 [2] K.B. Abdullahi, Statistical mirroring: A robust method for statistical dispersion estimation, MethodsX 12 (2024) 102682. https://doi.org/10.1016/j.mex.2024.102682 [3] K.B. Abdullahi, Statistical mirroring-based ordinalysis: A sensitive, robust, efficient, and ordinality-preserving descriptive method for analyzing ordinal assessment data, MethodsX 14 (2024) 103427. https://doi.org/10.1016/j.mex.2025.103427

Local association analysis, such as local similarity analysis and local shape analysis, of biological time series data helps elucidate the varying dynamics of biological systems. However, their applications to large scale high-throughput data are limited by slow permutation procedures for statistical significance evaluation. We developed a theoretical approach to approximate the statistical significance of local similarity and local shape analysis based on the approximate tail distribution of the maximum partial sum of independent identically distributed (i.i.d) and Markovian random variables. Simulations show that the derived formula approximates the tail distribution reasonably well (starting at time points > 10 with no delay and > 20 with delay) and provides p-values comparable to those from permutations. The new approach enables efficient calculation of statistical significance for pairwise local association analysis, making possible all-to-all association studies otherwise prohibitive. As a demonstration, local association analysis of human microbiome time series shows that core OTUs are highly synergetic and some of the associations are body-site specific across samples. The new approach is implemented in our eLSA package, which now provides pipelines for faster local similarity and shape analysis of time series data. The tool is freely available from eLSA's website: http://meta.usc.edu/softs/lsa.

The Next-Generation Sequencing (NGS) Informatics market has rapidly evolved over the past decade, becoming an integral component in genomics research, personalized medicine, and various biomedical applications. This market encompasses software and analytics tools that handle the vast data generated from NGS technolo

The Excel file contains:

Point-by-point data of singles matches at Wimbledon 1992-1995: 256 men's matches with 59,466 points, and 223 women's matches with 29,417 points; Match-level data of the same matches; Point-by-point data of three famous recent matches: Federer-Nadal, Clijsters-Williams, and Djokovic-Nadal.

Analysis of ‘Access statistics by moers.de for February 2013 ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/64d5c231-da44-4ac0-8184-322c28c7a16a on 16 January 2022.

--- Dataset description provided by original source is as follows ---

The zip file contains the following CSV files:

• Visitors per year_1.csv
• Visitors per month — individual period _1.csv
• Visitors per month 1.csv
• Visitors per Stunde_1.csv
• Visitors per day 1.csv
• Visitors _1.csv
• Main browser versions by operating system_1.csv
• browsers by operating system subversionen_1.csv
• Browser Unterversionen_1.csv
• boarding sides_1.csv
• Origin overview _1.csv
• Use per domain 1.csv
• Page Impressions per Page_1.csv
• Search word statistics Phrases by search engine 1.csv
• Search word statistics Phrasen_1.csv
• Search word statistics Wörter_1.csv

--- Original source retains full ownership of the source dataset ---

Analysis of ‘Local Area Unemployment Statistics’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/452841df-b9bd-4e65-abda-ffad4f0bc242 on 13 February 2022.

--- Dataset description provided by original source is as follows ---

The Local Area Unemployment Statistics (LAUS) program is a federal-state cooperative effort which produces monthly estimates of produces monthly and annual employment, unemployment, and labor force data for approximately 7,000 areas including Census regions and divisions, States, counties, metropolitan areas, and many cities.

This dataset includes data for all 50 states, the District of Columbia, and Puerto Rico. To only see data for Connecticut, create a filter where "State name" is equal to "Connecticut".

For more information on the LAUS program and data visit: https://www.bls.gov/lau/

For more information from the CT Department of Labor visit: https://www1.ctdol.state.ct.us/lmi/LAUS/default.asp

--- Original source retains full ownership of the source dataset ---

Spatial analysis and statistical summaries of the Protected Areas Database of the United States (PAD-US) provide land managers and decision makers with a general assessment of management intent for biodiversity protection, natural resource management, and recreation access across the nation. This data release presents results from statistical summaries of the PAD-US 2.1 protection status for various land unit boundaries (Protected Areas Database of the United States (PAD-US) Summary Statistics by GAP Status Code) as well as summaries of public access status (Public Access Statistics), provided in Microsoft Excel readable workbooks, the vector GIS analysis files and scripts used to complete the summaries, and raster GIS analysis files for combination with other raster data. The PAD-US 2.1 Combined Fee, Designation, Easement feature class in the full inventory (with Military Lands and Tribal Areas from the Proclamation and Other Planning Boundaries feature class) was modified to prioritize and remove overlapping management designations, limiting overestimation in protection status or public access statistics and to support user needs for vector and raster analysis data. Analysis files were clipped to the Census State boundary file to define the extent and fill in areas (largely private land) outside the PAD-US, providing a common denominator for statistical summaries.