Specialized collection of 0 free growth charts SVG illustrations from the business & finance category. Business growth illustrations featuring line graphs, pie charts, and data visualization elements Examples include: line graph, pie chart.

To create the dataset, the top 10 countries leading in the incidence of COVID-19 in the world were selected as of October 22, 2020 (on the eve of the second full of pandemics), which are presented in the Global 500 ranking for 2020: USA, India, Brazil, Russia, Spain, France and Mexico. For each of these countries, no more than 10 of the largest transnational corporations included in the Global 500 rating for 2020 and 2019 were selected separately. The arithmetic averages were calculated and the change (increase) in indicators such as profitability and profitability of enterprises, their ranking position (competitiveness), asset value and number of employees. The arithmetic mean values of these indicators for all countries of the sample were found, characterizing the situation in international entrepreneurship as a whole in the context of the COVID-19 crisis in 2020 on the eve of the second wave of the pandemic. The data is collected in a general Microsoft Excel table. Dataset is a unique database that combines COVID-19 statistics and entrepreneurship statistics. The dataset is flexible data that can be supplemented with data from other countries and newer statistics on the COVID-19 pandemic. Due to the fact that the data in the dataset are not ready-made numbers, but formulas, when adding and / or changing the values in the original table at the beginning of the dataset, most of the subsequent tables will be automatically recalculated and the graphs will be updated. This allows the dataset to be used not just as an array of data, but as an analytical tool for automating scientific research on the impact of the COVID-19 pandemic and crisis on international entrepreneurship. The dataset includes not only tabular data, but also charts that provide data visualization. The dataset contains not only actual, but also forecast data on morbidity and mortality from COVID-19 for the period of the second wave of the pandemic in 2020. The forecasts are presented in the form of a normal distribution of predicted values and the probability of their occurrence in practice. This allows for a broad scenario analysis of the impact of the COVID-19 pandemic and crisis on international entrepreneurship, substituting various predicted morbidity and mortality rates in risk assessment tables and obtaining automatically calculated consequences (changes) on the characteristics of international entrepreneurship. It is also possible to substitute the actual values identified in the process and following the results of the second wave of the pandemic to check the reliability of pre-made forecasts and conduct a plan-fact analysis. The dataset contains not only the numerical values of the initial and predicted values of the set of studied indicators, but also their qualitative interpretation, reflecting the presence and level of risks of a pandemic and COVID-19 crisis for international entrepreneurship.

Specialized collection of 0 free data visualization SVG illustrations from the technology & electronics category. Data visualization illustrations including bar charts, network graphs, and information graphics Examples include: bar chart, network graph.

Data for a typical pharmaceutical company transformed into useful charts and tables. Examples include PowerPivot charts and pivot table charts of various types. All data is created and owned by author.

Stock Charts

This dataset is a collection of a sample of images from tweets that I scraped using my Discord bot that keeps track of financial influencers on Twitter. The data consists of images that were part of tweets that mentioned a stock. This dataset can be used for a wide variety of tasks, such as image classification or feature extraction.

  FinTwit Charts Collection

This dataset is part of a larger collection of datasets, scraped from Twitter and labeled by a… See the full description on the dataset page: https://huggingface.co/datasets/StephanAkkerman/stock-charts.

Previous researches support that graphs are relevant decision aids to tasks related to the interpretation of numerical information. Moreover, literature shows that different types of graphical information can help or harm the accuracy on decision making of accountants and financial analysts. We conducted a 4×2 mixed-design experiment to examine the effects of numerical information disclosure on financial analysts’ accuracy, and investigated the role of overconfidence in decision making. Results show that compared to text, column graph enhanced accuracy on decision making, followed by line graphs. No difference was found between table and textual disclosure. Overconfidence harmed accuracy, and both genders behaved overconfidently. Additionally, the type of disclosure (text, table, line graph and column graph) did not affect the overconfidence of individuals, providing evidence that overconfidence is a personal trait. This study makes three contributions. First, it provides evidence from a larger sample size (295) of financial analysts instead of a smaller sample size of students that graphs are relevant decision aids to tasks related to the interpretation of numerical information. Second, it uses the text as a baseline comparison to test how different ways of information disclosure (line and column graphs, and tables) can enhance understandability of information. Third, it brings an internal factor to this process: overconfidence, a personal trait that harms the decision-making process of individuals. At the end of this paper several research paths are highlighted to further study the effect of internal factors (personal traits) on financial analysts’ accuracy on decision making regarding numerical information presented in a graphical form. In addition, we offer suggestions concerning some practical implications for professional accountants, auditors, financial analysts and standard setters.

The data represents and example output for environmental data (i.e. climate and pollution) linked with individual events through location and time. The linkage is the result of a semantic query that integrates environmental data within an area relevant to the event and selects a period of data before the event. The resulting event-environmental linked data contains: The data for analysis as a data table (.csv) and graph (.ttl) The metadata describing the linkage process and the data (.csv and .ttl) The interactive report to explore the (meta)data (.html) The graph files are ready to be shared and published as Findable, Accessible, Interoperable and Reusable (FAIR) data, including the necessary information to be reused by other researchers in different contexts.

This Excel table contains a detailed example of a graph-theoretic model used in the specification of the physical topology and network of the modeled system.

The data set is a collection of environmental records associated with the individual events. The data set has been generated using the serdif-api wrapper (https://github.com/navarral/serdif-api) when sending a CSV file with example events for the Republic of Ireland. The serdif-api send a semantic query that (i) selects the environmental data sets within the region of the event, (ii) filters by the specific period of interest from the event, (iii) aggregates the data sets using the minimum, maximum, average or sum for each of the available variables for a specific time unit. The aggregation method and the time unit can be passed to the serdif-api through the Command Line Interface (CLI) (see example in https://github.com/navarral/serdif-api). The resulting data set format can be also specified as data table (CSV) or as graph (RDF) for analysis and publication as FAIR data. The open-ready data for research is retrieved as a zip file that contains: (i) data as csv: environmental data associated to particular events as a data table (ii) data as rdf: environmental data associated to particular events as a graph (iii) metadata for publication as rdf: metadata record with generalized information about the data that do not contain personal data as a graph; therefore, publishable. (iv) metadata for research as rdf: metadata records with detailed information about the data, such as individual dates, regions, data sets used and data lineage; which could lead to data privacy issues if published without approval from the Data Protection Officer (DPO) and data controller.

Four tables and 23 figures of this paper. Table 1 shows the concept space comparison of existing taxonomies. Table 2 presents Hearst pattern examples. Table 3 shows labeling guideline for conceptualization. Table 4 presents precision of short text understanding. Figure 1 shows the framework overviews. Figure 2 is local taxonomy construction. Figure 3 shows horizontal merging. Figure 4 shows vertical merging: single sense alignment. Figure 5 shows vertical merging: multiple sense alignment. Figure 6 is a subgraph of heterogeneous semantic network around watch. Figure 7 is the compression procedure of typed-term co-occurrence network. Figure 8 presents an example of short text understanding. Figure 9 present examples of Chain model and Pairwise model. Figure 10 is a snapshot of the Probase browser. Figure 11 is a snapshot of single instance conceptualization.Figure 12 is a snapshot of context-aware single instance conceptualization. Figure 13 shows an example of short text conceptualization. Figure 14 is the framework of topic search. Figure 15 is a snapshot of the Web tables. Figure 16 shows query recommendation snapshot. Figure 17 shows the correlation of CTR with ads relevance score. Figure 18 presents the distribution of concepts in Microsoft Concept Graph. Figure 19 shows concept coverage of different taxonomies. Figure 20 shows precision of extracted isA pairs on 40 concepts.Figure 21 is precision of isA pairs after each iteration. Figure 22 shows the number of discovered concepts and isA pairs after each iteration. Figure 23 shows precision and nDCG comparison.

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.

This article discusses how to make statistical graphics a more prominent element of the undergraduate statistics curricula. The focus is on several different types of assignments that exemplify how to incorporate graphics into a course in a pedagogically meaningful way. These assignments include having students deconstruct and reconstruct plots, copy masterful graphs, create one-minute visual revelations, convert tables into “pictures,” and develop interactive visualizations, for example, with the virtual earth as a plotting canvas. In addition to describing the goals and details of each assignment, we also discuss the broader topic of graphics and key concepts that we think warrant inclusion in the statistics curricula. We advocate that more attention needs to be paid to this fundamental field of statistics at all levels, from introductory undergraduate through graduate level courses. With the rapid rise of tools to visualize data, for example, Google trends, GapMinder, ManyEyes, and Tableau, and the increased use of graphics in the media, understanding the principles of good statistical graphics, and having the ability to create informative visualizations is an ever more important aspect of statistics education. Supplementary materials containing code and data for the assignments are available online.

This is my one of datasets, that i have been working on it everyday. This one continues cleaning, pivot tables, and some charts for visualaization

A coffee order sales dataset on Kaggle typically contains transactional data from a coffee shop, including information about the products sold, sales patterns, and customer preferences. Here’s a breakdown of the typical columns and their descriptions you might find in such a dataset:

Example Columns in a Coffee Sales Dataset: Order ID: Unique identifier for each transaction or order. Description: Each time a customer makes a purchase, it gets recorded with a unique ID. Date and Time: Timestamp of when the order was placed. Description: Captures the exact date and time of the transaction. This can be useful for analyzing peak sales times, seasonal trends, or customer traffic during specific hours. Customer ID: Identifier for each customer. Description: Allows tracking of individual customers and their buying habits over time. Can be used for loyalty analysis or customer segmentation. Product Name: The name of the coffee or item sold. Description: Lists what products are being ordered, such as "Latte," "Cappuccino," "Espresso," or "Americano." Category: Category of the product (e.g., Coffee, Snacks, Pastries). Description: Differentiates between coffee types and other items like food or merchandise. Quantity: Number of units sold in each transaction. Description: Helps calculate total sales and identify popular items that are often purchased in bulk. Price: The price of each item ordered. Description: The cost of the item sold, useful for analyzing revenue and product pricing effectiveness. Total Sale: The total amount

This interactive tool allows users to generate tables and graphs on information relating to pregnancy and childbirth. All data comes from the CDC's PRAMS. Topics include: breastfeeding, prenatal care, insurance coverage and alcohol use during pregnancy. Background CPONDER is the interaction online data tool for the Center's for Disease Control and Prevention (CDC)'s Pregnancy Risk Assessment Monitoring System (PRAMS). PRAMS gathers state and national level data on a variety of topics related to pregnancy and childbirth. Examples of information include: breastfeeding, alcohol use, multivitamin use, prenatal care, and contraception. User Functionality Users select choices from three drop down menus to search for d ata. The menus are state, year and topic. Users can then select the specific question from PRAMS they are interested in, and the data table or graph will appear. Users can then compare that question to another state or to another year to generate a new data table or graph. Data Notes The data source for CPONDER is PRAMS. The data is from every year between 2000 and 2008, and data is available at the state and national level. However, states must have participated in PRAMS to be part of CPONDER. Not every state, and not every year for every state, is available.

Summarize big data with pivot table and charts and slicers

Source: Kaggle users Data analysis: Student's placement scores with their years to join the club. The file includes some worksheets such as raw data, summary data, pivot table and a chart, SMART questions and SOW. In the chart: You can see the connection between student's placement scores and their time to join the club. The table involves 5 columns, and then it was added more two columns (Year and Time_Join_Club) so that you can calculate the number of years in which the students have joined the club.

Math_Score| Reading_Score| Writing_Score |Placement_Score |Club_Join_Date|Year | Time _Join_Club|

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2021 American Community Survey 1-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The age dependency ratio is derived by dividing the combined under-18 and 65-and-over populations by the 18-to-64 population and multiplying by 100..The old-age dependency ratio is derived by dividing the population 65 and over by the 18-to-64 population and multiplying by 100..The child dependency ratio is derived by dividing the population under 18 by the 18-to-64 population and multiplying by 100..When information is missing or inconsistent, the Census Bureau logically assigns an acceptable value using the response to a related question or questions. If a logical assignment is not possible, data are filled using a statistical process called allocation, which uses a similar individual or household to provide a donor value. The "Allocated" section is the number of respondents who received an allocated value for a particular subject..The 2021 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineations due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Presented are propensity score mean-squared-error and standardised ration of propensity score mean-squared error.

The microbiome represents a complex community of trillions of microorganisms residing in various body parts and plays critical roles in maintaining host health and wellbeing. Understanding the interactions between microbiota and their host offers valuable insights into potential strategies for promoting health, including microbiome-targeted interventions. We have created MicrobiomeKG, a knowledge graph for microbiome research, that bridges various taxa and microbial pathways with host health. This novel knowledge graph derives algorithmically generated knowledge assertions from the supplementary tables that support published microbiome papers. By identifying knowledge assertions from supplementary tables and expressing them as knowledge graphs, we are casting this valuable content into a format that is ideal for hypothesis generation. To address the high heterogeneity of study contexts, methodologies, and reporting standards, we leveraged neural networks to implement a standardized edge scoring system, which we use to perform centrality analyses. We present three example use cases: linking helminth infections with non-alcoholic fatty-liver disease via microbial taxa, exploring connections between the Alistipes genus and inflammation, and identifying the Bifidobacterium genus as the most central connection with attention deficit hyperactivity disorder. MicrobiomeKG is deployed for integrative analysis and hypothesis generation, both programmatically and via the Biomedical Data Translator ecosystem. By bridging data gaps and facilitating the discovery of new biological relationships, MicrobiomeKG will help advance personalized medicine through a deeper understanding of the microbial contributions to human health and disease mechanisms.