In 2019, ** percent of the physicians and ** percent of students and residents surveyed in the U.S. said that patient data would be valuable to them clinically if it was sourced from a wearable device. Furthermore, ** percent of physicians and ** percent of students and residents said they would give clinical importance to patients self reported data if it was from a health app.

This statistic shows the importance of big data analysis and machine learning technologies worldwide as of 2019. Tensorflow was seen as the most important big data analytics and machine learning technology, with ** percent of respondents stating that it was important to critial for their organization.

This statistic shows the importance of big data search technologies in organizations worldwide as of 2019. Around ** percent of respondents stated that Elasticsearch was critical or very important for their organization as of 2019.

In this session, several examples of misinterpreting data are noted highlighting the importance of the data user having and reading complete and clear metadata. Several useful links and publications are highlighted describing Statistics Canada data and using Statistics Canada data that you should know.

This dataset gathers the most crucial SEO statistics for the year, providing an overview of the dominant trends and best practices in the field of search engine optimization. Aimed at digital marketing professionals, site owners, and SEO analysts, this collection of information serves as a guide to navigate the evolving SEO landscape with confidence and accuracy. Mode of Data Production: The statistics have been carefully selected and compiled from a variety of credible and recognized sources in the SEO industry, including research reports, web traffic data analytics, and consumer and marketing professional surveys. Each statistic was checked for reliability and relevance to current trends. Categories Included: User search behaviour: Statistics on the evolution of search modes, including voice and mobile search. Mobile Optimisation: Data on the importance of site optimization for mobile devices. Importance of Backlinks: Insights on the role of backlinks in SEO ranking and the need to prioritize quality. Content quality: Statistics highlighting the importance of relevant and engaging content for SEO. Search engine algorithms: Information on the impact of algorithm updates on SEO strategies. Usefulness of the Data: This dataset is designed to help users quickly understand current SEO dynamics and apply that knowledge in optimizing their digital marketing strategies. It provides a solid foundation for benchmarking, strategic planning, and informed decision-making in the field of SEO. Update and Accessibility: To ensure relevance and timeliness, the dataset will be regularly updated with new information and emerging trends in the SEO world.

This file is the data set form the famous publication Francis J. Anscombe "*Graphs in Statistical Analysis*", The American Statistician 27 pp. 17-21 (1973) (doi: 10.1080/00031305.1973.10478966). It consists of four data sets of 11 points each. Note the peculiarity that the same 'x' values are used for the first three data sets, and I have followed this exactly as in the original publication (originally done to save space), i.e. the first column (x123) serves as the 'x' for the next three 'y' columns; y1, y2 and y3.

In the dataset Anscombe_quintet_data.csv there is a new column (y5) as an example of Simpson's paradox (C. McBride Ellis "*Anscombe dataset No. 5: Simpson's paradox*", Zenodo doi: 10.5281/zenodo.15209087 (2025)

The environmental conditions data of Kaohsiung City includes five major categories of data including air, waste, environmental sanitation and toxic substance management, public complaints about pollution, and other related statistics.

Residential renovation price indexes (RRPI) by project group and individual project type. The table presents the relative importance of each project's contribution to the index total.

Provide important statistical tables of domestic travel indicators for nationals over the years.

During a survey carried out among decision-makers in charge of customer engagement/retention strategy from 20 countries worldwide, ** percent of respondents stated that they thought it was important or critical to collect customer channel engagement data; ************* named real-time experience in this context.

Recognizing the importance of transportation and the importance of objective statistics for transportation decision-making, Congress requires the Director of the Bureau of Transportation Statistics (BTS) of the U.S. Department of Transportation (USDOT) to provide the Transportation Statistics Annual Report (TSAR) each year to Congress and the President.1 BTS published the first TSAR in 1994. This 30th TSAR edition documents the conduct of the duties of BTS as called out in the statute.Source: https://rosap.ntl.bts.gov/view/dot/79039The Transportation Statistics Annual Report (TSAR) describes the Nation’s transportation system, the system’s performance, its contributions to the economy, and its effects on people and the environment. This report is based on information collected or compiled by the Bureau of Transportation Statistics (BTS), a principle Federal statistical agency at the U.S. Department of Transportation.Source: https://www.bts.gov/product/transportation-statistics-annual-reportThis upload contains xlsx files supporting the 2023 (https://rosap.ntl.bts.gov/view/dot/72943) and 2024 (https://rosap.ntl.bts.gov/view/dot/79039) TSARs.The two readme files were created for this upload and were not produced by the BTS.

Taiwan Economic Debates is published four times a year, with economic statistics included to select important and representative macroeconomic statistics items so that readers can have an overall understanding of Taiwan's current economic development. (Starting from the autumn issue of 107, the "Economic Statistics" section is canceled and the data will no longer be updated.)

Important statistics, fiscal revenues and expenditures, human resources, unemployment rate, number of employed population, industrial structure of employed individuals, household income and expenditure, consumer price index, land, population, social security, public safety, social welfare and assistance, education and culture, medical care, health, transportation, public construction, industrial and commercial conditions, environmental protection.

Note: This is a large dataset. To download, go to ArcGIS Open Data Set and click the download button, and under additional resources select the shapefile or geodatabase option. America's private forests provide a vast array of public goods and services, including abundant, clean surface water. Forest loss and development can affect water quality and quantity when forests are removed and impervious surfaces, such as paved roads, spread across the landscape. We rank watersheds across the conterminous United States according to the contributions of private forest land to surface drinking water and by threats to surface water from increased housing density. Private forest land contributions to drinking water are greatest in the East but are also important in Western watersheds. Development pressures on these contributions are concentrated in the Eastern United States but are also found in the North-Central region, parts of the West and Southwest, and the Pacific Northwest; nationwide, more than 55 million acres of rural private forest land are projected to experience a substantial increase in housing density from 2000 to 2030. Planners, communities, and private landowners can use a range of strategies to maintain freshwater ecosystems, including designing housing and roads to minimize impacts on water quality, managing home sites to protect water resources, and using payment schemes and management partnerships to invest in forest stewardship on public and private lands.This data is based on the digital hydrologic unit boundary layer to the Subwatershed (12-digit) 6th level for the continental United States. To focus this analysis on watersheds with private forests, only watersheds with at least 10% forested land and more than 50 acres of private forest were analyzed. All other watersheds were labeled ?Insufficient private forest for this analysis"and coded -99999 in the data table. This dataset updates forest and development statistics reported in the the 2011 Forests to Faucet analysis using 2006 National Land Cover Database for the Conterminous United States, Grid Values=41,42,43,95. and Theobald, Dr. David M. 10 March 2008. bhc2000 and bhc2030 (Housing density for the coterminous US in 2000 and 2030, respectively.) Field Descriptions:HUC_12: Twelve Digit Hydrologic Unit Code: This field provides a unique 12-digit code for each subwatershed.HU_12_DS: Sixth Level Downstream Hydrologic Unit Code: This field was populated with the 12-digit code of the 6th level hydrologic unit that is receiving the majority of the flow from the subwatershed.IMP1: Index of surface drinking water importance (Appendix Map). This field is from the 2011 Forests to Faucet analysis and has not been updated for this analysis.HDCHG_AC: Acres of housing density change on private forest in the subwatershed. HDCHG_PER: Percent of the watershed to experience housing density change on private forest. IMP_HD_PFOR: Index Private Forest importance to Surface Drinking Water with Development Pressure - identifies private forested areas important for surface drinking water that are likely to be affected by future increases in housing density, Ptle_IMP_HD: Private Forest importance to Surface Drinking Water with Development Pressure (Figure 7), percentile. Ptle_HDCHG: Percentage of each subwatershed to Experience an increase in House Density in Private Forest (Figure 6), percentile. FOR_AC: Acres forest (2006) in the subwatershed. PFOR_AC: Acres private forest (2006) in the subwatershed. PFOR_PER: Percent of the subwatershed that is private forest. HU12_AC: Acreage of the subwatershedFOR_PER: Percent of the subwatershed that is forest. PFOR_IMP: Index of Private Forest Importance to Surface Drinking Water. .Ptle_PFIMP: Private forest importance to surface drinking water(Figure 4), percentile. TOP100: Top 100 subwatersheds. 50 from the East, 50 from the west (using the Mississippi River as the divide.) Metadata

Additional file 1 R code to produce all analyses described in this paper.

Wholesale services price index (WSPI), relative importance by North American Industry Classification System (NAICS).

Two treatments (A and B) were applied in two groups (1 and 2) of patients. Treatment A seems to be more successful in each of the groups viewed separately (100 > 87.5 and 66.7 > 50). However, evaluated for the combined group of patients, treatment B appears to be more successful (75 < 80).

Important document scanning statistics............

This project aimed to understand the public acceptability of a Low Emission Zone in the city of Bath, UK (formally known as the 'Clean Air Zone'). The dataset consists of socio-demographic, travel-related, and psychological variables, and a measure of Low Emission Zone acceptability.