Around 20.7 percent of U.S. respondents in grades 8, 10, and 12 in 2023 stated that they used any illicit drug within that year. This statistic shows the annual prevalence of use of any illicit drug for grades 8, 10, and 12 combined from 1991 to 2023.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Simulated datasets used in the supplement with small group size of 10, log-scale noise levels and zero proportions.The settings are reflected in the title of the data:(1) small: group size = 10; (2) 0.5, 1, 1.5: the log-scale noise level(3) 0.3, 0.4, 0.5: the extra zero proportion(4) The last number is the index of replicates. There are 50 replicates for each setting
These archived live tables provide data for the historical land use change statistics which was last updated for the year 2011.
Archived guidance on this data is available.
<p class="gem-c-attachment_metadata"><span class="gem-c-attachment_attribute">MS Excel Spreadsheet</span>, <span class="gem-c-attachment_attribute">48 KB</span></p>
<p class="gem-c-attachment_metadata">This file may not be suitable for users of assistive technology.</p>
<details data-module="ga4-event-tracker" data-ga4-event='{"event_name":"select_content","type":"detail","text":"Request an accessible format.","section":"Request an accessible format.","index_section":1}' class="gem-c-details govuk-details govuk-!-margin-bottom-0" title="Request an accessible format.">
Request an accessible format.
If you use assistive technology (such as a screen reader) and need a version of this document in a more accessible format, please email <a href="mailto:alternativeformats@levellingup.gov.uk" target="_blank" class="govuk-link">alternativeformats@levellingup.gov.uk</a>. Please tell us what format you need. It will help us if you say what assistive technology you use.
<p class="gem-c-attachment_metadata"><span class="gem-c-attachment_attribute">MS Excel Spreadsheet</span>, <span class="gem-c-attachment_attribute">47.5 KB</span></p>
<p class="gem-c-attachment_metadata">This file may not be suitable for users of assistive technology.</p>
<details data-module="ga4-event-tracker" data-ga4-event='{"event_name":"select_content","type":"detail","text":"Request an accessible format.","sect
Around ** percent of U.S. respondents in grades *, **, and ** in 2023 stated that they used marijuana/hashish within ** days. This survey shows the 30-day prevalence of use of marijuana/hashish for grades *, **, and ** combined, from 1991 to 2023.
https://data.gov.tw/licensehttps://data.gov.tw/license
Compile statistical data on the top 10 trademark registration applications from the past year to the latest month for reference.
The level of catches and landings of key quota species are monitored throughout the year through a series of weekly and monthly spreadsheets.
The management of these quotas is through a system of allocation to various fishermen’s producer organisations.
<p class="gem-c-attachment_metadata"><span class="gem-c-attachment_attribute">MS Excel Spreadsheet</span>, <span class="gem-c-attachment_attribute">730 KB</span></p>
<p class="gem-c-attachment_metadata"><span class="gem-c-attachment_attribute">MS Excel Spreadsheet</span>, <span class="gem-c-attachment_attribute">466 KB</span></p>
Health indicators are quantifiable characteristics of a population which researchers use as supporting evidence for describing the health of a population. The researchers use a survey methodology to gather information about certain people, use statistics in an attempt to generalize the information collected to the entire population, then use the statistical analysis to make a statement about the health of a population. Health indicators are often used by governments to guide health care policy.
These experimental statistics contain data for the total number of people who are on Universal Credit up to 10 March 2022.
Read the background information and methodology note for guidance on these statistics, such as timeliness, uses, and procedures.
Software used for the interactive map showing claimants at Jobcentre Plus office level is no longer supported. The interactive map has therefore been withdrawn and a replacement is currently under development. Once completed and launched, the upcoming interactive tool (Examine-a-Stat) will have improved functionality, in addition to interactive maps, to better meet a wider range of user needs, and will be available in due course.
In addition to staff who are responsible for the production and quality assurance of the statistics, up to 24-hour pre-release access is provided to ministers and other officials. We publish the job titles and organisations of the people who have been granted up to 24-hour pre-release access to the latest Universal Credit statistics.
This USGS data release contains 7Q10 and 30Q10 [lowest annual 7-day and 30-day average streamflow that occurs (on average) once every 10 years] statistics at 292 USGS streamgages in or adjacent to New York State excluding Long Island. all_sites_wstats.csv - includes 7Q10 and 30Q10 values for all sites and includes information on results from the trend analysis and which sites have daily exceedance probability values available. site_regulated_7day_exc_perc#.csv and site_regulated_30day_exc_perc#.csv files include daily exceedance probability values for all altered sites that were not suitable for calculating low flow statistics. R scripts used to compile and screen streamgage datasets of daily flow, perform trend analysis, and calculate the low streamflow statistics 7Q10 and 30Q10 are included in processing_scripts.zip. Users are encouraged to read the readme file in this zipped file for details on the scripts and associated files used to generate the statistics.
Attribution-NonCommercial-NoDerivs 4.0 (CC BY-NC-ND 4.0)https://creativecommons.org/licenses/by-nc-nd/4.0/
License information was derived automatically
Facebook probably needs no introduction; nonetheless, here is a quick history of the company. The world’s biggest and most-famous social network was launched by Mark Zuckerberg while he was a...
Attribution-NonCommercial-NoDerivs 4.0 (CC BY-NC-ND 4.0)https://creativecommons.org/licenses/by-nc-nd/4.0/
License information was derived automatically
YouTube was launched in 2005. It was founded by three PayPal employees: Chad Hurley, Steve Chen, and Jawed Karim, who ran the company from an office above a small restaurant in San Mateo. The first...
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
As part of the “Biostatistics and Evidence Appraisal for Radiation Oncologists”, an online seminar series is sponsored by the University of Cincinnati Department of Radiation Oncology and ROECSG (Radiation Oncology Education Collaborative Study Group https://voices.uchicago.edu/roecsg/), Dr. Clifton fuller presented exemplar data from the following publications of prospective trial performed under the auspices of the University of Texas MD Anderson Cancer Center (Trial No, 88-001):-Peters LJ, Goepfert H, Ang KK, Byers RM, Maor MH, Guillamondegui O, Morrison WH, Weber RS, Garden AS, Frankenthaler RA, et al. Evaluation of the dose for postoperative radiation therapy of head and neck cancer: first report of a prospective randomized trial. Int J Radiat Oncol Biol Phys. 1993 Apr 30;26(1):3-11. doi: 10.1016/0360-3016(93)90167-t. PMID: 8482629.-Rosenthal DI, Mohamed ASR, Garden AS, Morrison WH, El-Naggar AK, Kamal M, Weber RS, Fuller CD, Peters LJ. Final Report of a Prospective Randomized Trial to Evaluate the Dose-Response Relationship for Postoperative Radiation Therapy and Pathologic Risk Groups in Patients With Head and Neck Cancer. Int J Radiat Oncol Biol Phys. 2017 Aug 1;98(5):1002-1011. doi: 10.1016/j.ijrobp.2017.02.218. Epub 2017 Jul 10. PMID: 28721881; PMCID: PMC5518636.Data from these publications was anonymized (I.e. stripped of 45 CFR § 164.514- defined PHI identifiers); age values were “scrambled” in random order, such that they are not associated directly with the index patient case-data. The resultant dataset is presented as a .csv file for use for training and statistical instruction purposes.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The performance of the RF model by 10-fold cross validation test.
Diagnosis data of patients and patients in hospitals.
The hospital diagnosis statistics are part of the hospital statistics and have been collected annually from all hospitals since 1993. The statistics include information on the main diagnosis (coded according to ICD-10), length of stay, department and selected sociodemographic characteristics such as age, gender and place of residence, among others.
Basic data of hospitals and preventive care or rehabilitation facilities.
The basic data statistics are part of the hospital statistics. The material and personnel resources of hospitals and preventive or rehabilitation facilities and their specialist departments have been reported annually since 1990.
The aggregated data are freely accessible.
The statistic shows the cumulative revenues from the ten leading artificial intelligence (AI) use cases worldwide, between 2016 and 2025. Over the ten years between 2016 and 2025, AI software for vehicular object detection, identification, and avoidance is expected to generate * billion U.S. dollars.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
ObjectiveTo provide a practical guidance for the analysis of N-of-1 trials by comparing four commonly used models.MethodsThe four models, paired t-test, mixed effects model of difference, mixed effects model and meta-analysis of summary data were compared using a simulation study. The assumed 3-cycles and 4-cycles N-of-1 trials were set with sample sizes of 1, 3, 5, 10, 20 and 30 respectively under normally distributed assumption. The data were generated based on variance-covariance matrix under the assumption of (i) compound symmetry structure or first-order autoregressive structure, and (ii) no carryover effect or 20% carryover effect. Type I error, power, bias (mean error), and mean square error (MSE) of effect differences between two groups were used to evaluate the performance of the four models.ResultsThe results from the 3-cycles and 4-cycles N-of-1 trials were comparable with respect to type I error, power, bias and MSE. Paired t-test yielded type I error near to the nominal level, higher power, comparable bias and small MSE, whether there was carryover effect or not. Compared with paired t-test, mixed effects model produced similar size of type I error, smaller bias, but lower power and bigger MSE. Mixed effects model of difference and meta-analysis of summary data yielded type I error far from the nominal level, low power, and large bias and MSE irrespective of the presence or absence of carryover effect.ConclusionWe recommended paired t-test to be used for normally distributed data of N-of-1 trials because of its optimal statistical performance. In the presence of carryover effects, mixed effects model could be used as an alternative.
This analysis uses location data collected on mule deer that were fitted with GPS collars in Idaho for 2003 – 2019. Individuals using a winter range (as defined as a winter herd), were used for the analysis if their location data was available at the time of the analysis. Each individual’s location dataset is used to estimate winter and summer ranges, and seasonal spring and fall migration using net-squared displacement techniques (Bunnefeld et al. 2011). Fall and spring migration locations are used for the migration route analysis. After individual mule deer spring and fall migration locations are determined, a Brownian Bridge Movement Model (BBMM, Horne et al. 2007) is used to estimate the individuals Utilized Distribution (UD) during the seasonal migrations. Depending of the frequency of the location data, either a BBMM or a Forced Motion Variance model (FMV) are used as an estimate of that season’s migration UD. If locations collected at a < 7hr schedule, the migration used BBMM modeling techniques. If the schedule was greater than 7 hrs a FMV modeling technique was used (Fatteberge et al, in review). Further, FMV techniques that allowed for a 14 hour gap in location schedule were preferred over FMV models that used a maximum of 27 hr gap. When an individual had several seasonal migrations, the resulting UDs distributions are combined and averaged to create a single UD of all the seasonal migrations conducted by that individual. Individual UDS are then combined for all individuals in the winter herd with available UD information. For migration routes, the following classes were delineated based on the area’s use across the winter herd, used by 1 individual, used by 2individuals to 10% of the winter herd, 10 to 20% use of the winter herd, and greater than 20% use by the winter herd. The combined individual UDS are aggregated to estimate winter herd stopover locations. From the combined winter herd UD, the top 10% of recorded values are selected to represent population level stopovers. Blacks Creek Migration Statistics:Analyzed/Prepared by: Jodi Berg and Scott Bergen Jan. 2020Spatial Metrics:Average length of Migration: 40 milesMaximum Migration Length: 76.3 milesMinimum Migration Length: 7.1 milesTotal Migrations Analyzed: 120Total Number of Individuals: 48Total Number Spring Migrations: 73Total Number Fall Migrations: 47Of 119 individual seasonal migrations 25 used forced motion variance (1000m) with a 14 hour time-lag, and 95 used force motion variance (1000M) with a 27 hour time-lag.Temporal Data: Extent of Study: March 15 2015 to December 17 2018Spring MigrationFall MigrationStart Date AverageApril 8October 9 Minimum February 29September 2 MaximumMay 1December 23End Date AverageMay 4November 20 MinimumMarch 25October 2 MaximumAugust 16March 5Duration Average25.3 days41.9 days Minimum7 days2 days Maximum113 days122 daysMigration Use Class Statistics:Migration Use Class:Acres 1 individual1,000,703 >2 indv – 10%487,588 Medium (10-20%)158,815 High (>20%)29,826 Stopover52,905
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Which countries spent the most and least time on social media?
Hydrologic indicator statistics were computed for 82 selected surface water sites located throughout Minnesota using daily streamflow data from the U.S. Geological Survey (USGS) National Water Information System (NWIS). The 187 hydrologic indicator statistics were computed in RStudio version 3.5.0 using the EflowStats version 5.0.0 (Mills and Blodgett, 2017) and NWCCompare version 5.0 (Blodgett, 2017). The computed hydrologic indicator statistics encompass the five components of hydrologic conditions: magnitude, frequency, duration, timing, and rate of change. Magnitude is the amount of water moving past a fixed location in a given unit of time. Frequency refers to how often streamflows above a given magnitude recur over a specified time interval. Duration is the period of time associated with a specific streamflow condition. Timing refers to the regularity with which streamflows of a given magnitude occur, and rate of change refers to how quickly the magnitude of streamflow changes (Poff and others, 1997). Site selection was based on sites previously selected in three other studies evaluating long-term streamflow records for trends (Novatny and Stefan, 2007; Peterson, Nieber, and Kanivetsky, 2011; Ziegeweid et.al, 2015). Nontrend sites were shown to not have trends in streamflow that were not related to precipitation. Hydrologic indicator statistics were computed for two periods: 1) the pre-period from 10-1-1944 through 9-30-1979 and 2) the post-period from 10-1-1980 through 9-30-2015. Exact dates of the start of trends varied among sites, but 1980 was the selected cutoff period based on an approximation of the largest cluster and on other anecdotal evidence of changes in farming practices. Both categories also had at least 10 water years with complete streamflow data. Blodgett, D., 2017, NWCCompare: Returns NWC comparison stats for two daily data sets version 5.0, https://github.com/USGS-R/NWCCompare. Mills, J., and Blodgett, D., 2017, EflowStats: Hydrologic Indicator and Alterations Stats version 5.0.0, https://github.com/USGS-R/EflowStats. Novotny, E.V., and Stefan, H.G., 2007, Stream flow in Minnesota: Indicator of climate change, Journal of Hydrology 334: 319-333. Peterson, H.M., Nieber, J.L., and Kanivetsky, R., 2011, Hydrologic regionalization to assess anthropogenic changes, Journal of Hydrology 408: 212-225. Ziegeweid, J.R., Lorenz, D.L., Sanocki, C.A., and Czuba, C.R., 2015, Methods for estimating flow-duration curve and low-flow frequency statistics for ungaged locations on small streams in Minnesota: U.S. Geological Survey Scientific Investigations Report 2015–5170, 23 p., http://dx.doi.org/10.3133/sir20155170.
In 2023, around 27 percent of U.S. respondents in grades 8, 10, and 12 stated they had used any illicit drug within their lifetime. This survey shows the lifetime prevalence of illicit drug use for grades 8, 10, and 12 combined as of 2023, by drug.
Around 20.7 percent of U.S. respondents in grades 8, 10, and 12 in 2023 stated that they used any illicit drug within that year. This statistic shows the annual prevalence of use of any illicit drug for grades 8, 10, and 12 combined from 1991 to 2023.