Note: n: The sample sizes for cognition, demand, and use. N/A: not applicable. (1). Descriptive statistics, (2). t-test, (3). ANOVA, (4). Chi-squared test, (5). Nonparametric test, (6). Correlation and regression, (7). Statistical graphs and tables, (8). Statistical design, (9). Multiple ANOVA, (10). Analysis of covariance, (11). Multiple linear regression, (12). Logistic regression, (13). Survival analysis, (14). Discriminant analysis, (15). Clustering analysis, (16). Principal components analysis and Factor analysis (PCA & FA),(17). SPSS, (18). SAS, (19). Overall cognition of and demand for medical statistics, (20). Overall cognition of and demand for software.Basic Demographic Characteristics of the Included Studies.

The analysis uses SAS Version 9.4 to compute descriptive statistics and estimate regression models. (SAS)

analyze the consumer expenditure survey (ce) with r the consumer expenditure survey (ce) is the primo data source to understand how americans spend money. participating households keep a running diary about every little purchase over the year. those diaries are then summed up into precise expenditure categories. how else are you gonna know that the average american household spent $34 (±2) on bacon, $826 (±17) on cellular phones, and $13 (±2) on digital e-readers in 2011? an integral component of the market basket calculation in the consumer price index, this survey recently became available as public-use microdata and they're slowly releasing historical files back to 1996. hooray! for a t aste of what's possible with ce data, look at the quick tables listed on their main page - these tables contain approximately a bazillion different expenditure categories broken down by demographic groups. guess what? i just learned that americans living in households with $5,000 to $9,999 of annual income spent an average of $283 (±90) on pets, toys, hobbies, and playground equipment (pdf page 3). you can often get close to your statistic of interest from these web tables. but say you wanted to look at domestic pet expenditure among only households with children between 12 and 17 years old. another one of the thirteen web tables - the consumer unit composition table - shows a few different breakouts of households with kids, but none matching that exact population of interest. the bureau of labor statistics (bls) (the survey's designers) and the census bureau (the survey's administrators) have provided plenty of the major statistics and breakouts for you, but they're not psychic. if you want to comb through this data for specific expenditure categories broken out by a you-defined segment of the united states' population, then let a little r into your life. fun starts now. fair warning: only analyze t he consumer expenditure survey if you are nerd to the core. the microdata ship with two different survey types (interview and diary), each containing five or six quarterly table formats that need to be stacked, merged, and manipulated prior to a methodologically-correct analysis. the scripts in this repository contain examples to prepare 'em all, just be advised that magnificent data like this will never be no-assembly-required. the folks at bls have posted an excellent summary of what's av ailable - read it before anything else. after that, read the getting started guide. don't skim. a few of the descriptions below refer to sas programs provided by the bureau of labor statistics. you'll find these in the C:\My Directory\CES\2011\docs directory after you run the download program. this new github repository contains three scripts: 2010-2011 - download all microdata.R lo op through every year and download every file hosted on the bls's ce ftp site import each of the comma-separated value files into r with read.csv depending on user-settings, save each table as an r data file (.rda) or stat a-readable file (.dta) 2011 fmly intrvw - analysis examples.R load the r data files (.rda) necessary to create the 'fmly' table shown in the ce macros program documentation.doc file construct that 'fmly' table, using five quarters of interviews (q1 2011 thru q1 2012) initiate a replicate-weighted survey design object perform some lovely li'l analysis examples replicate the %mean_variance() macro found in "ce macros.sas" and provide some examples of calculating descriptive statistics using unimputed variables replicate the %compare_groups() macro found in "ce macros.sas" and provide some examples of performing t -tests using unimputed variables create an rsqlite database (to minimize ram usage) containing the five imputed variable files, after identifying which variables were imputed based on pdf page 3 of the user's guide to income imputation initiate a replicate-weighted, database-backed, multiply-imputed survey design object perform a few additional analyses that highlight the modified syntax required for multiply-imputed survey designs replicate the %mean_variance() macro found in "ce macros.sas" and provide some examples of calculating descriptive statistics using imputed variables repl icate the %compare_groups() macro found in "ce macros.sas" and provide some examples of performing t-tests using imputed variables replicate the %proc_reg() and %proc_logistic() macros found in "ce macros.sas" and provide some examples of regressions and logistic regressions using both unimputed and imputed variables replicate integrated mean and se.R match each step in the bls-provided sas program "integr ated mean and se.sas" but with r instead of sas create an rsqlite database when the expenditure table gets too large for older computers to handle in ram export a table "2011 integrated mean and se.csv" that exactly matches the contents of the sas-produced "2011 integrated mean and se.lst" text file click here to view these three scripts for...

The simulated synthetic aperture sonar (SAS) data presented here was generated using PoSSM [Johnson and Brown 2018]. The data is suitable for bistatic, coherent signal processing and will form acoustic seafloor imagery. Included in this data package is simulated sonar data in Generic Data Format (GDF) files, a description of the GDF file contents, example SAS imagery, and supporting information about the simulated scenes. In total, there are eleven 60 m x 90 m scenes, labeled scene00 through scene10, with scene00 provided with the scatterers in isolation, i.e. no seafloor texture. This is provided for beamformer testing purposes and should result in an image similar to the one labeled "PoSSM-scene00-scene00-starboard-0.tif" in the Related Data Sets tab. The ten other scenes have varying degrees of model variation as described in "Description_of_Simulated_SAS_Data_Package.pdf". A description of the data and the model is found in the associated document called "Description_of_Simulated_SAS_Data_Package.pdf" and a description of the format in which the raw binary data is stored is found in the related document "PSU_GDF_Format_20240612.pdf". The format description also includes MATLAB code that will effectively parse the data to aid in signal processing and image reconstruction. It is left to the researcher to develop a beamforming algorithm suitable for coherent signal and image processing. Each 60 m x 90 m scene is represented by 4 raw (not beamformed) GDF files, labeled sceneXX-STARBOARD-000000 through 000003. It is possible to beamform smaller scenes from any one of these 4 files, i.e. the four files are combined sequentially to form a 60 m x 90 m image. Also included are comma separated value spreadsheets describing the locations of scatterers and objects of interest within each scene. In addition to the binary GDF data, a beamformed GeoTIFF image and a single-look complex (SLC, science file) data of each scene is provided. The SLC data (science) is stored in the Hierarchical Data Format 5 (https://www.hdfgroup.org/), and appended with ".hdf5" to indicate the HDF5 format. The data are stored as 32-bit real and 32-bit complex values. A viewer is available that provides basic graphing, image display, and directory navigation functions (https://www.hdfgroup.org/downloads/hdfview/). The HDF file contains all the information necessary to reconstruct a synthetic aperture sonar image. All major and contemporary programming languages have library support for encoding/decoding the HDF5 format. Supporting documentation that outlines positions of the seafloor scatterers is included in "Scatterer_Locations_Scene00.csv", while the locations of the objects of interest for scene01-scene10 are included in "Object_Locations_All_Scenes.csv". Portable Network Graphic (PNG) images that plot the location of objects of all the objects of interest in each scene in Along-Track and Cross-Track notation are provided.

SAS code used to produce descriptive statistics for Low-wage Atlas. These include demographics of low-wage workers by state. Program is run on American Community Survey 1% sample data.

The Healthcare Descriptive Analysis market is experiencing robust growth, projected to reach $18.36 million in 2025 and exhibiting a remarkable Compound Annual Growth Rate (CAGR) of 23.50%. This expansion is fueled by several key drivers. The increasing volume of healthcare data generated from electronic health records (EHRs), medical devices, and wearable sensors necessitates sophisticated analytical tools for efficient management and insightful interpretation. Furthermore, the rising demand for improved patient outcomes, operational efficiency within healthcare organizations, and the ability to conduct proactive, data-driven research are significantly contributing to market growth. The adoption of cloud-based solutions is accelerating, offering scalability and cost-effectiveness compared to on-premise deployments. Clinics and hospitals are leading the adoption, followed by other private organizations. The market is segmented across various applications (clinical, financial, administrative, and research data analytics) and components (software, hardware, and services). Software solutions dominate the market share, leveraging advanced algorithms for data mining, visualization, and predictive modeling. The market's growth trajectory is expected to continue throughout the forecast period (2025-2033). While specific regional market shares are not provided, North America is anticipated to maintain a substantial market share due to early adoption of advanced analytics and robust healthcare infrastructure. The Asia Pacific region, however, is poised for significant growth driven by increasing healthcare expenditure and technological advancements. Competitive pressures are intense, with established players like SAS Institute, Oracle, and IBM competing with specialized healthcare analytics providers such as MedeAnalytics and Health Catalyst. The market faces challenges such as data privacy concerns, the need for skilled data analysts, and the high cost of implementation and maintenance of advanced analytics solutions. However, ongoing technological advancements and increasing government initiatives to improve healthcare data management are expected to mitigate these challenges and drive further market expansion. This comprehensive report provides a detailed analysis of the Healthcare Descriptive Analysis Market, offering invaluable insights for stakeholders across the healthcare IT landscape. With a study period spanning 2019-2033, a base year of 2025, and a forecast period of 2025-2033, this report utilizes extensive data analysis to illuminate market trends, growth drivers, and potential challenges. The market is projected to reach significant values in the millions. Recent developments include: In November 2022, Ursa Health updated Ursa Studio, its healthcare analytics development platform, to help organizations meet the requirements of the Centers for Medicare and Medicaid Services (CMS)., In November 2022, Hartford HealthCare entered a long-term partnership with Google Cloud to advance the healthcare digital transformation, improve data analytics, and enhance care delivery and access.. Key drivers for this market are: Need for Comprehensive Analytics, Integration of Big Data into Healthcare. Potential restraints include: Data Privacy and Security Concerns. Notable trends are: Cloud-based Segment Expected to Hold a Significant Share of the Market During the Forecast Period.

Note. BA = body acceptance, SP = self-protection from social stigmas, FB = feeling and believing in one's capacities, M = mean, SD =  standard deviation, λ = item-factor loading, θ = error term.Brazilian Portuguese original version of the items are given in brackets.Item-factor loadings and descriptive statistics of SAS-EB item scores in Study 3.

Output from programming code written to summarize data describing 2018 MCSP Trial monitoring sites acquired using a SOP 1 (see ServCat reference 103364) of FWS Legacy Regions 2 and 3. Monitoring sites were selected using a custom GRTS draw conducted by USGS in 2017, within monitoring areas associated with select NWRS stations. Areas monitored included Balcones Canyonlands (TX), Hagerman (TX), Washita (OK), Neal Smith (IA) NWRs and several locations near the town of Lamoni, Iowa and private lands in northern Missouri.

Descriptive statistics of the SAS-SV [total (male, female)].

Mortality rates were calculated as defined in the text.Summary statistics for Black cervical cancer mortality rates in thirteen U.S. states from 1975 to 2010.

The Delta Neighborhood Physical Activity Study was an observational study designed to assess characteristics of neighborhood built environments associated with physical activity. It was an ancillary study to the Delta Healthy Sprouts Project and therefore included towns and neighborhoods in which Delta Healthy Sprouts participants resided. The 12 towns were located in the Lower Mississippi Delta region of Mississippi. Data were collected via electronic surveys between August 2016 and September 2017 using the Rural Active Living Assessment (RALA) tools and the Community Park Audit Tool (CPAT). Scale scores for the RALA Programs and Policies Assessment and the Town-Wide Assessment were computed using the scoring algorithms provided for these tools via SAS software programming. The Street Segment Assessment and CPAT do not have associated scoring algorithms and therefore no scores are provided for them. Because the towns were not randomly selected and the sample size is small, the data may not be generalizable to all rural towns in the Lower Mississippi Delta region of Mississippi. Dataset one contains data collected with the RALA Programs and Policies Assessment (PPA) tool. Dataset two contains data collected with the RALA Town-Wide Assessment (TWA) tool. Dataset three contains data collected with the RALA Street Segment Assessment (SSA) tool. Dataset four contains data collected with the Community Park Audit Tool (CPAT). [Note : title changed 9/4/2020 to reflect study name] Resources in this dataset:Resource Title: Dataset One RALA PPA Data Dictionary. File Name: RALA PPA Data Dictionary.csvResource Description: Data dictionary for dataset one collected using the RALA PPA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Two RALA TWA Data Dictionary. File Name: RALA TWA Data Dictionary.csvResource Description: Data dictionary for dataset two collected using the RALA TWA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Three RALA SSA Data Dictionary. File Name: RALA SSA Data Dictionary.csvResource Description: Data dictionary for dataset three collected using the RALA SSA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Four CPAT Data Dictionary. File Name: CPAT Data Dictionary.csvResource Description: Data dictionary for dataset four collected using the CPAT.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset One RALA PPA. File Name: RALA PPA Data.csvResource Description: Data collected using the RALA PPA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Two RALA TWA. File Name: RALA TWA Data.csvResource Description: Data collected using the RALA TWA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Three RALA SSA. File Name: RALA SSA Data.csvResource Description: Data collected using the RALA SSA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Four CPAT. File Name: CPAT Data.csvResource Description: Data collected using the CPAT.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Data Dictionary. File Name: DataDictionary_RALA_PPA_SSA_TWA_CPAT.csvResource Description: This is a combined data dictionary from each of the 4 dataset files in this set.

The 1990 SAS Transport Files portion of the Archive of Census Related Products (ACRP) contains housing and population data from the U.S. Census Bureau's 1990 Summary tape File (STF3A) database. The data are available by state and county, county subdivision/mcd, blockgroup, and places, as well as Indian reservations, tribal districts and congressional districts. This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).

The 1980 SAS Transport Files portion of the Archive of Census Related Products (ACRP) contains housing and population demographics from the 1980 Summary Tape File (STF3A) database and are organized by state. The population data includes education levels, ethnicity, income distribution, nativity, labor force status, means of transportation and family structure while the housing data embodies size, state and structure of housing Unit, value of the Unit, tenure and occupancy status in housing Unit, source of water, sewage disposal, availability of telephone, heating and air conditioning, kitchen facilities, rent, mortgage status and monthly owner costs. This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).

no abstract provided

This item has been replaced by the one which can be found at https://doi.org/10.7488/ds/3411 ## GWAS summary statistics from 7,491 critically ill patients from COVID-19 and 48,400 population controls: European(EUR) 5,989/42,891; South Asian(SAS) 788/3,793; African(AFR) 440/1,350; East Asian(EAS) 274/366. GWAS models were calculated with SAIGE using a logisitic mixed-model regression. A trans-ancestry meta-analysis was performed using inverse-variant weighted fixed-effects. Ancestry-specific and trans-ancestry summary statistics are available. TWAS was performed using GTEx v8 gene expression data for lung and blood and an all-tissue meta-analysis. Summary statistics for tissue-specific and meta-TWAS are available.

Mortality rates were calculated as defined in the text.Summary statistics for White cervical cancer mortality rates in 13 U.S. states from 1975 to 2010.

The GAPs Data Repository provides a comprehensive overview of available qualitative and quantitative data on national return regimes, now accessible through an advanced web interface at https://data.returnmigration.eu/.

This updated guideline outlines the complete process, starting from the initial data collection for the return migration data repository to the development of a comprehensive web-based platform. Through iterative development, participatory approaches, and rigorous quality checks, we have ensured a systematic representation of return migration data at both national and comparative levels.

The Repository organizes data into five main categories, covering diverse aspects and offering a holistic view of return regimes: country profiles, legislation, infrastructure, international cooperation, and descriptive statistics. These categories, further divided into subcategories, are based on insights from a literature review, existing datasets, and empirical data collection from 14 countries. The selection of categories prioritizes relevance for understanding return and readmission policies and practices, data accessibility, reliability, clarity, and comparability. Raw data is meticulously collected by the national experts.

The transition to a web-based interface builds upon the Repository’s original structure, which was initially developed using REDCap (Research Electronic Data Capture). It is a secure web application for building and managing online surveys and databases.The REDCAP ensures systematic data entries and store them on Uppsala University’s servers while significantly improving accessibility and usability as well as data security. It also enables users to export any or all data from the Project when granted full data export privileges. Data can be exported in various ways and formats, including Microsoft Excel, SAS, Stata, R, or SPSS for analysis. At this stage, the Data Repository design team also converted tailored records of available data into public reports accessible to anyone with a unique URL, without the need to log in to REDCap or obtain permission to access the GAPs Project Data Repository. Public reports can be used to share information with stakeholders or external partners without granting them access to the Project or requiring them to set up a personal account. Currently, all public report links inserted in this report are also available on the Repository’s webpage, allowing users to export original data.

This report also includes a detailed codebook to help users understand the structure, variables, and methodologies used in data collection and organization. This addition ensures transparency and provides a comprehensive framework for researchers and practitioners to effectively interpret the data.

The GAPs Data Repository is committed to providing accessible, well-organized, and reliable data by moving to a centralized web platform and incorporating advanced visuals. This Repository aims to contribute inputs for research, policy analysis, and evidence-based decision-making in the return and readmission field.

Explore the GAPs Data Repository at https://data.returnmigration.eu/.

Abstract (en): The main objectives of this data collection effort were to assemble a set of cross-nationally comparable microdata samples for Economic Commission for Europe (ECE) countries based on the 1990 national population and housing censuses in countries of Europe and North America, and to use these samples to study the social and economic conditions of older persons. The samples are designed to allow research on a wide range of issues related to aging, as well as on other social phenomena. Included in the Czech Republic dataset are questions on the type and characteristics of buildings/dwellings, available utility systems, and demographic information such as age, sex, marital status, number of children, education, income, religion, and occupation. Also included are questions concerning the presence of household amenities such as telephones, toilets, automobiles, baths/showers, washers, and television sets. All persons and housing units in the Czech Republic. Individual-based sample of 1,029,471 persons with progressive oversampling with age, while retaining information on all persons co-residing in the sampled person's dwelling unit (N = 1,574,936). 2013-09-27 This study was previously distributed on CD-ROM only. The contents of the CD-ROM are now available for public download from ICPSR as a zipped package.2008-09-24 The confidentiality agreement is now available as a downloadable PDF document. Funding insitution(s): United Nations Population Fund. United Nations Economic Commission for Europe. United States Department of Health and Human Services. National Institutes of Health. National Institute on Aging. In addition to the SAS data file provided by the principal investigator, ICPSR is distributing an ASCII data file extracted from the SAS file. Analysis of the ASCII file may be facilitated by dividing it.Erroneously coded missing values on age have been corrected, resulting in 1,650 households being dropped from the sample. The principal investigator has provided a corrected version of the data, in one file instead of four, a revised codebook, descriptive statistics, and SAS and SPSS data definition statements.

The Data Analysis Services market is experiencing robust growth, driven by the exponential increase in data volume and the rising demand for data-driven decision-making across various industries. The market, estimated at $150 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching an impressive $450 billion by 2033. This expansion is fueled by several key factors, including the increasing adoption of cloud-based analytics platforms, the growing need for advanced analytics techniques like machine learning and AI, and the rising focus on data security and compliance. The market is segmented by service type (e.g., predictive analytics, descriptive analytics, prescriptive analytics), industry vertical (e.g., healthcare, finance, retail), and deployment model (cloud, on-premise). Key players like IBM, Accenture, Microsoft, and SAS Institute are investing heavily in research and development, expanding their service portfolios, and pursuing strategic partnerships to maintain their market leadership. The competitive landscape is characterized by both large established players and emerging niche providers offering specialized solutions. The market's growth trajectory is influenced by various trends, including the increasing adoption of big data technologies, the growing prevalence of self-service analytics tools empowering business users, and the rise of specialized data analysis service providers catering to specific industry needs. However, certain restraints, such as the lack of skilled data analysts, data security concerns, and the high cost of implementation and maintenance of advanced analytics solutions, could potentially hinder market growth. Addressing these challenges through investments in data literacy programs, enhanced security measures, and flexible pricing models will be crucial for sustaining the market's momentum and unlocking its full potential. Overall, the Data Analysis Services market presents a significant opportunity for companies offering innovative solutions and expertise in this rapidly evolving landscape.

Descriptive statistics and distribution of PCOC SAS total scores and individual items, including by distress status.