This statistic depicts the distribution of tools used to compile data and present analytics and/or reports to management, according to a marketing survey of C-level executives, conducted in December 2015 by Black Ink. As of December 2015, 9 percent of respondents used statistical modeling tools, such as IBM's SPSS or the SAS Institute's Statistical Analysis System package, to compile and present their reports.

Most studies in the life sciences and other disciplines involve generating and analyzing numerical data of some type as the foundation for scientific findings. Working with numerical data involves multiple challenges. These include reproducible data acquisition, appropriate data storage, computationally correct data analysis, appropriate reporting and presentation of the results, and suitable data interpretation.Finding and correcting mistakes when analyzing and interpreting data can be frustrating and time-consuming. Presenting or publishing incorrect results is embarrassing but not uncommon. Particular sources of errors are inappropriate use of statistical methods and incorrect interpretation of data by software. To detect mistakes as early as possible, one should frequently check intermediate and final results for plausibility. Clearly documenting how quantities and results were obtained facilitates correcting mistakes. Properly understanding data is indispensable for reaching well-founded conclusions from experimental results. Units are needed to make sense of numbers, and uncertainty should be estimated to know how meaningful results are. Descriptive statistics and significance testing are useful tools for interpreting numerical results if applied correctly. However, blindly trusting in computed numbers can also be misleading, so it is worth thinking about how data should be summarized quantitatively to properly answer the question at hand. Finally, a suitable form of presentation is needed so that the data can properly support the interpretation and findings. By additionally sharing the relevant data, others can access, understand, and ultimately make use of the results.These quick tips are intended to provide guidelines for correctly interpreting, efficiently analyzing, and presenting numerical data in a useful way.

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching 149 zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than 394 zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just two percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of 19.2 percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached 6.7 zettabytes.

Statistical Analysis Software Market size was valued at USD 7,963.44 Million in 2023 and is projected to reach USD 13,023.63 Million by 2030, growing at a CAGR of 7.28% during the forecast period 2024-2030.

Global Statistical Analysis Software Market Drivers

The market drivers for the Statistical Analysis Software Market can be influenced by various factors. These may include:

Growing Data Complexity and Volume: The demand for sophisticated statistical analysis tools has been fueled by the exponential rise in data volume and complexity across a range of industries. Robust software solutions are necessary for organizations to evaluate and extract significant insights from huge datasets.
Growing Adoption of Data-Driven Decision-Making: Businesses are adopting a data-driven approach to decision-making at a faster rate. Utilizing statistical analysis tools, companies can extract meaningful insights from data to improve operational effectiveness and strategic planning.
Developments in Analytics and Machine Learning: As these fields continue to progress, statistical analysis software is now capable of more. These tools’ increasing popularity can be attributed to features like sophisticated modeling and predictive analytics.
A greater emphasis is being placed on business intelligence: Analytics and business intelligence are now essential components of corporate strategy. In order to provide business intelligence tools for studying trends, patterns, and performance measures, statistical analysis software is essential.
Increasing Need in Life Sciences and Healthcare: Large volumes of data are produced by the life sciences and healthcare sectors, necessitating complex statistical analysis. The need for data-driven insights in clinical trials, medical research, and healthcare administration is driving the market for statistical analysis software.
Growth of Retail and E-Commerce: The retail and e-commerce industries use statistical analytic tools for inventory optimization, demand forecasting, and customer behavior analysis. The need for analytics tools is fueled in part by the expansion of online retail and data-driven marketing techniques.
Government Regulations and Initiatives: Statistical analysis is frequently required for regulatory reporting and compliance with government initiatives, particularly in the healthcare and finance sectors. In these regulated industries, statistical analysis software uptake is driven by this.
Big Data Analytics’s Emergence: As big data analytics has grown in popularity, there has been a demand for advanced tools that can handle and analyze enormous datasets effectively. Software for statistical analysis is essential for deriving valuable conclusions from large amounts of data.
Demand for Real-Time Analytics: In order to make deft judgments fast, there is a growing need for real-time analytics. Many different businesses have a significant demand for statistical analysis software that provides real-time data processing and analysis capabilities.
Growing Awareness and Education: As more people become aware of the advantages of using statistical analysis in decision-making, its use has expanded across a range of academic and research institutions. The market for statistical analysis software is influenced by the academic sector.
Trends in Remote Work: As more people around the world work from home, they are depending more on digital tools and analytics to collaborate and make decisions. Software for statistical analysis makes it possible for distant teams to efficiently examine data and exchange findings.

Scientific advances in microbial ecology rely on both high-quality data and rigorous analysis. At present, Statistical Analysis sections of many microbiome papers lack essential detail and justification. To support researchers in clearly and transparently presenting their methods, we provide guidelines for describing a microbiome data analysis. The guidelines span data transformations, justification for modeling choices, parameter interpretation, sensitivity analyses, code and data availability, and more. These guidelines are available under a Creative Commons Zero (CC0) license. We hope to accelerate the accumulation and dissemination of scientific knowledge by permitting their condition-free distribution, adaptation, and development. Methods These guidelines were drafted by the authors.

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.

The Institutional Cost Report (ICR) is a uniform report completed by New York hospitals to report income, expenses, assets, liabilities, and statistics to the Department of Health (DOH). Under DOH regulations, (Part 86-1.2), Article 28 hospitals are required to file financial and statistical data with DOH annually. The data filed is part of the ICR and is received electronically through a secured network. This data is used to develop Medicaid rates, assist in the formulation of reimbursement methodologies, and analyze trends. This dataset includes the print image of the edited data. The ICR is a comprehensive compilation of exhibits that have been modified over time that users should consider when using the ICR dataset. It is possible that data is updated subsequent to posting on this website; therefore the data could become obsolete. To get the details related to the exhibits and data elements, please refer to the blank ICR form, the ICR Table of Contents, the ICR Instructions and the Glossary of Terms, Acronyms, and Abbreviations which are in the Supporting Information section of this site. The data posted as edited contains desk edit adjustments by DOH personnel. In 2009, this information was not audited; however effective with the 2010 ICR, all ICRs will be audited by a Certified Public Accounting Firm annually.

As of March 2024, there were a reported 5,381 data centers in the United States, the most of any country worldwide. A further 521 were located in Germany, while 514 were located in the United Kingdom. What is a data center? A data center is a network of computing and storage resources that enables the delivery of shared software applications and data. These centers can house large amounts of critical and important data, and therefore are vital to the daily functions of companies and consumers alike. As a result, whether it is a cloud, colocation, or managed service, data center real estate will have increasing importance worldwide. Hyperscale data centers In the past, data centers were highly controlled physical infrastructures, but the cloud has since changed that model. A cloud data service is a remote version of a data center – located somewhere away from a company's physical premises. Cloud IT infrastructure spending has grown and is forecast to rise further in the coming years. The evolution of technology, along with the rapid growth in demand for data across the globe, is largely driven by the leading hyperscale data center providers.

Abstract copyright UK Data Service and data collection copyright owner.

The Smart Energy Research Lab (SERL) Observatory facilitates a broad range of energy demand research and is a unique data resource for research where access to high resolution, large scale energy data linked to relevant contextual data is required. Further information about SERL can be found on the Smart Energy Research Lab website.

This dataset of aggregated statistics is available under standard Safeguarded (End User Licence) access conditions. It contains over 2.5 million rows of data and describes domestic gas and electricity energy use in Great Britain 2020-2023 based on data from the Smart Energy Research Lab (SERL) Observatory, which consists of smart meter and contextual data from approximately 13,000 homes that are broadly representative of the GB population in terms of region and Index of Multiple Deprivation (IMD) quintile. This aggregated dataset can be used, for example, to show how residential energy use in GB varies over time (monthly over the year and half-hourly over the course of the day); and can be broken down by occupant characteristics (number of occupants, tenure), property characteristics (age, size, form, and Energy Performance Certificate (EPC)), by type of heating system, presence of solar panels and of electric vehicles, and by weather, region and IMD quintile.

Secure Access data
A more detailed set of SERL data, including smart meter data and additional contextual data, is available under restricted Secure access conditions under SN 8666: Smart Energy Research Lab Observatory Data: Secure Access. It is a longitudinal dataset containing records from August 2019, with updates provided to researchers on a (roughly) quarterly basis. Users should download this safeguarded access statistical study first to see whether it is suitable for their needs before considering an application for the Secure dataset.

The second edition (May 2024) includes summaries of daily average energy use in a data file for 2020-2023, and summaries of half-hourly average energy use in four data files for 2020-2023, as well as an accompanying technical document.

Energy (electricity and gas) consumption in households across Great Britain.

Vatican Data Series {title at top of page}Data Developers: Burhans, Molly A., Cheney, David M., Emege, Thomas, Gerlt, R.. . “Vatican Data Series {title at top of page}”. Scale not given. Version 1.0. MO and CT, USA: GoodLands Inc., Catholic Hierarchy, Environmental Systems Research Institute, Inc., 2019.Web map developer: Molly Burhans, October 2019Web app developer: Molly Burhans, October 2019GoodLands’ polygon data layers, version 2.0 for global ecclesiastical boundaries of the Roman Catholic Church:Although care has been taken to ensure the accuracy, completeness and reliability of the information provided, due to this being the first developed dataset of global ecclesiastical boundaries curated from many sources it may have a higher margin of error than established geopolitical administrative boundary maps. Boundaries need to be verified with appropriate Ecclesiastical Leadership. The current information is subject to change without notice. No parties involved with the creation of this data are liable for indirect, special or incidental damage resulting from, arising out of or in connection with the use of the information. We referenced 1960 sources to build our global datasets of ecclesiastical jurisdictions. Often, they were isolated images of dioceses, historical documents and information about parishes that were cross checked. These sources can be viewed here:https://docs.google.com/spreadsheets/d/11ANlH1S_aYJOyz4TtG0HHgz0OLxnOvXLHMt4FVOS85Q/edit#gid=0To learn more or contact us please visit: https://good-lands.org/The Catholic Leadership global maps information is derived from the Annuario Pontificio, which is curated and published by the Vatican Statistics Office annually, and digitized by David Cheney at Catholic-Hierarchy.org -- updated are supplemented with diocesan and news announcements. GoodLands maps this into global ecclesiastical boundaries. Admin 3 Ecclesiastical Territories:Burhans, Molly A., Cheney, David M., Gerlt, R.. . “Admin 3 Ecclesiastical Territories For Web”. Scale not given. Version 1.2. MO and CT, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2019.Derived from:Global Diocesan Boundaries:Burhans, M., Bell, J., Burhans, D., Carmichael, R., Cheney, D., Deaton, M., Emge, T. Gerlt, B., Grayson, J., Herries, J., Keegan, H., Skinner, A., Smith, M., Sousa, C., Trubetskoy, S. “Diocesean Boundaries of the Catholic Church” [Feature Layer]. Scale not given. Version 1.2. Redlands, CA, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2016.Using: ArcGIS. 10.4. Version 10.0. Redlands, CA: Environmental Systems Research Institute, Inc., 2016.Boundary ProvenanceStatistics and Leadership DataCheney, D.M. “Catholic Hierarchy of the World” [Database]. Date Updated: August 2019. Catholic Hierarchy. Using: Paradox. Retrieved from Original Source.Catholic HierarchyAnnuario Pontificio per l’Anno .. Città del Vaticano :Tipografia Poliglotta Vaticana, Multiple Years.The data for these maps was extracted from the gold standard of Church data, the Annuario Pontificio, published yearly by the Vatican. The collection and data development of the Vatican Statistics Office are unknown. GoodLands is not responsible for errors within this data. We encourage people to document and report errant information to us at data@good-lands.org or directly to the Vatican.Additional information about regular changes in bishops and sees comes from a variety of public diocesan and news announcements.

Suspicious Activity Report (SAR) statistics generated by this tool solely reflect the data submitted on FinCEN Form 111. Use of this form for FinCEN SARs was voluntary during the period of March 1, 2012 through March 31, 2013 and mandatory starting on April 1, 2013. FinCEN Form 111 has replaced the individual legacy SAR types formerly filed on TD F 90-22.47 (Depository Institutions), FinCEN Form 109 (Money Services Business), FinCEN Form 102 (Casinos & Card Clubs), and FinCEN Form 101 (Securities & Futures Industries). The statistics are based on the Bank Secrecy Act Identification Number of each record within the SAR system. The Bank Secrecy Act Identification Number is a unique number assigned to each SAR submitted. Statistical data for SARs are updated as information is processed and refreshed data is periodically made available for this tool. For this reason, there may be discrepancies between the statistical figures returned from queries performed at different times. In addition, slight differences in query criteria may return different statistical results. Also note that the statistics generated by this tool do not include SAR fields that contain unknown or blank data. To the extent statistics including blank or unknown data are tabulated outside of this tool for other purposes, there may be discrepancies between statistics generated by this tool and those generated through other means. FinCEN makes no claims, promises or guarantees about the accuracy or completeness of the statistical figures provided from this tool and expressly disclaims liability for errors, omissions, or discrepancies in the statistical figures.

This annual report provides program and demographic information on the people who receive Social Security Disability Insurance Program benefits. This edition presents a series of detailed tables on the three categories of beneficiaries: disabled workers, disabled widowers, and disabled adult children. Numbers presented in these tables may differ slightly from other published statistics because all tables, except those using data from the Survey of Income and Program Participation, are based on 100 percent data files. Report for 2006.

This is the latest statistical publication of linked HES (Hospital Episode Statistics) and DID (Diagnostic Imaging Data set) data held by the Health and Social Care Information Centre. The HES-DID linkage provides the ability to undertake national (within England) analysis along acute patient pathways to understand typical imaging requirements for given procedures, and/or the outcomes after particular imaging has been undertaken, thereby enabling a much deeper understanding of outcomes of imaging and to allow assessment of variation in practice. This publication aims to highlight to users the availability of this updated linkage and provide users of the data with some standard information to assess their analysis approach against. The two data sets have been linked using specific patient identifiers collected in HES and DID. The linkage allows the data sets to be linked from April 2012 when the DID data was first collected; however this report focuses on patients who were present in the either data set in the period 1 April 2015 to 30 September 2015 only. This is provisional 2015-16 data. The linkage used for this publication was created on 7 January 2016 and released together with this publication on 4 February 2016.

Documentation for the Annual Statistical Report.

Prior to statistical analysis of mass spectrometry (MS) data, quality control (QC) of the identified biomolecule peak intensities is imperative for reducing process-based sources of variation and extreme biological outliers. Without this step, statistical results can be biased. Additionally, liquid chromatography–MS proteomics data present inherent challenges due to large amounts of missing data that require special consideration during statistical analysis. While a number of R packages exist to address these challenges individually, there is no single R package that addresses all of them. We present pmartR, an open-source R package, for QC (filtering and normalization), exploratory data analysis (EDA), visualization, and statistical analysis robust to missing data. Example analysis using proteomics data from a mouse study comparing smoke exposure to control demonstrates the core functionality of the package and highlights the capabilities for handling missing data. In particular, using a combined quantitative and qualitative statistical test, 19 proteins whose statistical significance would have been missed by a quantitative test alone were identified. The pmartR package provides a single software tool for QC, EDA, and statistical comparisons of MS data that is robust to missing data and includes numerous visualization capabilities.

This print image version of the Institutional Cost Report (ICR) has been audited by the DOH. is the Institutional Cost Report (ICR) is a uniform report completed by New York hospitals to report income, expenses, assets, liabilities, and statistics to the Department of Health (DOH). Under DOH regulations, (Part 86-1.2), Article 28 hospitals are required to file financial and statistical data with DOH annually. The data filed is part of the ICR and is received electronically through a secured network. This data is used to develop Medicaid rates, assist in the formulation of reimbursement methodologies, and analyze trends. For more information, check out: http://www.health.ny.gov/facilities/hospital/index.htm

This record was retired 29/03/2022 with approval from S.Oliver as it has been superseded by eCat 146091 Geoscience Australia Landsat Water Observation Statistics Collection 3

WOfS-STATS (WO_STATS_2.1.5) is a set of statistical summaries of the water observations contained in WOfS (WO_2.1.5). The layers available are: the count of clear observations;the count of wet observations;the percentage of wet observations over time.

This product is Water Observations from Space - Statistics (WO-STATS), a set of statistical summaries of the WOfS product that combines the many years of WOfS observations into summary products that help the understanding of surface water across Australia.

WO-STATS consists of the following datasets:

Clear Count: how many times an area could be clearly seen (ie. not affected by clouds, shadows or other satellite observation problems),
Wet Count: how many times water was detected inobservations that were clear,
Water Summary: what percentage of clear observations were detected as wet (ie. the ration of wet to clear as a percentage)

As no confidence filtering is applied to this product, it is affected by noise where misclassifications have occurred in the WOfS water classifications, and hence can be difficult to interpret on its own. The confidence layer and filtered summary are contained in the WO-Fil-STATS product, which provide a noise-reduced view of the water summary.

WO-STATS is available in multiple forms, depending on the length of time over which the statistics are calculated. At present the following are available:

WO-STATS: statistics calculated from the full depth of time series (1986 to present)
WO-STATS-ANNUAL: statistics calculated from each calendar year (1986 to present)
WO-STATS-NOV-MAR: statistics calculated yearly from November to March (1986 to present)
WO-STATS-APR-OCT: statistics calculated yearly from April to October (1986 to present)

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. This catalogue entry provides post-processed ERA5 hourly single-level data aggregated to daily time steps. In addition to the data selection options found on the hourly page, the following options can be selected for the daily statistic calculation:

The daily aggregation statistic (daily mean, daily max, daily min, daily sum*) The sub-daily frequency sampling of the original data (1 hour, 3 hours, 6 hours) The option to shift to any local time zone in UTC (no shift means the statistic is computed from UTC+00:00)

*The daily sum is only available for the accumulated variables (see ERA5 documentation for more details). Users should be aware that the daily aggregation is calculated during the retrieval process and is not part of a permanently archived dataset. For more details on how the daily statistics are calculated, including demonstrative code, please see the documentation. For more details on the hourly data used to calculate the daily statistics, please refer to the ERA5 hourly single-level data catalogue entry and the documentation found therein.