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.

Data analysis can be accurate and reliable only if the underlying assumptions of the used statistical method are validated. Any violations of these assumptions can change the outcomes and conclusions of the analysis. In this study, we developed Smart Data Analysis V2 (SDA-V2), an interactive and user-friendly web application, to assist users with limited statistical knowledge in data analysis, and it can be freely accessed at https://jularatchumnaul.shinyapps.io/SDA-V2/. SDA-V2 automatically explores and visualizes data, examines the underlying assumptions associated with the parametric test, and selects an appropriate statistical method for the given data. Furthermore, SDA-V2 can assess the quality of research instruments and determine the minimum sample size required for a meaningful study. However, while SDA-V2 is a valuable tool for simplifying statistical analysis, it does not replace the need for a fundamental understanding of statistical principles. Researchers are encouraged to combine their expertise with the software’s capabilities to achieve the most accurate and credible results.

This statistic shows the results of a survey conducted in the United States in 2017 on awareness of power tool brands. Some 87 percent of respondents stated they know Black + Decker. The Survey Data Table for the Statista survey DIY, Hardware & (Power)Tools in the United States 2017 contains the complete tables for the survey including various column headings.

The purpose of this project is to improve the accuracy of statistical software by providing reference datasets with certified computational results that enable the objective evaluation of statistical software. Currently datasets and certified values are provided for assessing the accuracy of software for univariate statistics, linear regression, nonlinear regression, and analysis of variance. The collection includes both generated and 'real-world' data of varying levels of difficulty. Generated datasets are designed to challenge specific computations. These include the classic Wampler datasets for testing linear regression algorithms and the Simon & Lesage datasets for testing analysis of variance algorithms. Real-world data include challenging datasets such as the Longley data for linear regression, and more benign datasets such as the Daniel & Wood data for nonlinear regression. Certified values are 'best-available' solutions. The certification procedure is described in the web pages for each statistical method. Datasets are ordered by level of difficulty (lower, average, and higher). Strictly speaking the level of difficulty of a dataset depends on the algorithm. These levels are merely provided as rough guidance for the user. Producing correct results on all datasets of higher difficulty does not imply that your software will pass all datasets of average or even lower difficulty. Similarly, producing correct results for all datasets in this collection does not imply that your software will do the same for your particular dataset. It will, however, provide some degree of assurance, in the sense that your package provides correct results for datasets known to yield incorrect results for some software. The Statistical Reference Datasets is also supported by the Standard Reference Data Program.

Journal of statistical software FAQ - ResearchHelpDesk - The Journal of Statistical Software (JSS) is an open-source and open-access scientific journal by the statistical software community for everybody interested in statistical computing. All aspects of the journal, from editorial work over review and copy-editing up to typesetting and publication, are run by a group of volunteers committed to free software (as in software that respects the users' essential freedoms: the freedom to run it, to study and change it, and to redistribute copies with or without changes) and free-subscription, free-submission open-access publishing ideas. Therefore, and as a matter of principle, JSS charges no author fees or subscription fees. The journal does expect the same level of commitment from authors seeking to publish in JSS. Authors will have to accept a high level of responsibility throughout the whole publishing process, including the preparation of the final publishable versions of article and software. Due to the steadily increasing number of incoming and accepted submissions and limited volunteer resources, publication times can be rather long. Compliance by authors to JSS standards and instructions typically speeds-up this process considerably.

Protein expression varies as a result of intricate regulation of synthesis and degradation of messenger RNAs (mRNA) and proteins. Studies of dynamic regulation typically rely on time-course data sets of mRNA and protein expression, yet there are no statistical methods that integrate these multiomics data and deconvolute individual regulatory processes of gene expression control underlying the observed concentration changes. To address this challenge, we developed Protein Expression Control Analysis (PECA), a method to quantitatively dissect protein expression variation into the contributions of mRNA synthesis/degradation and protein synthesis/degradation, termed RNA-level and protein-level regulation respectively. PECA computes the rate ratios of synthesis versus degradation as the statistical summary of expression control during a given time interval at each molecular level and computes the probability that the rate ratio changed between adjacent time intervals, indicating regulation change at the time point. Along with the associated false-discovery rates, PECA gives the complete description of dynamic expression control, that is, which proteins were up- or down-regulated at each molecular level and each time point. Using PECA, we analyzed two yeast data sets monitoring the cellular response to hyperosmotic and oxidative stress. The rate ratio profiles reported by PECA highlighted a large magnitude of RNA-level up-regulation of stress response genes in the early response and concordant protein-level regulation with time delay. However, the contributions of RNA- and protein-level regulation and their temporal patterns were different between the two data sets. We also observed several cases where protein-level regulation counterbalanced transcriptomic changes in the early stress response to maintain the stability of protein concentrations, suggesting that proteostasis is a proteome-wide phenomenon mediated by post-transcriptional regulation.

The size and share of the market is categorized based on Application (Small & Medium Business, Large Business) and Product (Cloud Based, on Premise) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

Statistical Process Control Software Market size was valued at USD 943.25 Million in 2024 and is projected to reach USD 2151.93 Million by 2031, growing at a CAGR of 11.98% from 2024 to 2031.

Statistical Process Control Software Market Drivers

Quality Assurance and Improvement: Increasing emphasis on quality control and continuous improvement in manufacturing and production processes drives the demand for SPC software. Organizations use SPC to monitor and control process variations, ensuring consistent product quality and reducing defects.

Regulatory Compliance: Many industries, such as pharmaceuticals, automotive, aerospace, and food and beverage, are subject to strict regulatory standards and quality requirements. SPC software helps organizations comply with these regulations by providing tools for monitoring and documenting process performance.

Industrial Automation and Industry 4.0: The rise of industrial automation and the implementation of Industry 4.0 technologies have increased the adoption of SPC software. These technologies rely on real-time data analysis and process control to optimize manufacturing operations and improve efficiency.

Docker has emerged as the leading tool for compiling, testing, and building software, with 59 percent of developers reporting its use in 2024, the popular tool dominates containerization technology. Containerization platforms enable developers to package applications and their dependencies into a standardized unit, ensuring consistency across different environments. Kubernetes, an open-source container platform, was employed by 22 percent of developers in the same year, reflecting the growing importance of scalable, cloud-native applications. JavaScript package managers dominate the landscape In the realm of JavaScript-based development environments, npm (Node Package Manager) is a dominant force, with 52 percent of developers utilizing it for managing packages and dependencies. Yarn, an alternative package manager for JavaScript, shows a significant usage rate at over 21 percent, highlighting its efficiency and reliability. Pip and Homebrew essential for development Python's package manager, Pip, ranked third overall with 30 percent adoption, underscoring the language's popularity in software development. This tool is essential for managing Python packages and dependencies, facilitating a smooth development process. When it came to macOS and Linux, Homebrew was utilized by 24 percent of developers.

The NATCOOP project set out to study how nature shapes the preferences and incentives of economic agents and how this in turn affects common-pool resource management. Imagine a group of fishermen targeting a species that requires a lot of teamwork to harvest. Do these fishers become more social over time compared to fishers that work in a more solitary manner? If so, does this have implications for how the fishery should be managed? To study this, the NATCOOP team travelled to Chile and Tanzania and collected data using surveys and economic experiments. These two very different countries have a large population of small-scale fishermen, and both host several distinct types of fisheries. Over the course of five field trips, the project team surveyed more than 2500 fishermen with each field trip contributing to the main research question by measuring fishermen’s preferences for cooperation and risk. Additionally, each fieldtrip aimed to answer another smaller research question that was either focused on risk taking or cooperation behavior in the fisheries. The data from both surveys and experiments are now publicly available and can be freely studied by other researchers, resource managers, or interested citizens. Overall, the NATCOOP dataset contains participants’ responses to a plethora of survey questions and their actions during incentivized economic experiments. It is available in both the .dta and .csv format, and its use is recommended with statistical software such as R or Stata. For those unaccustomed with statistical analysis, we included a video tutorial on how to use the data set in the open-source program R.

Journal of statistical software Acceptance Rate - ResearchHelpDesk - The Journal of Statistical Software (JSS) is an open-source and open-access scientific journal by the statistical software community for everybody interested in statistical computing. All aspects of the journal, from editorial work over review and copy-editing up to typesetting and publication, are run by a group of volunteers committed to free software (as in software that respects the users' essential freedoms: the freedom to run it, to study and change it, and to redistribute copies with or without changes) and free-subscription, free-submission open-access publishing ideas. Therefore, and as a matter of principle, JSS charges no author fees or subscription fees. The journal does expect the same level of commitment from authors seeking to publish in JSS. Authors will have to accept a high level of responsibility throughout the whole publishing process, including the preparation of the final publishable versions of article and software. Due to the steadily increasing number of incoming and accepted submissions and limited volunteer resources, publication times can be rather long. Compliance by authors to JSS standards and instructions typically speeds-up this process considerably.

Explore the Statistical Analysis Software Market trends! Covers key players, growth rate 10.6% CAGR, market size $15.49 Billion, and forecasts to 2033. Get insights now!

The project added functionality to the Stat-JR, a software environment for promoting interactive complex statistical modelling. For details and free download pages for UK academics, see Related Resources.

When social science researchers wish to carry out research and choose a quantitative approach, they will collect either new data or existing data and perform statistical analysis on this data. In the modern age it has become increasingly important for social science researchers to be trained in quantitative methods and the use of statistical software to analyse datasets and answer research questions. Modern statistical techniques have also become more computational and so there is a desire for software tools that simplify the research process whilst still allowing social scientists access to the most appropriate statistical methods.

In this proposal we build on earlier work where we have prototyped an interactive electronic book (or eBook) system for learning about statistical techniques and performing analysis. An eBook can be thought of as combining the features of a book with those of a statistical package as it contains a mixture of textual information, graphs and tables but also input boxes which when completed write sections of the book conditional on the inputs supplied. We intend to investigate the appropriateness of the new technology and how it may be adapted to be used for various tasks that are commonly performed by social scientists.

The State Revenue Office (SRO) is now publishing a range of First Home Owner Grant statistics. Find out how many Grants, Bonuses and Boost payments have been received in each postcode in Victoria. The SRO have developed an online search tool which will enable users to select a postcode and receive data by number and type of benefit for various years, and months within each year. Graphs representing the data extracted are also available.

This is the replication package for the analysis done in the paper "Evolution of statistical analysis in empirical software engineering research: Current state and steps forward" (DOI: https://doi.org/10.1016/j.jss.2019.07.002, preprint: https://arxiv.org/abs/1706.00933).

The package includes CSV files with data on statistical usage extracted from 5 journals in SE (EMSE, IST, JSS, TOSEM, TSE). The data was extracted from papers between 2001 - 2015. The package also contains forms, scripts and figures (generated using the scripts) used in the paper.

The extraction tool mentioned in the paper is available in dockerhub via: https://hub.docker.com/r/robertfeldt/sept

Statistical Analysis Software Market Analysis

The Statistical Analysis Software Market is experiencing significan...

Transparency in data visualization is an essential ingredient for scientific communication. The traditional approach of visualizing continuous quantitative data solely in the form of summary statistics (i.e., measures of central tendency and dispersion) has repeatedly been criticized for not revealing the underlying raw data distribution. Remarkably, however, systematic and easy-to-use solutions for raw data visualization using the most commonly reported statistical software package for data analysis, IBM SPSS Statistics, are missing. Here, a comprehensive collection of more than 100 SPSS syntax files and an SPSS dataset template is presented and made freely available that allow the creation of transparent graphs for one-sample designs, for one- and two-factorial between-subject designs, for selected one- and two-factorial within-subject designs as well as for selected two-factorial mixed designs and, with some creativity, even beyond (e.g., three-factorial mixed-designs). Depending on graph type (e.g., pure dot plot, box plot, and line plot), raw data can be displayed along with standard measures of central tendency (arithmetic mean and median) and dispersion (95% CI and SD). The free-to-use syntax can also be modified to match with individual needs. A variety of example applications of syntax are illustrated in a tutorial-like fashion along with fictitious datasets accompanying this contribution. The syntax collection is hoped to provide researchers, students, teachers, and others working with SPSS a valuable tool to move towards more transparency in data visualization.

Teaching undergraduate political methodology courses is a challenging task, yet has garnered little pedagogical discussion within the discipline. With the growing use of technology in the classroom, as well as the growing demand for data science and data literacy in our society, better understanding how we use statistical software in these courses is warranted. In this short paper, we shed light on current practices in teaching political methodology courses, with a particular emphasis on the use of statistical software. Combining an analysis of 93 course syllabi with a quantitative survey of research method instructors, we provide key information on the structure of these courses and how they incorporate statistical software. Our results reflect the growing importance of data literacy within the discipline, and suggest that more intentional discussions of research method pedagogy are needed in the future.

According to 84 percent of the software developers surveyed, source code collaboration tools, such as GitHub, GitLab, and Bitbucket, are the most regularly used tools among software developers as of 2022.

Dataset containing variables extracted from original articles published in Peruvian journals.