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.

In 2023, Morningstar Advisor Workstation was by far the most popular data analytics software worldwide. According to a survey carried out between December 2022 and March 2023, the market share of Morningstar Advisor Workstation was 23.81 percent. It was followed by Riskalyze Elite, with 12.21 percent, and YCharts, with 10.82 percent.

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.

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.

The statistic shows the analytics tools currently in use by business organizations in the United States, as well as the analytics tools respondents believe they will be using in two years, according to a 2015 survey conducted by the Harvard Business Review Analytics Service. As of 2015, 73 percent of respondents believed they were going to use predictive analytics for data analysis in two years' time.

The size and share of the market is categorized based on Application (Statistical analysis tools, Data visualization tools, Predictive analytics software, Data mining tools, Business intelligence tools) and Product (Data analysis, Business insights, Market research, Performance management, Forecasting) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

This dataset contains all of the supporting materials to accompany Helsel, D.R., Hirsch, R.M., Ryberg, K.R., Archfield, S.A., and Gilroy, E.J., 2020, Statistical methods in water resources: U.S. Geological Survey Techniques and Methods, book 4, chapter A3, 454 p., https://doi.org/10.3133/tm4a3. [Supersedes USGS Techniques of Water-Resources Investigations, book 4, chapter A3, version 1.1.]. Supplemental material (SM) for each chapter are available to re-create all examples and figures, and to solve the exercises at the end of each chapter, with relevant datasets provided in an electronic format readable by R. The SM provide (1) datasets as .Rdata files for immediate input into R, (2) datasets as .csv files for input into R or for use with other software programs, (3) R functions that are used in the textbook but not part of a published R package, (4) R scripts to produce virtually all of the figures in the book, and (5) solutions to the exercises as .html and .Rmd files. The suff ...

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.

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

This statistic illustrates the share of instruments adopted by small and medium-sized businesses in Italy in 2016. In 2016, only three percent of the respondents reported that there was no data software dedicated solutions because data analysis was outsourced.

Statistical methods for survival data analysis is a book. It was written by Elisa T. Lee and published by Wiley in 1992.

Exploratory data analysis.

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.

This paper is the first of a short series of articles aimed towards describing some of the various statistical methods and approaches that have been used in surface finishing. The methods fall broadly into two areas: analysis and design-of-experiments. This article introduces the subject, briefly reviewing the wide use of a number of experimental design tools in recent surface finishing research before starting with a discussion of parametric hypothesis testing, the simplest of the statistical methods.

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.

The global market for Regression Data Analysis Software is experiencing robust growth, driven by increasing adoption across diverse sectors. While precise figures for market size and CAGR are absent from the provided data, a reasonable estimation can be made based on industry trends. Considering the significant presence of established players like Microsoft, SAS, and IBM, along with the rising demand for data-driven decision-making across healthcare, BFSI (Banking, Financial Services, and Insurance), and other sectors, we can project a 2025 market size of approximately $5 billion. The compound annual growth rate (CAGR) is likely to be in the range of 8-10% for the forecast period (2025-2033), propelled by factors such as the growing volume of big data, advancements in machine learning algorithms integrated into regression analysis software, and the increasing need for sophisticated predictive modeling in various industries. The market segmentation reveals strong demand across applications, with healthcare and BFSI sectors demonstrating significant growth potential due to their reliance on risk assessment, fraud detection, and personalized medicine. The type segmentation highlights a preference for commercial analysis software, reflecting the need for robust features and scalable solutions in enterprise settings. Geographic expansion is expected across North America and Europe, while the Asia-Pacific region presents significant untapped potential for future growth. Competitive dynamics indicate a landscape dominated by established players, yet opportunities exist for specialized software providers catering to niche industry needs. Overall, the market outlook for Regression Data Analysis Software remains optimistic, supported by ongoing technological advancements and expanding data-driven applications across various sectors. The continued expansion of the Regression Data Analysis Software market is anticipated to be influenced by several key factors. The rising adoption of cloud-based solutions offers enhanced scalability and accessibility. Furthermore, increasing integration with other analytical tools and platforms allows for more comprehensive data analysis and reporting. This trend will likely be amplified by the continued growth in big data and the subsequent demand for advanced analytics capabilities. Government initiatives promoting data science and analytics are likely to fuel this trend, along with the growing need for predictive modeling in areas such as risk management, personalized medicine, and fraud detection. However, challenges remain, including the high cost of sophisticated software and the need for skilled professionals to effectively utilize such tools. Despite these restraints, the long-term outlook remains positive, with continued growth expected as the demand for data-driven decision-making transcends various sectors and geographies.

In the rapidly moving proteomics field, a diverse patchwork of data analysis pipelines and algorithms for data normalization and differential expression analysis is used by the community. We generated a mass spectrometry downstream analysis pipeline (MS-DAP) that integrates both popular and recently developed algorithms for normalization and statistical analyses. Additional algorithms can be easily added in the future as plugins. MS-DAP is open-source and facilitates transparent and reproducible proteome science by generating extensive data visualizations and quality reporting, provided as standardized PDF reports. Second, we performed a systematic evaluation of methods for normalization and statistical analysis on a large variety of data sets, including additional data generated in this study, which revealed key differences. Commonly used approaches for differential testing based on moderated t-statistics were consistently outperformed by more recent statistical models, all integrated in MS-DAP. Third, we introduced a novel normalization algorithm that rescues deficiencies observed in commonly used normalization methods. Finally, we used the MS-DAP platform to reanalyze a recently published large-scale proteomics data set of CSF from AD patients. This revealed increased sensitivity, resulting in additional significant target proteins which improved overlap with results reported in related studies and includes a large set of new potential AD biomarkers in addition to previously reported.

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.

ITS data collected as part of Comparison of statistical methods used to meta-analyse results from interrupted time series studies: an empirical study. Code used to analyse the ITS studies.

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.