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 ***** percent. It was followed by Riskalyze Elite, with ***** percent, and YCharts, with ***** percent.

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This book is written for statisticians, data analysts, programmers, researchers, teachers, students, professionals, and general consumers on how to perform different types of statistical data analysis for research purposes using the R programming language. R is an open-source software and object-oriented programming language with a development environment (IDE) called RStudio for computing statistics and graphical displays through data manipulation, modelling, and calculation. R packages and supported libraries provides a wide range of functions for programming and analyzing of data. Unlike many of the existing statistical softwares, R has the added benefit of allowing the users to write more efficient codes by using command-line scripting and vectors. It has several built-in functions and libraries that are extensible and allows the users to define their own (customized) functions on how they expect the program to behave while handling the data, which can also be stored in the simple object system.For all intents and purposes, this book serves as both textbook and manual for R statistics particularly in academic research, data analytics, and computer programming targeted to help inform and guide the work of the R users or statisticians. It provides information about different types of statistical data analysis and methods, and the best scenarios for use of each case in R. It gives a hands-on step-by-step practical guide on how to identify and conduct the different parametric and non-parametric procedures. This includes a description of the different conditions or assumptions that are necessary for performing the various statistical methods or tests, and how to understand the results of the methods. The book also covers the different data formats and sources, and how to test for reliability and validity of the available datasets. Different research experiments, case scenarios and examples are explained in this book. It is the first book to provide a comprehensive description and step-by-step practical hands-on guide to carrying out the different types of statistical analysis in R particularly for research purposes with examples. Ranging from how to import and store datasets in R as Objects, how to code and call the methods or functions for manipulating the datasets or objects, factorization, and vectorization, to better reasoning, interpretation, and storage of the results for future use, and graphical visualizations and representations. Thus, congruence of Statistics and Computer programming for Research.

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 ************* by Black Ink. As of *************, * 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.

Statistical Analysis Software Market Outlook

The global market size for statistical analysis software was estimated at USD 11.3 billion in 2023 and is projected to reach USD 21.6 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.5% during the forecast period. This substantial growth can be attributed to the increasing complexity of data in various industries and the rising need for advanced analytical tools to derive actionable insights.

One of the primary growth factors for this market is the increasing demand for data-driven decision-making across various sectors. Organizations are increasingly recognizing the value of data analytics in enhancing operational efficiency, reducing costs, and identifying new business opportunities. The proliferation of big data and the advent of technologies such as artificial intelligence and machine learning are further fueling the demand for sophisticated statistical analysis software. Additionally, the growing adoption of cloud computing has significantly reduced the cost and complexity of deploying advanced analytics solutions, making them more accessible to organizations of all sizes.

Another critical driver for the market is the increasing emphasis on regulatory compliance and risk management. Industries such as finance, healthcare, and manufacturing are subject to stringent regulatory requirements, necessitating the use of advanced analytics tools to ensure compliance and mitigate risks. For instance, in the healthcare sector, statistical analysis software is used for clinical trials, patient data management, and predictive analytics to enhance patient outcomes and ensure regulatory compliance. Similarly, in the financial sector, these tools are used for fraud detection, credit scoring, and risk assessment, thereby driving the demand for statistical analysis software.

The rising trend of digital transformation across industries is also contributing to market growth. As organizations increasingly adopt digital technologies, the volume of data generated is growing exponentially. This data, when analyzed effectively, can provide valuable insights into customer behavior, market trends, and operational efficiencies. Consequently, there is a growing need for advanced statistical analysis software to analyze this data and derive actionable insights. Furthermore, the increasing integration of statistical analysis tools with other business intelligence and data visualization tools is enhancing their capabilities and driving their adoption across various sectors.

From a regional perspective, North America currently holds the largest market share, driven by the presence of major technology companies and a high level of adoption of advanced analytics solutions. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, owing to the increasing adoption of digital technologies and the growing emphasis on data-driven decision-making in countries such as China and India. The region's rapidly expanding IT infrastructure and increasing investments in advanced analytics solutions are further contributing to this growth.

Component Analysis

The statistical analysis software market can be segmented by component into software and services. The software segment encompasses the core statistical analysis tools and platforms used by organizations to analyze data and derive insights. This segment is expected to hold the largest market share, driven by the increasing adoption of data analytics solutions across various industries. The availability of a wide range of software solutions, from basic statistical tools to advanced analytics platforms, is catering to the diverse needs of organizations, further driving the growth of this segment.

The services segment includes consulting, implementation, training, and support services provided by vendors to help organizations effectively deploy and utilize statistical analysis software. This segment is expected to witness significant growth during the forecast period, driven by the increasing complexity of data analytics projects and the need for specialized expertise. As organizations seek to maximize the value of their data analytics investments, the demand for professional services to support the implementation and optimization of statistical analysis solutions is growing. Furthermore, the increasing trend of outsourcing data analytics functions to third-party service providers is contributing to the growth of the services segment.

Within the software segment, the market can be further categori

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Comparison of features in SDA-V2 and well-known statistical analysis software packages (Minitab and SPSS).

The biostatistics software market is experiencing robust growth, driven by the increasing adoption of data-driven approaches in pharmaceutical research, clinical trials, and academic studies. The market, valued at approximately $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This expansion is fueled by several key factors. Firstly, the rising volume of complex biological data necessitates sophisticated software solutions for analysis and interpretation. Secondly, advancements in machine learning and artificial intelligence are enhancing the capabilities of biostatistics software, enabling more accurate and efficient data processing. Thirdly, regulatory pressures demanding robust data analysis in the pharmaceutical and healthcare sectors are boosting demand for validated and compliant biostatistics tools. The market is segmented by software type (general-purpose versus specialized) and end-user (pharmaceutical companies, academic institutions, and others). Pharmaceutical companies represent a significant portion of the market due to their extensive reliance on clinical trial data analysis. However, the academic and research segments are also exhibiting strong growth due to increased research activities and funding. Geographically, North America and Europe currently dominate the market, but Asia-Pacific is expected to witness substantial growth in the coming years due to increasing healthcare spending and technological advancements in the region. The competitive landscape is characterized by a mix of established players offering comprehensive suites and specialized niche vendors. While leading players like IBM SPSS Statistics and Minitab enjoy significant market share based on their brand recognition and established user bases, smaller companies specializing in specific statistical methods or user interfaces are gaining traction by catering to niche demands. This competitive dynamic will likely drive innovation and further segmentation within the market, resulting in specialized software offerings tailored to particular research areas and user requirements. The challenges the market faces include the high cost of software licensing, the need for specialized training for effective utilization, and the potential integration complexities with existing data management systems. However, the overall growth trajectory remains positive, driven by the inherent need for sophisticated biostatistical analysis in various sectors.

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IntroductionA required step for presenting results of clinical studies is the declaration of participants demographic and baseline characteristics as claimed by the FDAAA 801. The common workflow to accomplish this task is to export the clinical data from the used electronic data capture system and import it into statistical software like SAS software or IBM SPSS. This software requires trained users, who have to implement the analysis individually for each item. These expenditures may become an obstacle for small studies. Objective of this work is to design, implement and evaluate an open source application, called ODM Data Analysis, for the semi-automatic analysis of clinical study data.MethodsThe system requires clinical data in the CDISC Operational Data Model format. After uploading the file, its syntax and data type conformity of the collected data is validated. The completeness of the study data is determined and basic statistics, including illustrative charts for each item, are generated. Datasets from four clinical studies have been used to evaluate the application’s performance and functionality.ResultsThe system is implemented as an open source web application (available at https://odmanalysis.uni-muenster.de) and also provided as Docker image which enables an easy distribution and installation on local systems. Study data is only stored in the application as long as the calculations are performed which is compliant with data protection endeavors. Analysis times are below half an hour, even for larger studies with over 6000 subjects.DiscussionMedical experts have ensured the usefulness of this application to grant an overview of their collected study data for monitoring purposes and to generate descriptive statistics without further user interaction. The semi-automatic analysis has its limitations and cannot replace the complex analysis of statisticians, but it can be used as a starting point for their examination and reporting.

The global Multivariate Analysis Software market is poised for significant expansion, projected to reach an estimated market size of USD 4,250 million in 2025, with a robust Compound Annual Growth Rate (CAGR) of 12.5% anticipated through 2033. This growth is primarily fueled by the increasing adoption of advanced statistical techniques across a wide spectrum of industries, including the burgeoning pharmaceutical sector, sophisticated chemical research, and complex manufacturing processes. The demand for data-driven decision-making, coupled with the ever-growing volume of complex datasets, is compelling organizations to invest in powerful analytical tools. Key drivers include the rising need for predictive modeling in drug discovery and development, quality control in manufacturing, and risk assessment in financial applications. Emerging economies, particularly in the Asia Pacific region, are also contributing to this upward trajectory as they invest heavily in technological advancements and R&D, further amplifying the need for sophisticated analytical solutions. The market is segmented by application into Medical, Pharmacy, Chemical, Manufacturing, and Marketing. The Pharmacy and Medical applications are expected to witness the highest growth owing to the critical need for accurate data analysis in drug efficacy studies, clinical trials, and personalized medicine. In terms of types, the market encompasses a variety of analytical methods, including Multiple Linear Regression Analysis, Multiple Logistic Regression Analysis, Multivariate Analysis of Variance (MANOVA), Factor Analysis, and Cluster Analysis. While advanced techniques like MANOVA and Factor Analysis are gaining traction for their ability to uncover intricate relationships within data, the foundational Multiple Linear and Logistic Regression analyses remain widely adopted. Restraints, such as the high cost of specialized software and the need for skilled personnel to effectively utilize these tools, are being addressed by the emergence of more user-friendly interfaces and cloud-based solutions. Leading companies like Hitachi High-Tech America, OriginLab Corporation, and Minitab are at the forefront, offering comprehensive suites that cater to diverse analytical needs. This report provides an in-depth analysis of the global Multivariate Analysis Software market, encompassing a study period from 2019 to 2033, with a base and estimated year of 2025 and a forecast period from 2025 to 2033, building upon historical data from 2019-2024. The market is projected to witness significant expansion, driven by increasing data complexity and the growing need for advanced analytical capabilities across various industries. The estimated market size for Multivariate Analysis Software is expected to reach $2.5 billion by 2025, with projections indicating a substantial growth to $5.8 billion by 2033, demonstrating a robust compound annual growth rate (CAGR) of approximately 11.5% during the forecast period.

Data Analysis is the process that supports decision-making and informs arguments in empirical studies. Descriptive statistics, Exploratory Data Analysis (EDA), and Confirmatory Data Analysis (CDA) are the approaches that compose Data Analysis (Xia & Gong; 2014). An Exploratory Data Analysis (EDA) comprises a set of statistical and data mining procedures to describe data. We ran EDA to provide statistical facts and inform conclusions. The mined facts allow attaining arguments that would influence the Systematic Literature Review of DL4SE.

The Systematic Literature Review of DL4SE requires formal statistical modeling to refine the answers for the proposed research questions and formulate new hypotheses to be addressed in the future. Hence, we introduce DL4SE-DA, a set of statistical processes and data mining pipelines that uncover hidden relationships among Deep Learning reported literature in Software Engineering. Such hidden relationships are collected and analyzed to illustrate the state-of-the-art of DL techniques employed in the software engineering context.

Our DL4SE-DA is a simplified version of the classical Knowledge Discovery in Databases, or KDD (Fayyad, et al; 1996). The KDD process extracts knowledge from a DL4SE structured database. This structured database was the product of multiple iterations of data gathering and collection from the inspected literature. The KDD involves five stages:

Selection. This stage was led by the taxonomy process explained in section xx of the paper. After collecting all the papers and creating the taxonomies, we organize the data into 35 features or attributes that you find in the repository. In fact, we manually engineered features from the DL4SE papers. Some of the features are venue, year published, type of paper, metrics, data-scale, type of tuning, learning algorithm, SE data, and so on.

Preprocessing. The preprocessing applied was transforming the features into the correct type (nominal), removing outliers (papers that do not belong to the DL4SE), and re-inspecting the papers to extract missing information produced by the normalization process. For instance, we normalize the feature “metrics” into “MRR”, “ROC or AUC”, “BLEU Score”, “Accuracy”, “Precision”, “Recall”, “F1 Measure”, and “Other Metrics”. “Other Metrics” refers to unconventional metrics found during the extraction. Similarly, the same normalization was applied to other features like “SE Data” and “Reproducibility Types”. This separation into more detailed classes contributes to a better understanding and classification of the paper by the data mining tasks or methods.

Transformation. In this stage, we omitted to use any data transformation method except for the clustering analysis. We performed a Principal Component Analysis to reduce 35 features into 2 components for visualization purposes. Furthermore, PCA also allowed us to identify the number of clusters that exhibit the maximum reduction in variance. In other words, it helped us to identify the number of clusters to be used when tuning the explainable models.

Data Mining. In this stage, we used three distinct data mining tasks: Correlation Analysis, Association Rule Learning, and Clustering. We decided that the goal of the KDD process should be oriented to uncover hidden relationships on the extracted features (Correlations and Association Rules) and to categorize the DL4SE papers for a better segmentation of the state-of-the-art (Clustering). A clear explanation is provided in the subsection “Data Mining Tasks for the SLR od DL4SE”. 5.Interpretation/Evaluation. We used the Knowledge Discover to automatically find patterns in our papers that resemble “actionable knowledge”. This actionable knowledge was generated by conducting a reasoning process on the data mining outcomes. This reasoning process produces an argument support analysis (see this link).

We used RapidMiner as our software tool to conduct the data analysis. The procedures and pipelines were published in our repository.

Overview of the most meaningful Association Rules. Rectangles are both Premises and Conclusions. An arrow connecting a Premise with a Conclusion implies that given some premise, the conclusion is associated. E.g., Given that an author used Supervised Learning, we can conclude that their approach is irreproducible with a certain Support and Confidence.

Support = Number of occurrences this statement is true divided by the amount of statements Confidence = The support of the statement divided by the number of occurrences of the premise

It is a widely accepted fact that evolving software systems change and grow. However, it is less well-understood how change is distributed over time, specifically in object oriented software systems. The patterns and techniques used to measure growth permit developers to identify specific releases where significant change took place as well as to inform them of the longer term trend in the distribution profile. This knowledge assists developers in recording systemic and substantial changes to a release, as well as to provide useful information as input into a potential release retrospective. However, these analysis methods can only be applied after a mature release of the code has been developed. But in order to manage the evolution of complex software systems effectively, it is important to identify change-prone classes as early as possible. Specifically, developers need to know where they can expect change, the likelihood of a change, and the magnitude of these modifications in order to take proactive steps and mitigate any potential risks arising from these changes. Previous research into change-prone classes has identified some common aspects, with different studies suggesting that complex and large classes tend to undergo more changes and classes that changed recently are likely to undergo modifications in the near future. Though the guidance provided is helpful, developers need more specific guidance in order for it to be applicable in practice. Furthermore, the information needs to be available at a level that can help in developing tools that highlight and monitor evolution prone parts of a system as well as support effort estimation activities. The specific research questions that we address in this chapter are: (1) What is the likelihood that a class will change from a given version to the next? (a) Does this probability change over time? (b) Is this likelihood project specific, or general? (2) How is modification frequency distributed for classes that change? (3) What is the distribution of the magnitude of change? Are most modifications minor adjustments, or substantive modifications? (4) Does structural complexity make a class susceptible to change? (5) Does popularity make a class more change-prone? We make recommendations that can help developers to proactively monitor and manage change. These are derived from a statistical analysis of change in approximately 55000 unique classes across all projects under investigation. The analysis methods that we applied took into consideration the highly skewed nature of the metric data distributions. The raw metric data (4 .txt files and 4 .log files in a .zip file measuring ~2MB in total) is provided as a comma separated values (CSV) file, and the first line of the CSV file contains the header. A detailed output of the statistical analysis undertaken is provided as log files generated directly from Stata (statistical analysis software).

The global Statistics Software market is projected to experience robust growth, with a current estimated market size of approximately $4,500 million in 2025 and an anticipated Compound Annual Growth Rate (CAGR) of around 11% during the forecast period of 2025-2033. This expansion is significantly driven by the increasing demand for advanced analytical capabilities across various sectors. The Scientific Research segment is a primary beneficiary, leveraging statistical software for hypothesis testing, data modeling, and drawing conclusive insights from complex datasets. In parallel, the Finance industry is witnessing a surge in adoption, fueled by the need for sophisticated tools for risk assessment, algorithmic trading, fraud detection, and predictive modeling. Industrial applications are also contributing to market growth, with businesses utilizing statistical software for quality control, process optimization, and supply chain management to enhance efficiency and reduce operational costs. Emerging economies, particularly in the Asia Pacific region, are becoming crucial growth hubs due to increased investment in data analytics and a growing pool of skilled professionals. Key trends shaping the Statistics Software market include the rise of cloud-based solutions, offering greater accessibility, scalability, and cost-effectiveness for businesses of all sizes. Furthermore, the integration of artificial intelligence (AI) and machine learning (ML) within statistical software is a significant development, enabling automated data analysis, pattern recognition, and more accurate forecasting. While the market is poised for substantial growth, certain restraints could temper this trajectory. The high cost of advanced statistical software and the need for specialized expertise to effectively utilize its full potential can be prohibitive for smaller organizations. Data privacy and security concerns, especially with the increasing volume of sensitive data being processed, also pose a challenge, necessitating robust security features and compliance with evolving regulations. However, the continuous innovation by leading companies like Microsoft, IBM, and SAS Institute, alongside emerging players like RapidMiner and Knime, in developing user-friendly interfaces and powerful analytical tools, is expected to mitigate these restraints and propel the market forward.

Additional file 1: AF_table1.docx Graphic user interfaces and statistical methods in MEPHAS; AF_table2.docx Comparison of methods in MEPHAS with EZR, FSFS, and Radiant; AF_list.docx R packages used in MEPHAS; AF_result.docx The results in Example 1 and Example 2.