Statistical analysis softwares (with top 9 list)-Updated 2022
Home » Statistical analysis softwares (with top 9 list)-Updated 2022
What is statistical analysis software?
Statistical software is a program or package that carries out various calculations, analyses, and reporting tasks using methods and concepts of statistics. It is nearly impossible to perform calculations manually beyond a small data set. Additionally, manual statistical analysis requires a lot of time, and there are high chances of errors.
Statistical software programs are an integral part of almost every academic and commercial field today and are growing in importance with time. Engineering firms, health sciences, research, marketing, and every conceivable modern-day profession heavily rely on statistics for operations, competitive advantage, and profitability. Statistical software plays a crucial role in decision making, supporting the management to identify significant trends and make strategies based on projections for the future.
The statistical analysis tools are helpful for:
With vast volumes of input data available from multiple sources, the statistical package must validate the data points to minimize errors and inaccuracies in the downstream processes. This process includes checking data formats and removing outliers, among other steps.
It is the most widely used feature of statistics and, consequently, statistical software programs. Statistical calculations and analysis involve simple concepts like mean, median, and mode to complex ones like multiple linear regression. The appropriate usage of these concepts results in the data that feeds a lot of information flow, business intelligence, and, finally, decisions.
The statistical software can use historical data and trends to predict the weather, sales, commodity prices, GDP, stock market volatility, etc. The forecasting model generally uses one or more of the following techniques: time series analysis and projection, qualitative techniques, and causal models.
Statistical packages offer various types of data visualization options. The basic ones generate simple graphics and have fewer options for customization. On the other hand, the most advanced ones have interactive visualizations and provide powerful and personalized graphical outputs. This variation can become an important selection criterion.
Organizations and businesses use business intelligence driven by statistical methods and algorithms to plan for the future and form and modify a detailed roadmap. The BI features enable top management and analysts to analyze the company’s performance and compare it to that of its competitors.
Capabilities that make statistical software indispensable
Processing large amounts of data
There is no shortage of data in today’s technology-driven era. These software tools help crunch massive volumes of data. A capable statistical program can handle it easily when the data points run into millions, billions, or even more.
Accuracy with efficiency
Specialized computer programs can perform statistical calculations efficiently without errors. As an organization’s operations and decision-making processes depend on these factors, the importance of the speed and reliability that these analytical tools provide can’t be overstated.
Advanced presentation features
Statistical analysis packages come with visual dashboards and interactive graphical capabilities to present at-a-glance results and drill-down analyses. It makes for quick and efficient analysis and decision-making based on data.
Factors in choosing a statistical software
Statistics analytical tools range from free software to massive fees or prices per license. It is vital to budget for the statistical software and to choose it accordingly to obtain maximum value for money.
For an individual, the software should run on the computer operating system. It must easily integrate with the existing hardware and software ecosystem like the ERP and CRM solutions for organizations and businesses.
It is one of the most important but often underestimated aspects of selecting a statistical software. At the enterprise level, switching from one software package to another can cost a considerable amount. It, thus, makes sense to perform the due diligence required to select the software in line with the projected growth in function and scale.
Ease of use
If the users of a statistical software find it difficult to use, it would likely result in low motivation and performance. Additionally, the training cost for complicated software can be a big part of the total cost of ownership.
As the features offered by different statistical suites vary in capabilities and application, a careful evaluation of their suitability to the nature of business and its intricacies is a must-do exercise.
Some popular statistical software is more suited to a particular domain or industry. It is so due to a design that leans heavily towards providing analysis and functionality specific to one or more industries.
Reliable support by the software company is a much-desired factor in selecting any product. For an individual, it translates to peace of mind. For businesses, it ensures business continuity and minimal disruption of operations. An extended warranty period or free support duration is always a bonus.
List of the best & most popular statistical softwares
Statistical Package for the Social Sciences (SPSS) is a statistical software suite for data management, multivariate analysis, advanced analytics, criminal investigation, and business intelligence.It was initially produced by SPSS Inc. As IBM acquired it in 2009, and the current versions carry the brand name IBM SPSS. It runs on Windows, macOS, and Linux.
It is popular for its applications in social sciences. Its predictive analysis helps businesses understand consumer behavior patterns and creates business models that strengthen relationships and interaction.
Statistical Analysis Software (SAS) is a world-class tool from SAS Institute Inc. It is a leader in data management, analytics, and artificial intelligence. Its analytical capabilities range from exploratory data analysis to visualization techniques of statistics. SAS also supports a Free University Edition, which is downloadable by everyone for non-commercial use.
The SAS software suite boasts more than 200 components. Some of the most popular SAS components include SAS/OR for operations research, Enterprise Miner for data mining, SAS/QC for quality control, and SAS/PH for clinical trials. Banking, health care, education, agriculture, retail and consumer goods, and the public sector are a few among the many industries that use the SAS software suite.
Researchers Thomas A. Ryan, Jr., Barbara F. Ryan, and Brian L. Joiner from Pennsylvania State University developed the statistics software Minitab in 1972. Minitab, LLC, a privately-owned company headquartered in State College, Pennsylvania, distributes it now. Minitab is popular in the manufacturing industry that uses it to enhance productivity, perform quality control, track progress metrics, and build and maintain partnerships.
Other software tools work seamlessly and have high compatibility with Minitab. Quality Trainer is an eLearning package that has statistics tutorials for users. Businesses can organize and centralize their data using Minitab Connect, and Minitab Workspace has visualization and project planning tools. Minitab Engage is a tool for managing lean manufacturing deployments and Six Sigma and idea and innovation management.
It is one of the most popular statistics software on the market. The software is easy to use and offers various tools for simple statistics and data visualization. MS Excel is available in a local or cloud-based version. It runs on Microsoft Windows, Mac, iOS, and Android operating systems.
Individual users and small and medium businesses use it to create metrics, customized graphs, and charts. Its user-friendly interface containing cells, columns, and rows, a straightforward sort menu, and filter and display features are some reasons for its popularity.
R is a free statistical programming language that supports statistical computing and graphics. It can run on Windows and macOS and a variety of UNIX platforms. Statisticians and data miners use it for data analysis and to develop statistical software. Users can create packages to boost the functions of the R language further. It is helpful for a user to have some knowledge of programming.
It is a programming language and analytical platform by MathWorks, which is popular among scientists and engineers for analyzing data and creating models by developing algorithms. Like R, it is not the most user-friendly statistical software, and it helps if the user has a background in programming to make the most of it.
Matlab is the program of choice for statistics education at more than 4000 universities and colleges worldwide. It can also provide help for statistical researchers. It supports machine learning, deep learning, signal processing and communications, control systems, image and video processing, computational finance, test and measurement, and computational biology.
JMP is a suite of statistical analysis programs for JMP, a subsidiary of SAS Institute. It focuses on analysis, discovery, and innovation through its easy-to-use but powerful dynamic linking, data visualization, and analytic capabilities. It gives the user flexibility and control over data analysis and manipulation.
Stata is a general-use statistical software package by StataCorp for data analysis, visualization, and automated reporting. It is popular among researchers in many fields, like sociology, epidemiology, biomedicine, and science.
NVivo is a qualitative data analysis (QDA) software by QSR International. It helps qualitative researchers to prepare, analyze and look for insights in qualitative or unstructured data, namely, open-ended survey responses, interviews, journal articles, social media, and web content. It is useful where data isn’t necessarily a number or a mathematical value.
NVivo is mainly useful for government, academic, commercial, and health researchers in various fields, including tourism, marketing, forensics, and social sciences such as psychology, communication, anthropology, and sociology.
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To see the complete list of statistical softwares, visit wikipedia.
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