Top 15 Best Statistical Software (Free) in 2022 (Statistics and Data Analysis)

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What Are Statistical Software?

Statistical software, or statistical analysis software, refers to tools that assist in the statistics-based collection and analysis of data to provide science-based insights into patterns and trends. They often use statistical analysis theorems and methodologies, such as regression analysis and time series analysis to perform data science.

List of Top 15 Best Statistical Software Free in 2022 (Statistics and Data Analysis)

(1) SAS® software

Whether you’re a professor, teacher, student or independent learner, you can get easy access to powerful SAS software via the cloud. Setup is easy, too. After you get set up, a broadband internet connection is all you’ll need to run the best analytics software in the world.

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PSPP is a stable and reliable application. It can perform descriptive statistics, T-tests, anova, linear and logistic regression, measures of association, cluster analysis, reliability and factor analysis, non-parametric tests and more. Its backend is designed to perform its analyses as fast as possible, regardless of the size of the input data. You can use PSPP with its graphical interface or the more traditional syntax commands.

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(3) Statistical Lab

The Statistical Lab is an explorative and interactive tool designed both to support education in statistics and provide a tool for the simulation and solution of statistical problems. The graphical user interface is designed to make complex statistical relations easy to understand. It connects and displays data frames, frequency tables, random numbers or matrixes in a user-friendly statistical worksheet allowing users to run calculations, conduct analyses and perform multiple simulations and manipulations.

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(4) Develve Statistical Analysis Software

Statistical software for fast and easy interpretation of experimental data in science and R&D in a technical environment. This statistical package helps with analysis and prevents making false assumptions. In short it makes statistics faster and easier, suitable for less experience users but advanced enough for more demanding users.

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(5) IBM SPSS Statistical Analysis Software

IBM® SPSS® Statistics is a powerful statistical software platform. It offers a user-friendly interface and a robust set of features that lets your organization quickly extract actionable insights from your data. Advanced statistical procedures help ensure high accuracy and quality decision making. All facets of the analytics lifecycle are included, from data preparation and management to analysis and reporting.

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(6) Stata: Statistical software for data science

Fast. Accurate. Easy to use. Stata is a complete, integrated software package that provides all your data science needs—data manipulation, visualization, statistics, and automated reporting.

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(7) Minitab: Data Analysis, Statistical & Process Improvement Tools

Data is everywhere, but are you truly taking advantage of yours? Minitab Statistical Software can look at current and past data to discover trends, find and predict patterns, uncover hidden relationships between variables, and create stunning visualizations to tackle even the most daunting challenges and opportunities. With powerful statistics, industry-leading data analytics, and dynamic visualizations on your side, the possibilities are endless.

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(8) GraphPad Prism

A versatile statistics tool purpose-built for scientists-not statisticians. Get a head start by entering data into tables that are structured for scientific research and guide you to statistical analyses that streamline your research workflow. No coding required.

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(9) DataMelt

DataMelt is a free software for numeric computation, mathematics, statistics, symbolic calculations, data analysis and data visualization. This multiplatform program combines the simplicity of scripting languages, such as Python, Ruby, Groovy (and others), with the power of hundreds of Java packages.

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(10) GNU Octave

Powerful mathematics-oriented syntax with built-in 2D/3D plotting and visualization tools. Free software, runs on GNU/Linux, macOS, BSD, and Microsoft Windows. Drop-in compatible with many Matlab scripts

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(11) SOFA Statistics

SOFA is a user-friendly statistics, analysis, & reporting program. It is free, with an emphasis on ease of use, learn as you go, and beautiful output. SOFA lets you display results in an attractive format ready to share. And SOFA will help you learn as you go.

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(12) Dataplot

Dataplot® is a free, public-domain, multi-platform (Unix/Linux, MacOS, Windows 7/8/10) software system for scientific visualization, statistical analysis, and non-linear modeling. The target Dataplot user is the researcher and analyst engaged in the characterization, modeling, visualization, analysis, monitoring, and optimization of scientific and engineering processes.

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(13) SCIPY

SciPy is a set of open source (BSD licensed) scientific and numerical tools for Python. It currently supports special functions, integration, ordinary differential equation (ODE) solvers, gradient optimization, parallel programming tools, an expression-to-C++ compiler for fast execution, and others. A good rule of thumb is that if it’s covered in a general textbook on numerical computing (for example, the well-known Numerical Recipes series), it’s probably implemented in SciPy.

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(14) Zelig

Zelig is an easy-to-use, free, open source, general purpose statistics program for estimating, interpreting, and presenting results from any statistical method. Zelig turns the power of R, with thousands of open source packages — but with free ranging syntax, diverse examples, and documentation written for different audiences — into the same three commands and consistent documentation for every method.

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(15) MacAnova

MacAnova is a free, open source, interactive statistical analysis program for Windows, Macintosh, and Linux written by Gary W. Oehlert and Christopher Bingham, both of the School of Statistics, University of Minnesota. In spite of its name, MacAnova is not just for Macintosh computers and not just for doing Analysis of Variance.

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