Top Free Statistical Analysis Software

Check out our list of free Statistical Analysis Software. Products featured on this list are the ones that offer a free trial version. As with most free versions, there are limitations, typically time or features.

If you'd like to see more products and to evaluate additional feature options, compare all Statistical Analysis Software to ensure you get the right product.

(722)4.2 out of 5
Optimized for quick response
Entry Level Price:$99 USD per user per month

IBM SPSS Statistics software is used by a variety of customers to solve industry-specific business issues to drive quality decision-making.  Advanced statistical procedures and visualization can provide a robust, user friendly and an integrated platform to understand your data and solve complex business and research problems •Addresses all facets of the analytical process from data preparation and management to analysis and reporting •Provides tailored functionality and customizable interfaces

UH
Easy to use interface, simple point, and click for statistical analyses. Read review
Kenton W.
KW
Personally, I like the ease of use. Being able to select the type of statistical analysis I want to run and then pasting the syntax is incredibly... Read review
(504)4.5 out of 5

The primary mission of RStudio is to build a sustainable open-source business that creates software for data science and statistical computing. You may have already heard of some of our work, such as the RStudio IDE, Rmarkdown, shiny, and many packages in the tidyverse. Our open source projects are supported by our commercial products that help teams of R users work together effectively, share computing resources, and publish their results to decision makers within the organization. We also bui

UH
Commands can be run line by line, which makes it convenient find errors and correct them. The program environment is friendly. Ability to save... Read review
Anoj A.
AA
I have been using RStudio for more than two years. I feel awesome to play with its scripts. I feel good to learn new things every time. For me... Read review
(62)4.6 out of 5

Origin is a user-friendly and easy-to-learn software application that provides data analysis and publication-quality graphing capabilities tailored to the needs of scientists and engineers. OriginPro offers extended analysis tools for Peak Fitting, Surface Fitting, Statistics, Signal Processing and Image Handling. Users can customize operations such as importing, graphing and analysis, all from the GUI. Graphs, analysis results and reports update automatically when data or parameters change. Thi

UM
Free trial. Include statistics, signal processing, curve fitting and peak analysis in just one software. imports data files in various... Read review
NS
My Ph.D is depend on OriginPro. As a science student all plots are done with the help of it. One the most liking part is the user defined fitting... Read review
(31)4.9 out of 5

Since 2007, we are creating the most powerful framework to push the barriers of analytics, predictive analytics, AI and Big Data, while offering a helpful, fast and friendly environment. The TIMi Suite consists of four tools: 1. Anatella (Analytical ETL & Big Data), 2. Modeler (Auto-ML / Automated Predictive Modelling / Automated-AI), 3. StarDust (3D Segmentation) 4. Kibella (BI Dashboarding solution).

JF
I can automate giant process in order to execute it at any time Read review
Julián Felipe D.
JD
- The graphic modules implemented make a great UI, instead of only code based. - The flexibility to make changes in ETL's already working. - Really... Read review
(19)4.7 out of 5
Entry Level Price:500 USD

NumXL is a suite of time series Excel add-ins. It transforms your Microsoft Excel application into a first-class time series software and econometrics tool, offering the kind of statistical accuracy offered by the far more expensive statistical packages. NumXL integrates natively with Excel, adding scores of econometric functions, a rich set of shortcuts, and intuitive user interfaces to guide you through the entire process. NumXL comes packed with scores of functions that you can easily access

Nigel C.
NC
I really enjoy the tutorials and education that the NumXL team puts together. More examples are always appreciated to fully appreciate these... Read review
VT
Great product with fantastic customer services. User Friendliness is the foremost strength I reckon. Its functions are intuitive and easy to... Read review
0 ratings
Entry Level Price:$3 month

Number Analytics is a web based statistical software for business analytics applications. We are based in New York City starting in 2013. Our target customers are business users and students who want to learn statistical analysis for marketing research and social science research. It is based on a menu based user interface with statistical results explained in plain english. It's affordable with basic plan ($3 per month) with advanced ($30 per month) and premium plan ($300). It supports PC, Mac,

Affordable, easy to use add-in for Excel that creates control charts, histograms, Paretos, and more. Your data is already in Excel, shouldn't your SPC software be there too? QI Macros is an all-in-one toolkit used by quality and process improvement professionals for more than 20 years. In addition to drawing charts you can run statistical tests, create fishbones and value stream maps, and perform complex analysis like Gage R&R and DOE. You'll save time and look like an expert.

Top 7 Free Statistical Analysis Software in 2021

  • IBM SPSS Statistics
  • RStudio
  • OriginPro
  • TIMi Suite
  • NumXL

Learn More About Statistical Analysis Software

What You Should Know About Statistical Analysis Software

Enterprises have started to recognize the importance of accessing and combining all business-critical data to get a better understanding of how a company performs. Connecting disparate systems to make data consumable is a complex process, but once done, businesses are enabled to utilize statistical analytics solutions along with other data analysis techniques. This helps them visualize and interpret the data in an easily digestible manner. A business cannot transform into a digitally native enterprise until it uses its data to make intelligent decisions. These data-driven decisions provide an important edge and help separate a business from its competitors. Statistical analytics tools ultimately inform such data-driven decisions.

Medium and large companies are facing unprecedented challenges when managing and analyzing data. Some of these challenges are the exponential growth of the volume of enterprise data and its increasing complexity caused by new technologies like social media and the internet of things (IoT). To address these challenges, companies need to gather and analyze large volumes of data, both structured and unstructured, from different sources. Companies need to find trends and patterns in historical data while identifying future opportunities and risks. They also cannot afford to spend a lot of time analyzing data which loses its relevance quickly. Advanced statistical procedures and visualization provide a robust, user-friendly, and integrated platform to understand business data and solve complex business and research problems.

Key Benefits of Statistical Analysis Software

  • Allows businesses to ensure that data is analyzed properly and the results are conclusive and unbiased
  • Enables users to customize analysis to dig deep into the data and perform complex statistical analysis, such as regression, predictive analytics, and time series analysis
  • Gives users the ability to analyze cause and effect between variables

Why Use Statistical Analysis Software?

Irrespective of the type of complex data manipulation or analysis being performed, statistical analysis tools enable statisticians or business professionals to find correlation, regression, analysis of variance, and more with ease. These tools help identify duplicates and unusual cases that may arise during data cleaning and data curation and get detailed data analysis reports.

Manage large volumes of data — Statistical analysis software gives one the ability to easily prepare, blend, and analyze company data using a repeatable workflow, then deploy and share analytics at scale for deeper insights.

Gain insight into company data — Through data mining and statistical analysis, especially when coupled with other technology such as machine learning, data professionals are able to gain insights into data.

Better understand potential outcomes and scenarios — With statistical analysis, especially of the predictive variety, businesses better understand how different variables will affect them and are able to plan accordingly.

Who Uses Statistical Analysis Software?

In the digital age, data is ubiquitous. Whether it is higher education or the healthcare industry, it is likely that a lot of data is involved. Statistical analysis software tools are typically used by data scientists and mathematicians, but it provides industry-specific features. These may be features tailored to scientific research, cost modeling, or health science.

Higher education — Users from the higher education industry use statistical analysis tools to analyze data, obtain insights quickly, and find relevant trends in data.

Research — Researchers turn to statistical analysis to solve tough research problems. They are able to use these tools to dig into their research findings and find historical trends. With statistical analysis software, market researchers are enabled to handle a large volume of data in multiple formats.

Health care— For health care professionals, time isn’t just money—it also means the difference between life and death. These workers look to statistical tools to better understand their health-related data and to improve efficiencies in hospitals, manage and contain diseases, and much more.

Kinds of Statistical Analysis Software

Statistical analysis comes in many different flavors with each type being appropriate or beneficial for different use cases. Although the methods are many, the ultimate goal of analysis is broken into three types: descriptive, prescriptive, and predictive. Descriptive refers to analysis which simply looks at the data as it is, but does not refer or recommend any future outcome. Prescriptive refers to analysis which uses historical data to recommend a particular outcome. Finally, predictive refers to analysis which uses historical data to predict future data or outcomes. The following are methods that address these types of analysis.

Regression analysis — This type of analysis allows for conducting various regression methods such as ordinary least squares (OLS), weighted least squares (WLS), or generalized linear model (GLM).

Predictive analytics — Predictive analytics software mines and analyzes historical data patterns to predict future outcomes by extracting information from data sets to determine patterns and trends. Using a range of statistical analysis and algorithms, analysts use predictive analytics products to build decision models which business managers use to plan for the best possible outcome.

Survival analysis — This type of analysis allows for the evaluation of durations, events, and reliability in relation to statistical analysis.

Time series analysis — Allows users to identify patterns within massive, continuous time series data sets to perform reporting, forecasting, and predictive analysis.

Bayesian analysis — This method of statistical inference allows one to combine prior information about a statistical parameter with evidence from information contained in a sample to guide the statistical inference process.

Decision trees — One common type of classification model is a decision tree. These models predict a number of possible outcomes based on a variety of inputs. For example, if a sales team builds $1 million in pipeline, they can close $100,000 in revenue, but if they build $10 million in pipeline, they should be able to close $1 million in revenue.

Statistical Analysis Software Features

Statistical analysis tools allow users to mine and analyze structured or unstructured data. Through the process of analysis, data sets and visualizations are created from the compiled data.

Data preparation — In order to analyze the data, it must be properly cleaned and should be of high quality. This preparation consists of deduplication, cleansing, and appending the data for statistical analysis. Not all analysis is achieved at the surface. Instead, robust statistical analysis tools mine data from databases and prepare it for analysis.

Data sampling — Data sampling allows users to select samples of data for defined procedures.

Statistical modeling — Statistical modeling involves creating a mathematical model that embodies a set of statistical assumptions concerning the creation of sample data which may be used to get a better idea about the makeup and distribution of the data.

Hypothesis testing — Statistical analysis tools often provide hypothesis testing features to ensure the analysis is consistent with the data and correct based on predetermined factors. This helps the researcher, data scientist, or statistician evaluate the outcome based on their initial hypothesis.

Data visualization — In addition to numerical inputs and outputs, statistical analysis software frequently allows the user to visualize results by means of graphs, charts, and reports. This not only helps the end user better understand them, but also aids with communicating these results with the broader company.

Potential Issues with Statistical Analysis Software

Lack of skilled employees — The main issue with adopting statistical analysis software is the need to have a skilled data professional to interact with the data and build the models. There is a distinct skill gap in terms of finding users who both understand how to pull data and build models and the implications that the data has on the overall business. For this reason, data experts are in very high demand and thus, expensive.

Data organization — Organizing data in a way that is easily accessible is a challenge that many companies face. It is not easy in today’s world to harness big data sets that contain historical and real-time data. Companies often need to build a data warehouse or a data lake that combines all the disparate data sources for easy access. This again, requires highly knowledgeable employees.