Statistical Analysis reviews by real, verified users. Find unbiased ratings on user satisfaction, features, and price based on the most reviews available anywhere.
Statistical analysis software products are specialized programs designed to allow users to perform complex statistical analysis. These products typically provide tools for the organization, interpretation, and presentation of selected data sets. Statistical solutions typically package these data management capabilities with features specific to statistical analysis. Statistical analysis capabilities refer to capabilities that support analysis methodologies such as regression analysis, predictive analytics, and statistical modelling, among many others.
Statistical analysis software tools are typically used by data scientists and mathematicians, but can provide industry-specific features. These may be features tailored to scientific research, cost modelling, or health science; other tools can perform specific statistical analysis across industry or use case.
Some products overlap within both statistical analysis software and predictive analytics software or business intelligence platforms. However, most tools in those categories will not provide the same level of niche statistical analysis capabilities as statistical software.
To qualify for inclusion in the Statistical Analysis category, a product must:
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
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
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
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).
nQuery is now the world's most trusted sample size and power analysis software. In 2018, 91% of organizations with FDA approved clinical trials used nQuery as their sample size calculator. It is used by Biostatisticians of all levels of expertise. Created by sample size experts, nQuery boasts an extensive list of easy-to-use but powerful features for sample size calculation and power analysis.
SAS Base is a programming language software that provides web-based programming interface; ready-to-use programs for data manipulation, information storage and retrieval, descriptive statistics and reporting; a centralized metadata repository; and a macro facility that reduces programming time and maintenance headaches.
SAS Enterprise Miner is a software provide insights that drive better decision making, it streamline the data mining process to develop models quickly, understand key relationships and find the patterns that matter most.
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
Analyse-it is a statistical analysis software that includes hypothesis testing, model fitting, ANoVA, and PCA, statistical process control (SPC) and quality improvement, analytical and diagNostic method validation for laboratories to meet regulatory compliance.
G*Power is a tool to compute statistical power analyses for many different t tests, F tests, χ2 tests, z tests and some exact tests. G*Power can also be used to compute effect sizes and to display graphically the results of power analyses.
Provides a common interface for different vendor data and automates all functionality such as data importing and display, integration, background subtraction, display of extracted mass chromatograms, molecule match, enumeration of molecular formulae, etc.
By combining enterprise-scale R analytics software with the power of Apache Hadoop and Apache Spark, Microsoft R Server for HDInsight gives you the scale and performance you need. Multi-threaded math libraries and transparent parallelization in R Server handle up to 1000x more data and up to 50x faster speeds than open-source R, which helps you to train more accurate models for better predictions. R Server works with the open-source R language, so all of your R scripts run without changes.
ROOT is a modular scientific software framework that provides all the functionalities needed to deal with big data processing, statistical analysis, visualisation and storage, it is mainly written in C++ but integrated with other languages such as Python and R.
Multiple users can explore data, then interactively create and refine predictive models. Distributed, in-memory processing slashes model development time, quickly surfacing valuable insights you can act on.
Fully featured Statistics application and development framework built on the open source R project Provides familiar powerful user interface available in mainstream statistical applications like SPSS, SAS etc. Unlocks the power of R for the analyst community by providing a rich GUI and output for several popular statistics, data mining, data manipulation and graphics commands, all out of the box... Provide a rich development framework for developing and deploying new statistical modules, a
DataMelt is a computational environment that allows you to perform data analysis, mathematical and statistical calculations using scripting languages (Python/Jython, BeanShell, Groovy, JRuby, Matlab/Octave) on the Java and Android platforms.
Intellectus Statistics is revolutionary software that allows you to conduct analyses without requiring statistical expertise. The output is in the form of plain English sentences, formatted in the APA style, to make understanding statistical results easier than ever. Intellectus Statistics is a statistics research tool and an instructional tool.
PS is an interactive program for performing power and sample size calculations that may be downloaded for free. It can be used for studies with dichotomous, continuous, or survival response measures. The alternative hypothesis of interest may be specified either in terms of differing response rates, means, or survival times, or in terms of relative risks or odds ratios. Studies with dichotomous or continuous outcomes may involve either a matched or independent study design. The program can deter
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
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.
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.
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 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.
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.