➲ Apps for automated data analysis and human-readable interpretation in natural language
➲ Designed especially for learners and applied researchers
➲ Upload your data and let the app crunch the statistics
➲ Download your data analysis report
➲ Interpretation in plain English included (most of the apps)
➲ R or Python code to reproduce results included (most of the apps)
➲ Free. No registration required
To the apps
(Yellow for R, Green for Python)
Exploratory Data Analysis
Exploratory data analysis report containing univariate descriptive statistics and graphics: Histograms, box-plots, bar-plots, pie-plots etc. for continuous and categorical variables with the R programming language. R Code included. More...
Unsure which correlation measure to use? Pearson, Spearman or even Distance Correlation? Ask this app. Pairwise correlation & association analysis for approximately continuous variables. Interpretation in plain English and R Code included. More...
Confirmatory Factor Analysis
Struggling with the interpretation of a Confirmatory Factor Analysis (CFA)? Try this app. Single-group, first-order CFA for approximately continuous variables with the R package lavaan. Interpretation in plain English and R Code included. More...
Principal Components Analysis
Struggling with the coding and interpretation of a Principal Components Analysis (PCA)? Try this app for approximately continuous variables. Interpretation in plain English by the FactoInvestigate package and R Code included. More...
Exploratory Data Analysis
Exploratory data analysis report containing univariate descriptive statistics and graphics: Histograms, box-plots, bar-plots, pie-plots etc. for continuous and categorical variables with the Python programming language. Python Code included. More...
Multiple Comparisons Procedures (MCP) with a Control, in particular the Dunnet test and variants for linear models with at least one factor of interest. Annotated tables, graphics and a brief theoretical background are included where appropriate. Go to app...
Principal Components Analysis with Python.
Two binomial distributions with Bayes theory.
The Statsomat project and site have the goal of developing, collecting and maintaining open-source and web-based apps for automated data analysis with a human-readable interpretation. The apps are a great help for applied researchers and Data Science learners all over the world. The strategy we follow is a maximal automation with a minimal, but sufficient user-interaction.
If you are an applied researcher or a Data Science learner unfamiliar with data analysis or programming, you can use the Statsomat apps directly in the browser to generate automated data analysis reports. Classic statistical data analysis and Machine Learning questions are handled by the apps in a similar way as by a human. The reports contain annotated tables, graphics, an interpretation in plain English and the code to reproduce the analysis by yourself. If you need a quick help with your data analysis or with programming, then check the Statsomat apps. You will be surprised.
Statsomat is a project in continuous progress. The core contributor responsible for the overall design as well as authoring the content of the apps is Denise Welsch. Most of the other contributors are students at the University of Applied Sciences Koblenz, Department of Mathematics and Technics.
You are also welcomed to improve and extend the functionality of the apps. Automizing data analysis interpretation and contributing to Statsomat are fantastic ways to learn Data Science. If you want to contribute, take a look at the GitHub repositories which are currently public or contact us if you want to develop a completely new app for the Statsomat.
What do users say
To get the code is essential
It’s very essential for me as a junior to get the source code at the end, which is hard for many students to deal with. I’ve used this app for my Bachelor thesis testing several datasets, its flexible, easy, effective, beneficial. I hope that this website could be extended in the future, so it treats statistical issues as many as possible. I would recommend everyone to try it.
A great tool
The Statsomat is a great tool for analyzing data. I hope that more and more methods will become available in the near future. For a user, obtaining R code is extremely helpful. One can modify this code without the need to start from scratch, and one has code for documentation and possible replication.
Excellent for beginners
Thank you very much for the Statsomat applications, they are excellent for beginners!
Thank you for this application. It will be a massive help to people who have little time (or scared of coding). I tried it, and the graphs look really nice.
This is a new and awesome app, this is the future! Please add CFA for categorical variables. Thanks.