Stat4all is a web-based statistical package for automating data analysis procedures and report generation.

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Easy to Use

The interface is designed with non-statisticians in mind, so you don't need to know statistical terms to use Stat4all.

Automated Procedures

Stat4all automatically tests assumptions and chooses appropriate tests based on the goal of your analysis.

Pay as You Go

You can access the software for free without registration and pay only if you want to download the report.

Supports All Major File Formats

Stat4all can upload data from different types of files, including files from SPSS, SAS, MS Excel, STATA, and other statistical packages. (If you don't have a data set, you can use one of the example data sets to explore how Stat4all works.)

Prepare & Transform Your Data

If you still need to prepare your data set before you can analyze it, you can use Stat4all to label categorical variables, standardize numerical variables, create composite variables, and perform various other operations on your data set.

Explore & Understand Your Data

Examine the distributions of your numeric and categorical variables with bar plots, density plots, and histograms; inspect the distribution of numeric variables with Q-Q plots; examine the relationship between variables using scatter plots.

All Data Analysis Procedures are Automated

When you compare groups or pre-/post-test measurements, Stat4all will automatically test parametric assumptions and choose the statistical tests accordingly. If you examine correlations between variables and assume a linear relationship, an outlier analysis graph will be included in the report. When you build simple and multiple regression models, a model diagnostic is performed if statistical significance is found.

Customize Analysis Settings

For most analyses, you won't need to change the default analysis settings. However, you still have some control over the p-value used for statistical significance cut-off, test used to assess normality, and preference of parametric tests in large sample sizes.

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