Web & Desktop Application
Graph statistics app - a visual statistics web & desktop application for analysing survey-like variables as attribute networks — so you can see how variables interact and explore patterns without drowning in numbers and tables; even if you are an experienced statistician.
Peoplet Stat integrates network science into interactive visualizations of statistical variable networks: variables – human attributes - become nodes, statistical relationships become weights on edges — letting users explore complex datasets in a clear, intuitive environment.
It’s designed for human-attribute data from large population surveys and from attribute data collected through the Peoplet ecosystem, enabling fast insight from rich, real-world data.
All of this leads to…
clearer decisions and faster improvement.
App Capabilities
Peoplet Stat transforms complex datasets into interactive network systems,combining statistical rigor with visual exploration to make structure,relationships, and key drivers immediately understandable.
See your dataset as a connected system: each variable is a node, and edges represent validated statistical relationships, revealing principal components, factors, latent variables, and backbone structure at a glance.
Build analyses visually. Drag variables into a workspace and explore results through interactive graphs — minimizing reliance on scripts, syntax, or complex command structures.
Bring familiar statistical methods into a graph-first experience — shifting from static numeric outputs to interactive network-based understanding.
Handle heterogeneous variables (binary, ordinal, continuous, and AI-derived attributes) while keeping the analysis coherent and interpretable.
Identify the most central attributes (key drivers) using network centrality — a complementary lens to classical statistics.
Detect communities of variables that naturally group together, revealing themes and structure without collapsing everything into abstract dimensions.
Uncover mediated and indirect relationships (how variables connect through others), not just simple pairwise effects.
Use pruning and backbone-style simplifications to keep only the most structurally relevant links — making dense systems readable.
Transform free-form responses into topic and sentiment variables and include them in the same analytical graph — expanding beyond classic survey-only workflows.
Calibrate network indicators against classical statistical baselines so insights remain transparent, defensible, and easier to communicate across stakeholders.
A graph-database backend and GPU-accelerated infrastructure enable fast querying and interactive visualization on large attribute networks.
Built with GDPR compliance, data protection impact assessments, and transparent transformation protocols — especially important when expanding into free-form and communication-derived data.
A single environment that aims to serve researchers, consultants, policy analysts, agencies, public institutions, and enterprises working with complex survey and market research data.