ChakataStat User Guide

A task-oriented manual for people running analyses — the companion to the product overview at chakatastat.app, which describes what ChakataStat is. The goal: you can learn ChakataStat end to end from this guide alone.

The pages are ordered so a brand-new user can read one short page for the whole picture, then drop into the detailed task pages as needed, with the advanced features kept in their own section. Every worked example runs on the bundled sample dataset (File → Open Sample Dataset), so you can follow along and get the same numbers.

Start here

• Quick Start — install to your first result in about ten minutes, on the bundled sample dataset. The fastest way in.
• ChakataStat at a glance — the whole application in one read: what it is, every part of the window, and what each menu does.
• Getting Started — install, the workbench tour, and your first analysis in five minutes.
• Tutorial Workbook — a ten-module course on the bundled sample health study (see its codebook), for self-study or classroom training, with exercises and the reproducible syntax for every module.

Core tasks (the detail)

• Working with Data — importing SPSS, Stata, SAS, CSV, Excel, Parquet and fixed-width text (encodings detected, delimiters sniffed); the Data View and Variable View; value labels, missing values and measurement levels; the .ckd document; find & replace; the data dictionary; bulk rename and variable sets.
• Case Semantics & Transforms — Select / Weight / Split cases, and the Compute / Recode / Rank / Sort / Merge / Transpose transforms.
• Analyzing Data — every procedure, grouped as the menu is. When to use it, how to fill the dialog, how to read the output:
  • Descriptive Statistics
  • Compare Means
  • Correlation & Regression
  • Panel Regression
  • Meta-Analysis
  • Propensity Score
  • Nonparametric Tests
  • Scale, Dimension Reduction & Classify
  • Method Comparison (Bland-Altman)
  • Latent Class Analysis
  • Item Response Theory
  • Confirmatory Factor Analysis
  • Complex Samples (Survey Designs)
  • Survival & Time Series
  • VAR & Granger Causality
  • Custom Tables
• Graphs — Histogram, Scatterplot, Boxplot, the Chart Builder (violin, scatterplot matrix, ECDF, dot plot, bubble) and analysis-attached plots.
• Editing a chart — restyle a finished chart in place: title, legend, colours, line width, bar stacking — every edit recorded as a command amendment, so a styled chart re-runs styled.
• Settings — appearance, connectivity, the AI assistant and Advanced mode.

Advanced features (their own section)

Optional power-user and developer features, in advanced/:

• The AI Assistant — connect a model backend and ask for analyses in plain language.
• Reproducibility — the .cks syntax console and its editor (line numbers, syntax colours, find & replace, run keys, as-you-type checking, completion and hover help), the command palette, headless batch runs (--run), output export to HTML/Markdown/LaTeX, and Export as Python.
• Direct Engine Access — Advanced mode, the engine diagnostics panel, and driving ChakataStat over MCP.
• Verify this Installation — the built-in check of the installed copy against independent reference values, from the Help menu or --verify.

Reference

• Choosing a procedure — "what do I have, what do I want to know?" → which analysis.
• Glossary — ChakataStat and statistics terms.
• Command reference — every command and its arguments, for writing syntax by hand, scripting a .cks file, or driving ChakataStat from the Python client or an MCP agent.

In-app entry points

This guide is reachable from Help → Documentation (and F1) and from the first-run welcome / empty-state surfaces.