MIDAS Documentation

MIDAS is a free exploratory data analysis tool that runs in your browser, currently offered as a beta. Load CSV files, compute statistics, create graphs, and run regression models — all within the browser. The files and project data you load are never sent to external servers. For usage analytics, the "open from URL" feature, and licensing and commercial use, see Privacy and Security. Open MIDAS

Getting Started

• Basic Usage - Walk through basic operations with sample data

User Guide

Data Preparation

• Data Preparation and Import - Loading CSV/TSV files and automatic data type inference
• Datasets - Managing imported data and derived datasets created through SQL or other transformations
• Sample Datasets - Description of sample data included in MIDAS

Data Exploration

• Data Table - Viewing, filtering, and sorting data
• Row Selection - How row selection works across tabs
• Selected Rows - Viewing selected rows and saving them as a derived dataset
• Filtered Data - Viewing and refining rows behind double-clicked graph or crosstab elements

Screen Layout

• Workspace and Layout Management - Managing multiple analysis tasks in parallel

Data Processing

• Column Type Conversion - Converting data types and handling errors
• Data Reshape - Converting between Wide and Long formats
• Dummy Coding - Converting categorical variables to dummy variables
• Data Processing with SQL - Transforming data using SQL
• Enum Definitions - Creating and managing Enum types for categorical data
• Orthogonal Polynomials - Generating orthogonal polynomial columns from a numeric column

Data Visualization

• Creating Graphs - Histograms, scatter plots, bar charts, time series plots, pair plots, and more
• Advanced Graph Creation - Layer multiple graph types, add facets, and control scales using Grammar of Graphics

Statistical Analysis

• Basic Statistics - Mean, standard deviation, quantiles, and other summary statistics
• ANOVA - One-way and two-way analysis of variance with Tukey HSD post-hoc comparisons
• DoE Analysis - 2-level orthogonal array generation and factorial experiment analysis
• Cross Tabulation - Pivot tables for categorical variables
• Linear Regression - Using the Linear Regression tab
• Generalized Linear Model (GLM) - Logistic, Poisson, and other regression models for response variables that need not follow a normal distribution
• Generalized Linear Mixed Model (GLMM) - Random intercept models for grouped data
• Survival Analysis - Kaplan-Meier and Cox regression
• Random Forest - Random forests for classification and regression with feature importances
• Principal Component Analysis (PCA) - Dimensionality reduction and exploring correlation structures among variables

Organizing Analysis Results

• Reports - Saving graphs and statistical results together
• Export - Export data as CSV/TSV/JSON, download graphs as SVG

Project Management

• Project Management - Managing datasets, reports, and models
• Project Overview - View and manage resources
• Project Lineage - Visualize dependencies
• Compare Project - Compare project versions
• MDS Files - Saving, exporting, and signing project files
• Storage Management - Viewing and deleting saved projects and checking storage usage
• Managing Signing Keys - Verifying MDS file signatures and managing trusted keys

Reference

• Custom Graph Reference - Geometry/Statistics list
• Agent API (window.midas) - Controlling MIDAS from AI agents and external tools
• Numerical Accuracy - Verifying statistical computation accuracy with NIST Statistical Reference Datasets

Tutorials

Step-by-step walkthroughs using sample data.

• Assembly Line Dimension Error Analysis - Exploratory analysis workflow with ANOVA and linear regression
• Optimizing Injection Molding Parameters - Analyze a 3-factor 2-level factorial experiment
• Survival Analysis with the Kaplan-Meier Method - Estimate survival curves and compare groups
• Grouped Binomial GLM with Dose-Response Data - Logistic regression for aggregated binomial data

Statistical Concepts

Background knowledge on the statistical methods used in MIDAS. Reference these pages when you want to deepen your understanding of analysis results.

• Data Types and Measurement Scales - Nominal, ordinal, interval, ratio scales and their impact on analysis
• OLS Fundamentals - Normal equations, Gauss-Markov theorem, VIF
• GLM Fundamentals - Exponential family, link functions, IRLS
• Survival Analysis Fundamentals - Censoring, Kaplan-Meier, Cox proportional hazards model
• GLMM Fundamentals - Random effect models, REML, BLUP, ICC
• Numerical Computing Fundamentals - How floating-point precision, catastrophic cancellation, and condition numbers affect computation accuracy
• Missing Data Mechanisms - MCAR, MAR, MNAR, and the assumptions behind listwise deletion
• Glossary - Definitions of estimator, convergence, likelihood, deviance, and more

System Requirements

• Privacy and Security - Data processing, storage, external communication, and browser requirements
• PWA and Offline Use - Install as an app and work offline

Support

• Release Notes - New features and change history
• For questions or bug reports, contact contact@midas-app.org

Also available as a Markdown file.