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User Guide

This section covers hands-on usage of DaSPi — from installation through data visualization, statistical analysis, and measurement system evaluation.

Getting Started

Guide Description
Installing Install DaSPi from PyPI and verify the setup
Plotting Build charts with SingleChart, MultivariateChart, and the Facets helpers
ANOVA Fit linear models, run ANOVA, and automate backward elimination
DOE Design full- and fractional-factorial experiments
Hypothesis Testing Normality, variance, location, and proportion tests
Gage Analysis MSA Type 1 gage studies and Gage R&R

3S Methodology

The 3S Methodology is a streamlined, three-phase problem-solving framework that combines the best elements from 8D and Six Sigma DMAIC.

Phase Focus
Overview Introduction, comparison with DMAIC / 8D
Specify Define & contain — team, charter, SIPOC, containment
Scrutinize Investigate & analyse — root cause, DOE, hypothesis tests
Stabilize Implement & control — solution validation, SPC, knowledge transfer

Phase guides coming soon

Detailed step-by-step guidance for the Specify, Scrutinize, and Stabilize phases is in preparation.

Statistical Analysis Workflow

A typical DaSPi analysis follows these steps:

  1. Load data — dsp.load_dataset(name) or read your own CSV/Excel.
  2. Explore visually — use SingleChart or MultivariateChart with Scatter, GaussianKDE, or QuantileBoxes.
  3. Check assumptions — anderson_darling_test, variance_test.
  4. Test hypotheses — position_test, proportions_test.
  5. Fit a model — LinearModel with optional backward elimination.
  6. Validate residuals — ResidualsCharts(model).plot().
  7. Interpret — ParameterRelevanceCharts, model.anova(), model.gof_metrics().
  8. Assess capability — ProcessCapabilityAnalysisCharts with SpecLimits.

Data Visualization Overview

DaSPi's plotting system is built in layers:

AxesFacets          ← subplot grid (rows × cols or mosaic)
  └─ Chart          ← data wiring (source, target, hue, shape, size)
       ├─ Plotter   ← mark drawing (Scatter, Line, GaussianKDE, …)
       └─ Facets    ← labels, legend, reference stripes

See the Plotting Guide for examples at every layer.