capability for attribute data

**Capability for attribute data** is the **quality assessment approach for pass-fail or count-based outcomes where continuous-variable Cp and Cpk are not applicable** - it uses defect-rate and binomial/Poisson metrics to evaluate process performance. **What Is Capability for attribute data?** - **Definition**: Capability evaluation for discrete outcomes such as defect present/absent or defects per unit. - **Core Metrics**: DPMO, ppm defective, yield, sigma level equivalents, and confidence bounds. - **Data Models**: Binomial for pass-fail and Poisson/negative-binomial for defect counts. - **Reporting Focus**: Expected nonconformance rate under current process conditions. **Why Capability for attribute data Matters** - **Method Correctness**: Applying continuous capability indices to binary data gives misleading conclusions. - **Quality Governance**: Attribute metrics align with inspection and escape-rate management workflows. - **Customer Alignment**: Many contracts specify ppm or DPMO limits for acceptance. - **Improvement Tracking**: Discrete metrics reveal effectiveness of defect-prevention actions over time. - **Cross-Process Comparability**: Standardized attribute indices support benchmarking across product lines. **How It Is Used in Practice** - **Data Stratification**: Segment defects by mechanism, tool, lot, and opportunity count. - **Rate Estimation**: Compute defect rates with confidence intervals using correct discrete models. - **Control Deployment**: Use attribute control charts and targeted corrective actions for dominant defect categories. Capability for attribute data is **the correct statistical lens for binary quality outcomes** - discrete defects require discrete metrics for honest process assessment.