Advanced p‑Chart Proportion Nonconforming Calculator

Analyze attribute data with a robust p‑chart tool that computes proportion nonconforming, center line, variable control limits, and out‑of‑control signals. Paste subgroups, set sigma, and visualize performance. Validate inputs, handle unequal sample sizes, and highlight special causes. Export results, interpret patterns, and strengthen quality decisions using proven statistical rules for compliance, audits, and continuous improvement programs in demanding production environments.

Input
Typical values: 2 or 3.

Formulas: \(\bar{p}=\frac{\sum d_i}{\sum n_i}\), \(\text{SE}_i=\sqrt{\frac{\bar{p}(1-\bar{p})}{n_i}}\), \(\text{UCL}_i=\bar{p}+k\,\text{SE}_i\), \(\text{LCL}_i=\max\{0,\bar{p}-k\,\text{SE}_i\}\).

How this tool helps
  • Handles unequal subgroup sizes with variable limits.
  • Flags out‑of‑control points automatically.
  • Computes center line and key summary statistics.
  • Interactive chart for quick visual checks.
  • Client‑side CSV export from the results table.

For severe over‑dispersion or serial correlation, consider advanced adjustments (e.g., Laney p′). This page focuses on the classical p‑chart.

FAQs
1) What is a p‑chart?

A p‑chart monitors the proportion nonconforming (defective units divided by inspected units) across subgroups. It is appropriate for attribute data with varying sample sizes.

2) How are the control limits calculated?

Limits are based on the overall proportion \(\bar{p}\). For each subgroup size \(n_i\), the standard error is \(\sqrt{\bar{p}(1-\bar{p})/n_i}\). Limits are \(\bar{p} \pm k\) times this value, truncated to [0,1].

3) Can I use unequal sample sizes?

Yes. This tool computes variable limits for each subgroup using its actual size. If all subgroup sizes are equal, a constant pair of limits is also reported for reference.

4) What does “out of control” mean here?

It indicates the subgroup proportion is outside the calculated limits, suggesting a special cause. Investigate process changes, measurement shifts, or assignable causes.

5) Which sigma multiplier should I choose?

Three sigma is common for routine monitoring; two sigma is sometimes used for early detection with more false alarms. Choose based on your risk tolerance and standard practices.

6) What if my data show over‑dispersion?

Classical p‑charts assume binomial variation. If there’s extra variation or strong serial correlation, consider adjusted approaches (e.g., Laney p′ chart) or stratify the process.

7) How should I interpret a run within limits?

Being within limits doesn’t guarantee stability. Consider run rules (e.g., long runs on one side of the center line) and context. Pair the chart with process knowledge and investigations.

Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.