Turn raw readings into actionable control-chart insights fast. Toggle each Nelson rule and thresholds easily. See violations instantly, then export clean compliance files anytime.
| # | Measurement | Note |
|---|---|---|
| 1 | 10.00 | Baseline |
| 2 | 10.05 | Baseline |
| 3 | 9.98 | Baseline |
| 4 | 10.02 | Baseline |
| 5 | 10.01 | Baseline |
| 6 | 10.07 | Small rise |
| 7 | 10.10 | Small rise |
| 8 | 10.14 | Small rise |
| 9 | 10.18 | Small rise |
| 10 | 10.22 | Potential trend |
| 11 | 10.25 | Potential trend |
| 12 | 10.28 | Potential trend |
This calculator evaluates eight Nelson patterns to highlight non‑random behavior in time‑ordered measurements. It is designed for individual readings, subgroup averages, or any sequential metric where stability matters. When patterns trigger, the last point completing the pattern is flagged for fast review and escalation. For routine monitoring, 25 points often reveal drift while keeping review effort manageable.
With Auto settings, the center line equals the arithmetic mean of all submitted values, and sigma is the sample standard deviation using n−1 in the denominator. These estimates are appropriate for an initial screening pass. Manual inputs support locked baselines from validated reference periods, so new data can be compared against a stable historical target.
Classic limits use CL ± 3σ, aligning with common control‑chart practice and producing few false alarms under random variation. A normal process is expected to place about 99.7% of points within ±3σ, so a Rule 1 signal is rare and meaningful. Custom UCL/LCL entries support regulated processes, customer specs, or engineered thresholds. The graph overlays CL, UCL, and LCL to contextualize every point.
Rules 1, 5, and 6 emphasize large standardized deviations and clusters beyond 1σ or 2σ on the same side, suggesting shifts or drifts. Rules 2, 3, and 4 focus on sustained runs and trends, often caused by tooling wear, calibration changes, or environment effects. Rules 7 and 8 detect over‑control or mixture behavior, such as rounding, filtering, or feedback adjustments.
The point table reports each value, its z‑score, and any triggered rules. If multiple patterns hit the same point, the violation list aggregates them for prioritization. CSV export supports audits and downstream analytics, including counts by rule. The PDF report creates a compact, timestamped snapshot suitable for management review packets.
Use a consistent sampling interval, confirm data ordering, and avoid mixing multiple product families in one series. If sigma is near zero, review resolution or gage capability, then re‑estimate sigma from a representative window. Pair signals with context such as lot changes, setup times, and operator shifts. Treat rule hits as prompts for investigation, not automatic defects.
Use sequential measurements collected at consistent intervals. Subgroup means also work if each point represents the same subgroup size and method.
Auto is useful for quick screening. Manual sigma is better when you have a validated baseline or want to compare new points to a locked reference period.
Many rules describe multi‑point sequences. Flagging the last point shows exactly where the pattern becomes complete and actionable.
Some rules need long runs, so short datasets may show few signals. Collect more points or focus on Rules 1, 5, and 6 for early warnings.
Multiple hits strengthen evidence of special‑cause behavior. Investigate recent changes first, then confirm with additional sampling before adjusting the process.
Not necessarily. Nelson rules indicate unusual process behavior. Use specifications, risk criteria, and root‑cause checks to decide disposition and actions.
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.