{ "cells": [ { "cell_type": "markdown", "id": "69c113a3", "metadata": {}, "source": "# Reading the assumption report\n\nEvery mfgQC analysis checks its own assumptions and reports the outcome. It\nwarns, it quantifies the impact, and it recommends a next step, but it never\nsilently switches methods or transforms your data. This notebook walks the\nanatomy of an assumption line, the difference between a p-value and an effect\nsize, and the two honest ways to respond when an assumption fails. Every cell\nruns, so open it in Colab and try it on your own numbers.\n\n
Run in ColabView on GitHubDownload notebook
" }, { "cell_type": "markdown", "id": "f314abce", "metadata": {}, "source": [ "Install it first (skip this if mfgQC is already in your environment):" ] }, { "cell_type": "code", "execution_count": null, "id": "948e7556", "metadata": { "execution": { "iopub.execute_input": "2026-06-26T23:19:58.776747Z", "iopub.status.busy": "2026-06-26T23:19:58.776561Z", "iopub.status.idle": "2026-06-26T23:19:59.767355Z", "shell.execute_reply": "2026-06-26T23:19:59.766486Z" }, "tags": [ "skip-execution" ] }, "outputs": [], "source": [ "!pip install mfgqc" ] }, { "cell_type": "markdown", "id": "cdabf7a3", "metadata": {}, "source": [ "## The guardrail: report, never silently switch\n", "\n", "This is the \"statistical guardrails\" pillar. If a capability study finds your\n", "measurements are non-normal, it tells you and keeps computing the normal-theory\n", "number you asked for. It does not quietly Box-Cox the data and hand you a\n", "different Cpk. Auto-correction is opt-in only: a non-normal method runs only\n", "when you pass `method=` explicitly.\n", "\n", "The philosophy in the code's own words: \"type hints, not decisions.\" Each check\n", "reports a binary verdict from the direct test of the assumption, plus two pieces\n", "of adjacent context: practical impact and the test's resolving power at your\n", "sample size. The context never flips the verdict, and the verdict never changes\n", "the analysis. You decide what to do." ] }, { "cell_type": "markdown", "id": "c1f949c9", "metadata": {}, "source": [ "## Where the report comes from\n", "\n", "Every result carries a list of structured `AssumptionCheck` records and renders\n", "them in `.report()` under an Assumption checks block, followed by\n", "Recommendations. Here is a small bore-diameter study of 15 parts. Print the\n", "report and read the last line." ] }, { "cell_type": "code", "execution_count": 2, "id": "d6833dbf", "metadata": { "execution": { "iopub.execute_input": "2026-06-26T23:19:59.769756Z", "iopub.status.busy": "2026-06-26T23:19:59.769618Z", "iopub.status.idle": "2026-06-26T23:20:00.844581Z", "shell.execute_reply": "2026-06-26T23:20:00.844198Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Process Capability (method=normal)\n", "==================================\n", "n = 15 mean = 10.008\n", "sigma (within) = n/a\n", "sigma (overall) = 0.044324\n", "Cp/Cpk sigma = overall\n", "\n", "Cp = 1.504 95% CI (0.954, 2.05)\n", "Cpk = 1.446 95% CI (0.884, 2.01) (Cpu=1.446, Cpl=1.562)\n", "Pp = 1.504 Ppk = 1.446 (Ppu=1.446, Ppl=1.562)\n", "Cpm = n/a\n", "\n", "Assumption checks:\n", " [PASS] normality (Anderson-Darling): AD=0.368, p=0.383; est. Cpk impact 3.4%; n=15 [low power]\n" ] } ], "source": [ "import numpy as np, pandas as pd, mfgqc\n", "\n", "rng = np.random.default_rng(11)\n", "df = pd.DataFrame({\"bore\": np.round(rng.normal(10.0, 0.05, size=15), 4)})\n", "qc = mfgqc.load(df, measure=\"bore\").spec(lower=9.8, upper=10.2)\n", "print(qc.capability())" ] }, { "cell_type": "markdown", "id": "796af347", "metadata": {}, "source": [ "The same data is available structurally via `.to_dict()`. Consume that from\n", "code; never parse the report text. Each record is one `AssumptionCheck`." ] }, { "cell_type": "code", "execution_count": 3, "id": "7793c3b8", "metadata": { "execution": { "iopub.execute_input": "2026-06-26T23:20:00.845688Z", "iopub.status.busy": "2026-06-26T23:20:00.845613Z", "iopub.status.idle": "2026-06-26T23:20:00.853206Z", "shell.execute_reply": "2026-06-26T23:20:00.852926Z" } }, "outputs": [ { "data": { "text/plain": [ "[{'name': 'normality',\n", " 'test': 'Anderson-Darling',\n", " 'statistic': 0.36791797495857104,\n", " 'p_value': 0.3825213544886901,\n", " 'passed': True,\n", " 'magnitude': 0.033915625974104434,\n", " 'magnitude_label': 'est. Cpk impact',\n", " 'reliability': 'low_power',\n", " 'n': 15,\n", " 'recommendation': None}]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "qc.capability().to_dict()[\"assumptions\"]" ] }, { "cell_type": "markdown", "id": "e5aed87a", "metadata": {}, "source": [ "## Anatomy of an assumption line\n", "\n", "Take that PASS line apart, field by field. Each piece maps to a field on the\n", "`AssumptionCheck` dataclass (`mfgqc/assumptions.py`):\n", "\n", "```text\n", "[PASS] normality (Anderson-Darling): AD=0.368, p=0.383; est. Cpk impact 3.4%; n=15 [low power]\n", " │ │ │ │ │ │ │ │\n", " │ │ │ │ │ │ │ └─ reliability\n", " │ │ │ │ │ │ └─ n (sample size)\n", " │ │ │ │ │ └─ magnitude + magnitude_label\n", " │ │ │ │ └─ p_value\n", " │ │ │ └─ statistic\n", " │ │ └─ test\n", " │ └─ name\n", " └─ passed (verdict)\n", "```\n", "\n", "| Part of the line | `AssumptionCheck` field | What it means |\n", "| --- | --- | --- |\n", "| `[PASS]` / `[FAIL]` | `passed` | The binary verdict from the direct test at $\\alpha = 0.05$. Nothing else in the line changes this. |\n", "| `normality` | `name` | What was checked. |\n", "| `(Anderson-Darling)` | `test` | The exact test used to reach the verdict. |\n", "| `AD=0.368` | `statistic` | The test statistic. |\n", "| `p=0.383` | `p_value` | P-value of the direct test. `passed` is simply `p >= 0.05`. Omitted when a test has no defined p-value. |\n", "| `est. Cpk impact 3.4%` | `magnitude` + `magnitude_label` | The practical-impact / effect-size context: here, how much the capability index would move under a non-normal fit. |\n", "| `n=15` | `n` | The sample size the check ran on. |\n", "| `[low power]` | `reliability` | The test's resolving power at this $n$: `ok` (no marker), `low power`, or `oversensitive`. |\n", "\n", "The `magnitude_label` varies by check: `est. Cpk impact` and `skew` for\n", "normality, `variance ratio` for homogeneity (Levene), `dispersion ratio` for\n", "attribute charts, `lag-1 autocorr` for independence, `subgroup count`/`ndc` for\n", "adequacy rules. The label tells you what the magnitude number is." ] }, { "cell_type": "markdown", "id": "d7f32e9e", "metadata": {}, "source": [ "You can pull any field straight off the record. No text parsing." ] }, { "cell_type": "code", "execution_count": 4, "id": "295754b6", "metadata": { "execution": { "iopub.execute_input": "2026-06-26T23:20:00.854197Z", "iopub.status.busy": "2026-06-26T23:20:00.854151Z", "iopub.status.idle": "2026-06-26T23:20:00.861220Z", "shell.execute_reply": "2026-06-26T23:20:00.860870Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "name: normality\n", "test: Anderson-Darling\n", "statistic: 0.368\n", "p_value: 0.383\n", "passed: True\n", "magnitude: 0.034 (est. Cpk impact)\n", "reliability: low_power\n", "n: 15\n" ] } ], "source": [ "chk = qc.capability().to_dict()[\"assumptions\"][0]\n", "print(\"name: \", chk[\"name\"])\n", "print(\"test: \", chk[\"test\"])\n", "print(\"statistic: \", round(chk[\"statistic\"], 3))\n", "print(\"p_value: \", round(chk[\"p_value\"], 3))\n", "print(\"passed: \", chk[\"passed\"])\n", "print(\"magnitude: \", round(chk[\"magnitude\"], 3), f\"({chk['magnitude_label']})\")\n", "print(\"reliability: \", chk[\"reliability\"])\n", "print(\"n: \", chk[\"n\"])" ] }, { "cell_type": "markdown", "id": "2278328b", "metadata": {}, "source": [ "## `[PASS]` vs `[FAIL]`: a verdict, not an action\n", "\n", "A `[FAIL]` means the assumption was rejected at $\\alpha = 0.05$. It does not\n", "mean mfgQC changed anything. The number above the assumption block was still\n", "computed with the method you asked for. A FAIL is a flag that says \"the reported\n", "number rests on an assumption that didn't hold; read on, and decide.\" The\n", "recommendation line tells you the conventional remedy; acting on it is your\n", "call.\n", "\n", "The marker is driven purely by the direct test of the assumption, never by the\n", "context fields. This is deliberate: it stops a coincidentally-small impact\n", "estimate from issuing a false all-clear on grossly non-normal data. The verdict\n", "answers \"did the assumption hold?\"; the context answers \"does it matter, and\n", "could the test even tell?\"" ] }, { "cell_type": "markdown", "id": "ad8cc14f", "metadata": {}, "source": [ "### `[low power]` and `[oversensitive]`: the reliability flag\n", "\n", "The reliability flag is a fact about the test, not a judgment about your data:\n", "\n", "- `[low power]`: the sample is small enough ($n < 20$ for normality, with\n", " per-check thresholds) that the test has weak power to detect a real\n", " violation. A `[PASS]` carrying `[low power]` is weak evidence: the test did\n", " not reject, but at this $n$ it might not have caught a violation that is\n", " genuinely there. Treat it as \"no evidence against,\" not \"confirmed.\"\n", "- `[oversensitive]`: at very large $n$ ($> 5000$) significance tests reject\n", " trivial, practically-irrelevant departures. A `[FAIL]` carrying\n", " `[oversensitive]` is the cue to lean on the magnitude, not the p-value.\n", "\n", "The bore study above carried `[low power]` because $n = 15$. That is why mfgQC\n", "reports the effect size alongside the p-value: at small $n$ the p-value alone is\n", "not enough to act on." ] }, { "cell_type": "markdown", "id": "af500a89", "metadata": {}, "source": [ "## Magnitude: statistical vs practical significance\n", "\n", "A statistically significant violation can be practically negligible, and a\n", "practically serious one can hide under a non-significant p-value at small $n$.\n", "The p-value answers \"is the departure real?\"; the magnitude answers \"is it big\n", "enough to care about?\" mfgQC reports both so you can judge practical\n", "significance yourself.\n", "\n", "In the PASS example above, `est. Cpk impact 3.4%` says: even if you switched to a\n", "non-normal method, your Cpk would move by about 3%, not enough to change a\n", "sourcing decision. The failing case below carries the same label, but it reads\n", "89.1%. Same statistic name, completely different engineering consequence, and you\n", "can only see the difference because the magnitude is on the line." ] }, { "cell_type": "markdown", "id": "91ad35fd", "metadata": {}, "source": [ "## A FAIL, worked end to end\n", "\n", "Here is a genuinely right-skewed positive measurement, the kind you get from\n", "flatness, surface roughness, or runout, where values are bounded at zero and\n", "tail to the right. Build it with a gamma draw, run the default capability study,\n", "and read the assumption block." ] }, { "cell_type": "code", "execution_count": 5, "id": "6d102e46", "metadata": { "execution": { "iopub.execute_input": "2026-06-26T23:20:00.862237Z", "iopub.status.busy": "2026-06-26T23:20:00.862186Z", "iopub.status.idle": "2026-06-26T23:20:00.867187Z", "shell.execute_reply": "2026-06-26T23:20:00.866830Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Process Capability (method=normal)\n", "==================================\n", "n = 120 mean = 0.94012\n", "sigma (within) = n/a\n", "sigma (overall) = 0.5121\n", "Cp/Cpk sigma = overall\n", "\n", "Cp = n/a\n", "Cpk = 1.341 95% CI (1.16, 1.52) (Cpu=1.341, Cpl= n/a)\n", "Pp = n/a Ppk = 1.341 (Ppu=1.341, Ppl= n/a)\n", "Cpm = n/a\n", "\n", "Assumption checks:\n", " [FAIL] normality (Anderson-Darling): AD=2.74, p=6.14e-07; est. Cpk impact 89.1%; n=120\n", "\n", "Recommendations:\n", " - Data are not normal (AD=2.74, p=6.14e-07); for capability use a non-normal method (method='clements'/'johnson').\n" ] } ], "source": [ "rng = np.random.default_rng(42)\n", "x = np.round(rng.gamma(shape=2.0, scale=0.4, size=120) + 0.2, 3)\n", "flat = pd.DataFrame({\"flatness\": x})\n", "qc_flat = mfgqc.load(flat, measure=\"flatness\").spec(upper=3.0)\n", "\n", "print(qc_flat.capability()) # default: method='normal'" ] }, { "cell_type": "markdown", "id": "3567d11a", "metadata": {}, "source": [ "Read the FAIL line: the Anderson-Darling test rejects normality hard\n", "(`p=6.14e-07`), and the magnitude says it matters (`est. Cpk impact 89.1%`). The\n", "default normal method reports `Cpk = 1.341`, comfortably above the usual 1.33\n", "gate. The recommendation names the conventional remedy. mfgQC did not act on it;\n", "it reported it.\n", "\n", "The chart shows why. Plot the result and look at the right tail against the\n", "fitted normal curve." ] }, { "cell_type": "code", "execution_count": 6, "id": "3ee73a18", "metadata": { "execution": { "iopub.execute_input": "2026-06-26T23:20:00.868055Z", "iopub.status.busy": "2026-06-26T23:20:00.868004Z", "iopub.status.idle": "2026-06-26T23:20:01.117787Z", "shell.execute_reply": "2026-06-26T23:20:01.117419Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "qc_flat.capability().view()" ] }, { "cell_type": "markdown", "id": "51a57f1d", "metadata": {}, "source": [ "Now opt into Box-Cox explicitly and compare the number. This is the only way a\n", "non-normal method runs: you pass `method=` on purpose." ] }, { "cell_type": "code", "execution_count": 7, "id": "e3d92c60", "metadata": { "execution": { "iopub.execute_input": "2026-06-26T23:20:01.118817Z", "iopub.status.busy": "2026-06-26T23:20:01.118727Z", "iopub.status.idle": "2026-06-26T23:20:01.128401Z", "shell.execute_reply": "2026-06-26T23:20:01.128062Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Process Capability (method=boxcox)\n", "==================================\n", "n = 120 mean = 0.94012\n", "sigma (within) = n/a\n", "sigma (overall) = 0.5121\n", "Cp/Cpk sigma = box-cox (lambda=0.017)\n", "\n", "Cp = n/a CI: n/a (non-normal method)\n", "Cpk = 0.8366 CI: n/a (non-normal method) (Cpu=0.8366, Cpl= n/a)\n", "Pp = n/a Ppk = 0.8366 (Ppu=0.8366, Ppl= n/a)\n", "Cpm = n/a\n", "\n", "Assumption checks:\n", " [FAIL] normality (Anderson-Darling): AD=2.74, p=6.14e-07; est. Cpk impact 89.1%; n=120\n", "\n", "Recommendations:\n", " - Data are not normal (AD=2.74, p=6.14e-07); for capability use a non-normal method (method='clements'/'johnson').\n" ] } ], "source": [ "print(qc_flat.capability(method=\"boxcox\")) # explicit opt-in" ] }, { "cell_type": "markdown", "id": "67c1c98c", "metadata": {}, "source": [ "The default normal method reported `Cpk = 1.341`. The Box-Cox number is\n", "`Cpk = 0.8366`. The normality assumption was inflating capability by a third.\n", "That is the reason mfgQC refuses to transform silently: had it auto-applied\n", "Box-Cox, you would never have seen 1.341 and might never have questioned it; had\n", "it auto-suppressed the transform, you would have reported a 1.341 that overstates the process by a third.\n", "\n", "The default never transforms. Non-normal methods (`boxcox`, `clements`,\n", "`johnson`) run only when you pass `method=` explicitly. `qc.capability()` with\n", "no argument always reports the normal-theory number, FAIL flag and all. The\n", "choice to transform is yours, on the record, in the provenance history." ] }, { "cell_type": "markdown", "id": "3a05a4d9", "metadata": {}, "source": [ "## When to opt into a correction, and when not to\n", "\n", "A FAIL gives you two legitimate responses. Picking the right one is an\n", "engineering judgment, not a statistical one.\n", "\n", "**DO choose a non-normal method when the shape is the true nature of the data.**\n", "If the measurement is inherently skewed or bounded (flatness, roundness, taper,\n", "particle counts, time-to-event), then normality was never the right model and\n", "the non-normal index is the honest one. The flatness example above is exactly\n", "this case: opt into `method=\"boxcox\"` (or `\"clements\"`/`\"johnson\"`), and the\n", "choice is recorded in the lineage.\n", "\n", "**DON'T \"correct away\" a FAIL that is signaling a special cause.** A normality\n", "FAIL can also mean your process is not in control: a mixture of two streams, a\n", "drifting mean, a tool-change step, an outlier from a bad part. That is a special\n", "cause to investigate, not a distribution to transform. Box-Cox-ing an unstable\n", "process to make it look capable is precisely what mfgQC refuses to do silently;\n", "do not do it by hand either. Before reaching for a transform, plot the data on a\n", "control chart and confirm the process is stable." ] }, { "cell_type": "markdown", "id": "7fa477f0", "metadata": {}, "source": [ "The discipline: establish stability first, then assess capability. A capability\n", "index, normal or non-normal, only means something for an in-control process. To\n", "make the distinction concrete, here is a second non-normal-looking dataset that\n", "is really two streams stacked together: a tool that drifted to a new setpoint\n", "partway through the run. Its histogram fails normality for a reason that a\n", "transform would only hide." ] }, { "cell_type": "code", "execution_count": 8, "id": "6257d2e6", "metadata": { "execution": { "iopub.execute_input": "2026-06-26T23:20:01.129369Z", "iopub.status.busy": "2026-06-26T23:20:01.129319Z", "iopub.status.idle": "2026-06-26T23:20:01.136611Z", "shell.execute_reply": "2026-06-26T23:20:01.136309Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Process Capability (method=normal)\n", "==================================\n", "n = 120 mean = 10.083\n", "sigma (within) = n/a\n", "sigma (overall) = 0.10302\n", "Cp/Cpk sigma = overall\n", "\n", "Cp = 0.9707 95% CI (0.847, 1.09)\n", "Cpk = 0.9147 95% CI (0.784, 1.05) (Cpu=1.027, Cpl=0.9147)\n", "Pp = 0.9707 Ppk = 0.9147 (Ppu=1.027, Ppl=0.9147)\n", "Cpm = n/a\n", "\n", "Assumption checks:\n", " [FAIL] normality (Anderson-Darling): AD=3.3, p=2.62e-08; est. Cpk impact 0.4%; n=120\n", "\n", "Recommendations:\n", " - Data are not normal (AD=3.3, p=2.62e-08); for capability use a non-normal method (method='clements'/'johnson').\n" ] } ], "source": [ "rng = np.random.default_rng(7)\n", "stream_a = rng.normal(10.00, 0.05, size=60) # before the tool change\n", "stream_b = rng.normal(10.18, 0.05, size=60) # after the tool change\n", "mixed = np.round(np.concatenate([stream_a, stream_b]), 4)\n", "mix = pd.DataFrame({\"bore\": mixed, \"order\": np.arange(1, 121)})\n", "qc_mix = mfgqc.load(mix, measure=\"bore\").spec(lower=9.8, upper=10.4)\n", "\n", "print(qc_mix.capability())" ] }, { "cell_type": "markdown", "id": "725e8629", "metadata": {}, "source": [ "The capability report flags non-normality again, but the cause here is a step\n", "change, not an inherently skewed measurement. The way to see it is an\n", "individuals control chart in run order. Plot one, and read the assumption block\n", "and the out-of-control signals." ] }, { "cell_type": "code", "execution_count": 9, "id": "8dd12470", "metadata": { "execution": { "iopub.execute_input": "2026-06-26T23:20:01.137692Z", "iopub.status.busy": "2026-06-26T23:20:01.137643Z", "iopub.status.idle": "2026-06-26T23:20:01.141875Z", "shell.execute_reply": "2026-06-26T23:20:01.141575Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Control Chart: i_mr (specified); rules=nelson\n", "=============================================\n", "Individual: CL=10.083 UCL=10.221 LCL=9.9439\n", "MR: CL=0.052171 UCL=0.17044 LCL=0\n", "\n", "Out-of-control signals: 104\n", " point 4 (location): nelson_5 - two of three points beyond 2 sigma (same side)\n", " point 5 (location): nelson_5 - two of three points beyond 2 sigma (same side)\n", " point 6 (location): nelson_5 - two of three points beyond 2 sigma (same side)\n", " point 7 (location): nelson_6 - four of five points beyond 1 sigma (same side)\n", " point 9 (location): nelson_2 - nine points in a row on one side of CL\n", " point 10 (location): nelson_2 - nine points in a row on one side of CL\n", " point 11 (location): nelson_2 - nine points in a row on one side of CL\n", " point 12 (location): nelson_2 - nine points in a row on one side of CL\n", " point 13 (location): nelson_2 - nine points in a row on one side of CL\n", " point 14 (location): nelson_2 - nine points in a row on one side of CL\n", " point 15 (location): nelson_2 - nine points in a row on one side of CL\n", " point 16 (location): nelson_2 - nine points in a row on one side of CL\n", " point 17 (location): nelson_1 - one point beyond 3 sigma\n", " point 18 (location): nelson_2 - nine points in a row on one side of CL\n", " point 19 (location): nelson_1 - one point beyond 3 sigma\n", " point 20 (location): nelson_1 - one point beyond 3 sigma\n", " point 21 (location): nelson_1 - one point beyond 3 sigma\n", " point 22 (location): nelson_2 - nine points in a row on one side of CL\n", " point 23 (location): nelson_1 - one point beyond 3 sigma\n", " point 24 (location): nelson_2 - nine points in a row on one side of CL\n", " point 25 (location): nelson_2 - nine points in a row on one side of CL\n", " point 26 (location): nelson_2 - nine points in a row on one side of CL\n", " point 27 (location): nelson_1 - one point beyond 3 sigma\n", " point 28 (location): nelson_2 - nine points in a row on one side of CL\n", " point 29 (location): nelson_2 - nine points in a row on one side of CL\n", " point 30 (location): nelson_2 - nine points in a row on one side of CL\n", " point 31 (location): nelson_1 - one point beyond 3 sigma\n", " point 32 (location): nelson_2 - nine points in a row on one side of CL\n", " point 33 (location): nelson_2 - nine points in a row on one side of CL\n", " point 34 (location): nelson_2 - nine points in a row on one side of CL\n", " point 35 (location): nelson_2 - nine points in a row on one side of CL\n", " point 36 (location): nelson_2 - nine points in a row on one side of CL\n", " point 37 (location): nelson_2 - nine points in a row on one side of CL\n", " point 38 (location): nelson_2 - nine points in a row on one side of CL\n", " point 39 (location): nelson_2 - nine points in a row on one side of CL\n", " point 40 (location): nelson_2 - nine points in a row on one side of CL\n", " point 41 (location): nelson_2 - nine points in a row on one side of CL\n", " point 42 (location): nelson_2 - nine points in a row on one side of CL\n", " point 43 (location): nelson_1 - one point beyond 3 sigma\n", " point 44 (location): nelson_2 - nine points in a row on one side of CL\n", " point 45 (location): nelson_2 - nine points in a row on one side of CL\n", " point 46 (location): nelson_1 - one point beyond 3 sigma\n", " point 47 (location): nelson_2 - nine points in a row on one side of CL\n", " point 48 (location): nelson_2 - nine points in a row on one side of CL\n", " point 49 (location): nelson_2 - nine points in a row on one side of CL\n", " point 52 (location): nelson_1 - one point beyond 3 sigma\n", " point 55 (location): nelson_6 - four of five points beyond 1 sigma (same side)\n", " point 56 (location): nelson_6 - four of five points beyond 1 sigma (same side)\n", " point 57 (location): nelson_6 - four of five points beyond 1 sigma (same side)\n", " point 58 (location): nelson_6 - four of five points beyond 1 sigma (same side)\n", " point 59 (location): nelson_2 - nine points in a row on one side of CL\n", " point 60 (location): nelson_2 - nine points in a row on one side of CL\n", " point 63 (location): nelson_5 - two of three points beyond 2 sigma (same side)\n", " point 65 (location): nelson_6 - four of five points beyond 1 sigma (same side)\n", " point 66 (location): nelson_6 - four of five points beyond 1 sigma (same side)\n", " point 67 (location): nelson_1 - one point beyond 3 sigma\n", " point 68 (location): nelson_1 - one point beyond 3 sigma\n", " point 69 (location): nelson_2 - nine points in a row on one side of CL\n", " point 70 (location): nelson_2 - nine points in a row on one side of CL\n", " point 71 (location): nelson_2 - nine points in a row on one side of CL\n", " point 74 (location): nelson_6 - four of five points beyond 1 sigma (same side)\n", " point 75 (location): nelson_1 - one point beyond 3 sigma\n", " point 76 (location): nelson_5 - two of three points beyond 2 sigma (same side)\n", " point 77 (location): nelson_6 - four of five points beyond 1 sigma (same side)\n", " point 78 (location): nelson_6 - four of five points beyond 1 sigma (same side)\n", " point 79 (location): nelson_6 - four of five points beyond 1 sigma (same side)\n", " point 80 (location): nelson_1 - one point beyond 3 sigma\n", " point 81 (location): nelson_2 - nine points in a row on one side of CL\n", " point 82 (location): nelson_2 - nine points in a row on one side of CL\n", " point 83 (location): nelson_2 - nine points in a row on one side of CL\n", " point 84 (location): nelson_2 - nine points in a row on one side of CL\n", " point 85 (location): nelson_2 - nine points in a row on one side of CL\n", " point 86 (location): nelson_2 - nine points in a row on one side of CL\n", " point 87 (location): nelson_2 - nine points in a row on one side of CL\n", " point 88 (location): nelson_2 - nine points in a row on one side of CL\n", " point 89 (location): nelson_1 - one point beyond 3 sigma\n", " point 90 (location): nelson_2 - nine points in a row on one side of CL\n", " point 91 (location): nelson_2 - nine points in a row on one side of CL\n", " point 92 (location): nelson_2 - nine points in a row on one side of CL\n", " point 93 (location): nelson_2 - nine points in a row on one side of CL\n", " point 94 (location): nelson_1 - one point beyond 3 sigma\n", " point 95 (location): nelson_2 - nine points in a row on one side of CL\n", " point 96 (location): nelson_2 - nine points in a row on one side of CL\n", " point 97 (location): nelson_2 - nine points in a row on one side of CL\n", " point 98 (location): nelson_2 - nine points in a row on one side of CL\n", " point 99 (location): nelson_2 - nine points in a row on one side of CL\n", " point 104 (location): nelson_1 - one point beyond 3 sigma\n", " point 105 (location): nelson_6 - four of five points beyond 1 sigma (same side)\n", " point 106 (location): nelson_6 - four of five points beyond 1 sigma (same side)\n", " point 107 (location): nelson_6 - four of five points beyond 1 sigma (same side)\n", " point 108 (location): nelson_5 - two of three points beyond 2 sigma (same side)\n", " point 109 (location): nelson_2 - nine points in a row on one side of CL\n", " point 110 (location): nelson_2 - nine points in a row on one side of CL\n", " point 111 (location): nelson_2 - nine points in a row on one side of CL\n", " point 112 (location): nelson_2 - nine points in a row on one side of CL\n", " point 113 (location): nelson_2 - nine points in a row on one side of CL\n", " point 114 (location): nelson_2 - nine points in a row on one side of CL\n", " point 115 (location): nelson_2 - nine points in a row on one side of CL\n", " point 116 (location): nelson_2 - nine points in a row on one side of CL\n", " point 117 (location): nelson_2 - nine points in a row on one side of CL\n", " point 118 (location): nelson_2 - nine points in a row on one side of CL\n", " point 119 (location): nelson_1 - one point beyond 3 sigma\n", " point 120 (location): nelson_2 - nine points in a row on one side of CL\n", " point 61 (dispersion): nelson_1 - one point beyond control limits\n", "\n", "Assumption checks:\n", " [FAIL] independence (lag-1 autocorrelation): r=0.797, p=0; n=120\n", "\n", "Recommendations:\n", " - Observations are autocorrelated (lag-1 r=0.80); control limits are unreliable - consider a time-series chart (e.g. EWMA).\n" ] } ], "source": [ "print(qc_mix.control_chart(kind=\"i_mr\"))" ] }, { "cell_type": "markdown", "id": "16d02df2", "metadata": {}, "source": [ "The chart finds the special cause directly. The independence check fails (the\n", "points are autocorrelated because the mean steps), and the run after the tool\n", "change sits outside limits computed from the whole run. This is a process to\n", "fix, not a distribution to transform. Here is the same chart drawn." ] }, { "cell_type": "code", "execution_count": 10, "id": "3b0de155", "metadata": { "execution": { "iopub.execute_input": "2026-06-26T23:20:01.142767Z", "iopub.status.busy": "2026-06-26T23:20:01.142717Z", "iopub.status.idle": "2026-06-26T23:20:01.237407Z", "shell.execute_reply": "2026-06-26T23:20:01.237069Z" } }, "outputs": [ { "data": { "image/png": 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" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "qc_mix.control_chart(kind=\"i_mr\").view()" ] }, { "cell_type": "markdown", "id": "423c46b0", "metadata": {}, "source": [ "Read the two cases together. The flatness data is genuinely skewed: the right\n", "response is to opt into a non-normal method on purpose. The mixed-stream data is\n", "unstable: the right response is to find and fix the tool change, then reassess\n", "capability on the stable process. Both fail a normality check in the capability\n", "report, so the capability report alone cannot tell them apart. The control chart\n", "is how you make the distinction, and the engineering judgment about which case\n", "you are in is the one mfgQC leaves to you." ] }, { "cell_type": "markdown", "id": "ee96546c", "metadata": {}, "source": [ "## Next\n", "\n", "- [Quickstart](/guide/quickstart/) walks the `load → spec → analysis` flow and the result surface.\n", "- [Non-normal capability](/reference/non-normal-capability/) covers choosing among Box-Cox, Clements, and Johnson.\n", "- [Control charts](/reference/control-charts/) establish stability before judging capability.\n", "- The [Reference](/reference/) gives the formula, assumptions, and source standard behind every method." ] } ], "metadata": { "jupytext": { "main_language": "python" }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.13.7" } }, "nbformat": 4, "nbformat_minor": 5 }