ISO/TR 20693:2019 PDF

Name: ISO/TR 20693:2019 — Statistical methods for implementation of Six Sigma — Selected illustrations of distribution identification studies
SKU: ISO/TR 20693:2019
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Statistical methods for implementation of Six Sigma — Selected illustrations of distribution identification studies

Название (английский):

ISO/TR 20693:2019

Statistical methods for implementation of Six Sigma — Selected illustrations of distribution identification studies

Статус документа:: Действующий
Формат:: Электронный (PDF)
Количество страниц:: 33
Дата публикации:: 24 апреля 2019 г.
Издание:: ISO TR 20693 edition 1 version 1
ICS:: 03.120.30

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Описание (английский):

This document provides guidelines for the identification of distributions related to the implementation of Six Sigma. Examples are given to illustrate the related graphical and numerical procedures. It only considers one dimensional distribution with one mode. The underlying distribution is either continuous or discrete.

Abstract

Похожие стандарты

Упомянутые в описании и другие стандарты ISO

Overview - ISO/TR 20693:2019 (Statistical methods for Six Sigma)

ISO/TR 20693:2019 provides guidance for distribution identification within Six Sigma implementations. The technical report documents graphical and numerical procedures to identify one‑dimensional, unimodal probability distributions - either continuous or discrete. It emphasizes exploratory data analysis (EDA), hypothesis testing (including goodness‑of‑fit and normality tests), and pragmatic decision steps that fit within the DMAIC lifecycle (recommended to start in the Measure phase and continue through Improve and Control).

Key topics and technical highlights

Scope: One‑dimensional, single‑mode distributions; continuous or discrete data only.

Exploratory Data Analysis (EDA): Use of descriptive statistics (mean, median, skewness, kurtosis, quartiles) and visual tools such as histograms, boxplots, stem‑and‑leaf, and Q‑Q plots to form distribution hypotheses.

Distribution selection: Guidance to narrow candidate families based on process knowledge (e.g., positive‑only, discrete counts) and Occam’s Razor (favor simpler models like exponential, normal, Poisson when appropriate).

Graphical and numerical procedures: Illustrated workflows for discrete and continuous cases, combining visual diagnostics with formal tests and goodness‑of‑fit methods.

Practical workflow: Steps include stating objectives, formulating model theory, collecting/preparing/exploring data, selecting candidate distributions, performing goodness‑of‑fit testing, and drawing conclusions.

Illustrative examples: Annexed case studies covering lottery uniformity tests, post‑release technical issues, software effort estimation, and warranty period determination to demonstrate applied techniques.

Applications - practical value

Supports Six Sigma project teams in selecting appropriate parametric methods by verifying underlying distributional assumptions.

Enables quality engineers, reliability engineers, data analysts, and statisticians to model process behavior, estimate life or failure distributions, and choose correct statistical tests for inference and control.

Useful for tasks like process baseline characterization (Measure), root‑cause analysis (Analyse), validating improvements (Improve), and ongoing monitoring (Control).

Helps avoid misleading automation in software packages by combining contextual process knowledge with formal statistical methods.

Related standards

ISO 3534‑1 - Statistics: vocabulary and symbols (referenced normative).

ISO 16269‑4 - Statistical interpretation of data (boxplot terminology referenced).

Keywords: ISO/TR 20693:2019, Six Sigma, distribution identification, exploratory data analysis, goodness of fit, Q‑Q plot, histogram, normality test, DMAIC.

ISO/TR 20693:2019 PDF

Abstract

Похожие стандарты

Overview - ISO/TR 20693:2019 (Statistical methods for Six Sigma)

Key topics and technical highlights

Applications - practical value

Who should use this standard

Related standards

Технические детали

SIST ISO 3534-1:2008

Statistics - Vocabulary and symbols - Part 1: General statistical terms and terms used in probability

BS ISO 16269-4:2010

Statistical interpretation of data. Detection and treatment of outliers.

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