Table of Contents

Foreword
Introduction
1 Scope
2 Normative references
3 Terms and definitions
4 Illustration of value and role of statistical method
   through examples
   4.1 Statistical method
   4.2 Example 1: Strength of wire
        4.2.1 General
        4.2.2 Overall test results and lower specification
               limit
        4.2.3 Initial analysis
        4.2.4 Preliminary investigation
        4.2.5 General discussion on findings
        4.2.6 Explanation of statistical terms and tools
               used in this example
   4.3 Example 2: Mass of fabric
        4.3.1 General
        4.3.2 Test results and specification limits
        4.3.3 Discussion of specific results
        4.3.4 Discussion on general findings
   4.4 Example 3: Mass fraction of ash (in %) in a cargo
        of coal
        4.4.1 General
        4.4.2 Test results (reference ISO 11648-1: Statistical
               aspects of sampling from bulk materials)
        4.4.3 Initial graphical analysis of specific results
        4.4.4 Benefits of a statistically sound sampling plan
        4.4.5 General conclusions
5 Introduction to basic statistical tools
   5.1 General
   5.2 Basic statistical terms and measures
   5.3 Presentation of data
        5.3.1 Dot or line plot
        5.3.2 Tally chart
        5.3.3 Stem and leaf plot
        5.3.4 Box plot
        5.3.5 Multi-vari chart
        5.3.6 Position-Dimension (P-D) diagram
        5.3.7 Graphical portrayal of frequency distributions
        5.3.8 The normal distribution
        5.3.9 The Weibull distribution
        5.3.10 Graphs
        5.3.11 Scatter diagram and regression
        5.3.12 Pareto (or Lorenz) diagram
        5.3.13 Cause and effect diagram
6 Variation and sampling considerations
   6.1 Statistical control and process capability
        6.1.1 Statistical control
        6.1.2 Erratic variation
        6.1.3 Systematic variation
        6.1.4 Systematic changes with time
        6.1.5 Statistical indeterminacy
        6.1.6 Non-normal variation
        6.1.7 Quality level and process capability
   6.2 Sampling considerations
7 Methods of conformity assessment
   7.1 The statistical concept of a population
   7.2 The basis of securing conformity to specification
        7.2.1 The two principal methods
        7.2.2 Considerations of importance to the customer
        7.2.3 Considerations of importance to the supplier
8 The statistical relationship between sample and population
   8.1 The variation of the mean and the standard deviation in
        samples
        8.1.1 General
        8.1.2 Variation of means
        8.1.3 Variation of standard deviations
   8.2 The reliability of a mean estimated from stratified
        and duplicate sampling
        8.2.1 Stratified sampling
        8.2.2 Duplicate sampling
   8.3 Illustration of the use of the mean mass, and
        the lowest mass, in a sample of prescribed
        size of specimens of fabric
   8.4 Tests and confidence intervals for means and standard
        deviations
        8.4.1 Confidence intervals for means and standard
               deviations
        8.4.2 Tests for means and standard deviations
        8.4.3 Equivalence of methods of testing hypotheses
   8.5 Simultaneous variation in the sample mean and in the
        sample standard deviation
   8.6 Tests and confidence intervals for proportions
        8.6.1 Attributes
        8.6.2 Estimating a proportion
        8.6.3 Confidence intervals for a proportion
        8.6.4 Comparison of a proportion with a given value
        8.6.5 Comparison of two proportions
        8.6.6 Sample size determination
   8.7 Prediction intervals
        8.7.1 One-sided prediction interval for the next m
               observations
        8.7.2 Two-sided prediction interval for the next m
               observations
        8.7.3 One and two-sided prediction intervals for the
               mean of the next m observations
   8.8 Statistical tolerance intervals
        8.8.1 Statistical tolerance intervals for normal
               populations
        8.8.2 Statistical tolerance intervals for populations
               of an unknown distributional type
        8.8.3 Tables for statistical tolerance intervals
   8.9 Estimation and confidence intervals for the Weibull
        distribution
        8.9.1 The Weibull distribution
   8.10 Distribution-free methods: estimation and confidence
        intervals for a median
9 Acceptance sampling
   9.1 Methodology
   9.2 Rationale
   9.3 Some terminology of acceptance sampling
        9.3.1 Acceptance quality limit (AQL)
        9.3.2 Limiting quality (LQ)
        9.3.3 Classical versus economic methods
        9.3.4 Inspection levels
        9.3.5 Inspection severity and switching rules
        9.3.6 Use of 'non-accepted' versus 'rejected'
   9.4 Acceptance sampling by attributes
        9.4.1 General
        9.4.2 Single sampling
        9.4.3 Double sampling
        9.4.4 Multiple sampling
        9.4.5 Sequential sampling
        9.4.6 Continuous sampling
        9.4.7 Skip-lot sampling
        9.4.8 Audit sampling
        9.4.9 Sampling for parts per million
        9.4.10 Isolated lots
        9.4.11 Accept-zero plans
   9.5 Acceptance sampling by variables - Single quality
        characteristic
        9.5.1 General
        9.5.2 Single sampling plans by variables for known
               process standard deviation - The 'sigma'
               method
        9.5.3 Single sampling plans by variables for unknown
               process standard deviation - The 's' method
        9.5.4 Double sampling plans by variables
        9.5.5 Sequential sampling plans by variables for known
               process standard deviation
        9.5.6 Accept-zero plans by variables
   9.6 Multiple quality characteristics
        9.6.1 Classification of quality characteristics
        9.6.2 Unifying theme
        9.6.3 Inspection by attributes for nonconforming items
        9.6.4 Inspection by attributes for nonconformities
        9.6.5 Independent
        9.6.6 Dependent variables
        9.6.7 Attributes and variables
10 Statistical process control (SPC)
   10.1 Process focus
   10.2 Essence of SPC
   10.3 Statistical process control or statistical product
        control?
   10.4 Over-control, under-control and control of processes
        10.4.1 General
        10.4.2 Scenario 1: Operator reacts to each individual
               sample giving rise to process over-control
        10.4.3 Scenario 2: Operator monitors a process using
               a run chart giving rise to haphazard control
        10.4.4 Scenario 3: Monitoring using SPC chart with a
               potential for effective control
   10.5 Key statistical steps in establishing a standard
        performance-based control chart
        10.5.1 General
        10.5.2 Monitoring strategy
        10.5.3 Construction of a standard control chart
   10.6 Interpretation of standard Shewhart-type control charts
   10.7 Selection of an appropriate control chart for a
        particular use
        10.7.1 Overview
        10.7.2 Shewhart-type control charts
        10.7.3 Cumulative sum (cusum) charts
11 Process capability
   11.1 Overview
   11.2 Process performance versus process capability
   11.3 Process capability for measured (i.e. variables) data
        11.3.1 General
        11.3.2 Estimation of process capability (normally
               distributed data)
        11.3.3 Estimation of process capability (non-normally
               distributed data)
   11.4 Process capability indices
        11.4.1 General
        11.4.2 The C[p] index
        11.4.3 The C[pk] family of indices
        11.4.4 The C[pm] index
   11.5 Process capability for attribute data
12 Statistical experimentation and standards
   12.1 Basic concepts
        12.1.1 What is involved in experimentation?
        12.1.2 Why experiment?
        12.1.3 Where does statistics come in?
        12.1.4 What types of standard experimental designs are
               there and how does one make a choice of which
               to use?
13 Measuring systems
   13.1 Measurements and standards
   13.2 Measurements, result quality and statistics
   13.3 Examples of statistical methods to ensure quality of
        measured data
        13.3.1 Example 1: Resolution
        13.3.2 Example 2: Bias and precision
        13.3.3 Precision - Repeatability
        13.3.4 Precision - Reproducibility
Annex A (informative) Measured data control charts:
        Formulae and constants
Bibliography
Index

Abstract

Specifies a broad range of statistical methods applicable to the management, control and improvement of processes.

General Product Information

Published
Document Type	Standard
Status	Current
Publisher	International Organization for Standardization
Pages
ISBN
Committee	TC 69