Description
Book SynopsisStatistical Methods for SPC and TQM sets out to fill the gap for those in statistical process control (SPC) and total quality management (TQM) who need a practical guide to the logical basis of data presentation, control charting, and capability indices.
Statistical theory is introduced in a practical context, usually by way of numerical examples. Several methods familiar to statisticians have been simplified to make them more accessible. Suitable tabulations of these functions are included; in several cases, effective and simple approximations are offered.
Contents
Data Collection and Graphical Summaries
Numerical Data Summaries-Location and Dispersion
Probability and Distribution
Sampling, Estimation, and Confidence
Sample Tests of Hypothesis; Significance Tests
Control Charts for Process Management and Improvement
Control Charts for Average and Variation
Control Charts for Single-Valued Observations
Control Charts for Attributes and Events
Con
Trade Review"This book should be compulsory reading for every quality management consultant, applied statistician, and student of quality or statistics. It finds the right balance between data analysis and commonsense management which few people ever strike."
-The Statistician
Table of ContentsIntroduction - statistics, SPC and total quality; data collection and graphical summaries; numerical data summaries - location and dispersion; probability and distribution; sampling, estimation and confidence; simple tests of hypotheses - "significance tests"; control charts for process management and improvement; control charts for average and variation; control charts for "single-valued" observations; control charts for attributes and events; control charts - problems and special cases; cusum methods; process capability - attributes, events and normally distributed data; capability - non-normal distributions; evaluating the precision of a measurement system (gauge capability); getting more from control chart data; SPC in "non-product" applications. Appendices: linear combinations of independent variables; linear combination of correlated variables; products and quotients; non-linear functions; dealing with composite functions; other useful standard errors.
