Title	Frontiers in Statistical Quality Control [electronic resource] / edited by Hans-Joachim Lenz, Peter-Theodor Wilrich
Imprint	Heidelberg : Physica-Verlag HD : Imprint: Physica, 1997
Connect to	http://dx.doi.org/10.1007/978-3-642-59239-3
Descript	X, 297 p. online resource

Like the preceding volumes, and met with a lively response, the present volume is collecting contributions stressed on methodology or successful industrial applications. The papers are classified under four main headings: sampling inspection, process quality control, data analysis and process capability studies and finally experimental design

1: Sampling Inspection -- An Adaptive Sampling System with a Fuzzy Controller -- Determination of Sample Sizes for Double Sampling Attribute Plans -- Unbiased Estimation of Generalized Moments of Process Curves -- On the Non-Robustness of Maximum-Likelihood Sampling Plans by Variables -- Normal Approximations to the Distribution Function of the Symmetric Beta Distribution -- 2: Process Quality Control -- A Mathematical Framework for Statistical Process Control -- A Review of Statistical and Fuzzy Quality Control Charts Based on Categorical Data -- Efficient Estimation of Control Chart Parameters -- Control Charts for Dependent and Multivariate Measurements -- On EWMA Charts for Time Series -- Statistical Process Control for Autocorrelated Processes: A Case-Study -- Quality Control of a Continuously Monitored Production Process -- Group Sequential Design with Delayed Observations for Selecting One of Two Processes in a Production System -- Design of the ($$ \overline x $$, s) Control Chart Based on Kullback-Leibler Information -- 3: Data Analysis And Process Capability Studies -- Measurement Error Effects on the Performance of Process Capability Indices -- A New Approach for Describing and Controlling Process Capability -- On the Use of Field Failure Data for Repairable Systems to Identify Sources of Variation -- 4: Experimental Design -- Control Chart Method for Analyzing Staggered Nested Data -- Prediction Properties of the Process Variance Using the Combined Array -- Joint Analysis of Mean and Variance Function Based on Second Order Polynomials -- Dependability Improvement Through Unreplicated Experimental Designs