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020			_a0-07-024117-1
040			_aAOBREGON _bspa _cAOBREGON
100			_aEugene L. Grant
245			_aSTATISTICAL QUALITY CONTROL /
250			_a6
260			_bMc Graw Hill _aU.S.A _c1974
300			_a714 Páginas _bIlustraciones, tablas, graficos _c23.5cm
490	0		_aseries
500			_a2 Ejemplares
504			_aEditorial Mc Graw Hill ISBN 0-07-024117-1
505			_aContents 29 List of Examples Preface 1 Introduction XV 1 Part One Statistical Process Control 2 Why the Control Chart Works; Some Statistical Concepts frequency distributions; averages and measures of dispersion, statistics and parameters; normal curve; estimating parameters 31 3. Why the Control Chart Works; Some Examples control-chart limit factors, Shewhart's normal bowl; lack of control; interpreting patterns of variation 74 4 Directions for Simple X and R Charts control-chart objectives: subgrouping; recording measurements; plotting control charts; drawing conclusions; revising control limits; use of computer software 115 5 Rational Subgrouping: Analyzing Process Capability sources of variation. isolating variation sources; using control charts to analyze variation: process capability indexes; other statistical techniques 154 6 Some Fundamentals of the Theory of Probability definitions and concepts: basic theorems; hypergeometric, binomial, and Poisson distributions, normal distribution and Central Limit Theorem; estimating parameters: extreme runs; computer programs 181 7 The Control Chart for Fraction Rejected p chart; trial limits, standard values, revising control limits, steps for control-chart setup, np chart. interpretation of lack of control 239 8 The Control Chart for Nonconformities chart; a chart, probability limits: Pareto Analysis: Cause and Effect Analysis; weighting nonconformities 274 9 Some Special Process Control Procedures variations on the standard Shewhart chart; charts for medians, homogeneity tests, probability limits. charts for moving averages: linear trend charts, narrow limit gauging. reject limits, acceptance control charts 302 10 Cumulative Sum Control Charts & chart and mask construction: Average Run Length. R chart mask construction 348 11 Some Aspects of Specifications and Tolerances setting design specifications: interpreting pilot runs, combinations of tolerances, measurement error 361 Part Two Acceptance Sampling 391 12 Some Fundamental Concepts in Acceptance Sampling operating characteristic curves, distribution assumptions; indexing systems of acceptance plans; single-, double, and multi-level plans 393 13 The Dodge-Romig System for Lot-by-Lot Acceptance Sampling by Attributes indexing plans by Lot Tolerance Percent Defective and Average Outgoing Quality Limit; calculation of AOQ: Average Fraction Inspected; plan operation; minimizing total inspection 426 14 An AQL System for Lot-by-Lot Acceptance Sampling by Attributes (ABC-STD-105) indexing by Acceptable Quality Level (AQL); operation of a system of plans; plan OC curves vs. system OC curves; Average Sample Number (ASN); classification of defects: Limiting Quality (LQ); ANSI standard 21.4 450 15 Certain Other Plans for Lot-by-Lot Acceptance Sampling by Attributes custom sampling plans; sequential sampling; Philips Standard Sampling System; a simplified AQL system; chain sampling 492 16 Acceptance Inspection for Continuous Production continuous sampling plans CSP-1, CSP-2, and CSP-3; multilevel continuous sampling; skip-lot sampling Plans SkSP-1 and SkSP-2 520 17 Acceptance Sampling by Variables Lot Plot method; OC curves for known variables sampling plans; MIL-STD-414 system of plans, known and unknown σ; ANSI standard 21.9; proof testing 537 18 Some Aspects of Life Testing and Reliability relationship of life testing to acceptance sampling; constant failure rate assumption; OC curves:
520			_aThis is a practical working manual. It deals primarily with various types of Shewhart control charts and with various types of acceptance sampling systems and procedures. These are simple but powerful techniques that have been widely used in many industries and in many countries throughout the world to improve product quality and to reduce costs. The most effective use of these techniques depends upon their being understood by production and inspection supervisors. by engineers, and by management.
526			_aIngeniería Industrial
650			_aEstadística
700			_aRichard S. Leavenworth
942			_cLIB _2ddc _e6
945			_a1 _badmin _c1261 _dJenny Viridiana Quiroz Linares
999			_c1322 _d1322