Introduction to statistical quality control montgomery pdf download
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PART 1 INTRODUCTION 1 1 QUALITY IMPROVEMENT IN THE MODERN BUSINESS ENVIRONMENT 3 Chapter Overview and Learning Objectives 3 1.1 The Meaning of Quality and Quality Improvement 4 Dimensions of Quality 4 Quality Engineering Terminology 8 1.2 A Brief History of Quality Control and Improvement 9 1.3 Statistical Methods for Quality Control and Improvement 13 1.4 Management Aspects of Quality Improvement 16 Quality Philosophy and Management Strategies 17 The Link Between Quality and Productivity 35 Supply Chain Quality Management 36 Quality Costs 38 Legal Aspects of Quality 44 Implementing Quality Improvement 45 2 THE DMAIC PROCESS 48 Chapter Overview and Learning Objectives 48 2.1 Overview of DMAIC 49 2.2 The Define Step 52 2.3 The Measure Step 54 2.4 The Analyze Step 55 2.5 The Improve Step 56 2.6 The Control Step 57 2.7 Examples of DMAIC 57 Litigation Documents 57 Improving On- Time Delivery 59 Improving Service Quality in a Bank 62 PART 2 STATISTICAL METHODS USEFUL IN QUALITY CONTROL AND IMPROVEMENT 65 3 MODELING PROCESS QUALITY 67 Chapter Overview and Learning Objectives 68 3.1 Describing Variation 68 The Stem-and- Leaf Plot 68 The Histogram 70 Numerical Summary of Data 73 The Box Plot 75 Probability Distributions 76 3.2 Important Discrete Distributions 80 The Hypergeometric Distribution 80 The Binomial Distribution 81 The Poisson Distribution 83 The Negative Binomial and Geometric Distributions 86 3.3 Important Continuous Distributions 88 The Normal Distribution 88 The Lognormal Distribution 90 The Exponential Distribution 92 The Gamma Distribution 93 The Weibull Distribution 95 3.4 Probability Plots 97 Normal Probability Plots 97 Other Probability Plots 99 3.5 Some Useful Approximations 100 The Binomial.
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Article Fred Spiring University of Manitoba Message author Remove suggestion Technometrics ( Impact Factor: 1.81). 01/2012; 49(1 108-109. DOI: 10.1198/tech.2007.s465 Figures in this publication Get notified about updates to this publication Follow publication Full-text DOI: · Available from: Fred Spiring, Aug 05, 2014 Download full-text Share Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by Ro MEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable. References (1) Cited In (0) Sorted by: Order of availability Order of availability Appearance in publication Article: Accounting for dropout bias using mixed-effect models Craig H. Mallinckrodt · W. Scott Clark · Stacy R. David [ Show abstract] [ Hide abstract] ABSTRACT: Treatment effects are often evaluated by comparing change over time in outcome measures. However, valid analyses of longitudinal data can be problematic when subjects discontinue (dropout) prior to completing the study. This study assessed the merits of likelihood-based repeated measures analyses ( MMRM) compared with fixed-effects analysis of variance where missing values were imputed using the last observation carried forward approach ( LOCF) in accounting for dropout bias. Comparisons were made in simulated data and in data from a randomized clinical trial. Subject dropout was introduced in the simulated data to generate ignorable and nonignorable missingness. Estimates of treatment group differences in mean change from baseline to endpoint from MMRM were, on average, markedly closer to the true value than estimates from LOCF in every scenario simulated. Standard errors and confidence.