Details

Statistical Quality Control


Statistical Quality Control

Using MINITAB, R, JMP, and Python
1. Aufl.

von: Bhisham C Gupta

96,99 €

Verlag: Wiley
Format: EPUB
Veröffentl.: 23.07.2021
ISBN/EAN: 9781119671725
Sprache: englisch
Anzahl Seiten: 400

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Beschreibungen

<b>STATISTICAL QUALITY CONTROL</b> <p><b>Provides a basic understanding of statistical quality control (SQC) and demonstrates how to apply the techniques of SQC to improve the quality of products in various sectors</b><p>This book introduces Statistical Quality Control and the elements of Six Sigma Methodology, illustrating the widespread applications that both have for a multitude of areas, including manufacturing, finance, transportation, and more. It places emphasis on both the theory and application of various SQC techniques and offers a large number of examples using data encountered in real life situations to support each theoretical concept.<p><i>Statistical Quality Control: Using MINITAB, R, JMP and Python</i> begins with a brief discussion of the different types of data encountered in various fields of statistical applications and introduces graphical and numerical tools needed to conduct preliminary analysis of the data. It then discusses the basic concept of statistical quality control (SQC) and Six Sigma Methodology and examines the different types of sampling methods encountered when sampling schemes are used to study certain populations. The book also covers Phase 1 Control Charts for variables and attributes; Phase II Control Charts to detect small shifts; the various types of Process Capability Indices (CPI); certain aspects of Measurement System Analysis (MSA); various aspects of PRE-control; and more. This helpful guide also<ul><li>Focuses on the learning and understanding of statistical quality control for second and third year undergraduates and practitioners in the field</li><li>Discusses aspects of Six Sigma Methodology</li><li>Teaches readers to use MINITAB, R, JMP and Python to create and analyze charts</li><li>Requires no previous knowledge of statistical theory</li><li>Is supplemented by an instructor-only book companion site featuring data sets and a solutions manual to all problems, as well as a student book companion site that includes data sets and a solutions manual to all odd-numbered problems</li></ul><p><i>Statistical Quality Control: Using MINITAB, R, JMP and Python</i> is an excellent book for students studying engineering, statistics, management studies, and other related fields and who are interested in learning various techniques of statistical quality control. It also serves as a desk reference for practitioners who work to improve quality in various sectors, such as manufacturing, service, transportation, medical, oil, and financial institutions. It‘s also useful for those who use Six Sigma techniques to improve the quality of products in such areas.
<p>Chapter 1- QUALITY IMPROVEMENT AND MANAGEMENT1</p> <p>1.1 Introduction 1</p> <p>1.2 Statistical Quality Control 1</p> <p>1.3 Implementing Quality Improvement 9</p> <p>1.4 Managing Quality Improvement 15</p> <p>Chapter 2 - BASIC CONCEPTS OF THE SIX SIGMA METHODOLOGY20</p> <p>2.1 Introduction 20</p> <p>2.2 What is Six Sigma? 20</p> <p>2.3 Is Six Sigma New? 29</p> <p>2.4 Quality Tools Used in Six Sigma 30</p> <p>2.5 Six Sigma Benefits and Criticism 40</p> <p>Review Practice Problems 42</p> <p>Chapter 3- DESCRIBING QUANTITATIVE AND QUALITATIVE DATA 44</p> <p>3.1 Introduction 44</p> <p>3.2 Classification of Various Types of Data 44</p> <p>3.3 Analyzing Data Using Graphical Tools 47</p> <p>3.4 Describing Data Graphically52</p> <p>3.5 Analyzing Data Using Numerical Tools 71</p> <p>3.6 Some Important Probability Distributions 87</p> <p>Review Practice Problems107</p> <p>Chapter 4 - SAMPLING METHODS 118</p> <p>4.1 Introduction 118</p> <p>4.2 Basic Concepts of Sampling 118</p> <p>4.3 Simple Random Sampling123</p> <p>4.4 Stratified Random Sampling 132</p> <p>4.5 Systematic Random Sampling139</p> <p>4.6 Cluster Random Sampling145</p> <p>Review Practice Problems 151</p> <p>Chapter 5 - Phase I Quality Control Charts for Variables 157</p> <p>5.1 Introduction 157</p> <p>5.2 Basic Definition of Quality and its Benefits158</p> <p>5.3 Statistical Process Control159</p> <p>5.4 Control Charts for Variables 170</p> <p>5.5 Shewhart and R Control Charts when Process Mean and Standard Deviation Known 194</p> <p>5.6 Process Capability211</p> <p>Review Practice Problems 213</p> <p>Chapter 6 - Phase I Control Charts for Attributes 223</p> <p>6.1 Introduction 223</p> <p>6.2 Control Charts for Attributes 223</p> <p>6.3 The p chart: Control Chart for Fraction Nonconforming with Constant Samples Sizes225</p> <p>6.4 The c-Control chart - Control chart for nonconformities per sample 237</p> <p>6.5 The U-Chart 242</p> <p>Review Practice Problems 249</p> <p>Chapter 7 - Phase II Control Charts for Detecting Small Shifts256</p> <p>7.1 Introduction 256</p> <p>7.2 Basic Concepts of CUSUM Control Chart257</p> <p>7.3 Designing a CUSUM Control Chart 261</p> <p>7.4 Moving Average Control Chart 279</p> <p>7.5 Exponentially Weighted Moving Average Control Chart 284</p> <p>Review Practice Problems292</p> <p>Chapter 8 - Process and Measurement System Capability Analysis 298</p> <p>8.1 Introduction 298</p> <p>8.2 Development of Process Capability Indices300</p> <p>8.3 Various Process Capability Indices 302</p> <p>8.4 The Pre-control 326</p> <p>8.5 Measurement System Capability Analysis 334</p> <p>Review Practice Problems 354</p> <p>Chapter 9 - ACCEPTANCE SAMPLING PLANS 363</p> <p>9.1 Introduction363</p> <p>9.2 The Intent of Acceptance of Sampling Plan 363</p> <p>9.3 Sampling Inspection Versus 100 Percent Inspection 364</p> <p>9.4 Classification of Sampling Plans 364</p> <p>9.5 Acceptance Sampling by Attributes 371</p> <p>9.6 Single Sampling Plans for Attributes 375</p> <p>9.7 Other Types of Sampling Plans for Attributes 376</p> <p>9.8 Sampling Standards and Plans 386</p> <p>9.9 Dodge-Romig Tables 392</p> <p>9.10 Acceptance Sampling Plans By variables 392</p> <p>9.11 Continuous Sampling Plans 399</p> <p>Review Practice Problems 401</p> <p>Chapter 10 – CPMPUTER RESOURCES TO SUPPORT SQC 427</p> <p>10.1 Introduction427</p> <p>10.2 Using MINITAB</p> <p>10.3 Using R</p> <p>10.4 Using JMP</p> <p>10.5 Using PYTHON</p>
<p><b>Bhisham C. Gupta, PhD,</b> is Professor Emeritus of Statistics at the University of Southern Maine, where he has taught for 31 years. Prior to coming to USM as a full professor in 1985, Dr. Gupta served for 21 years at various institutions in Canada, Brazil, and India. He is the co-author of <i>Statistics and Probability with Applications for Engineers and Scientists, First Edition</i> and <i>Second Edition</i>, as well as the accompanying solutions manuals, all published by Wiley. He is also co-author of three books published by American Society for Quality (ASQ).</p>
<p><b>Provides a basic understanding of statistical quality control (SQC) and demonstrates how to apply the techniques of SQC to improve the quality of products in various sectors</b></p><p>This book introduces Statistical Quality Control and the elements of Six Sigma Methodology, illustrating the widespread applications that both have for a multitude of areas, including manufacturing, finance, transportation, and more. It places emphasis on both the theory and application of various SQC techniques and offers a large number of examples using data encountered in real life situations to support each theoretical concept.</p><p><i>Statistical Quality Control: Using MINITAB, R, JMP and Python</i> begins with a brief discussion of the different types of data encountered in various fields of statistical applications and introduces graphical and numerical tools needed to conduct preliminary analysis of the data. It then discusses the basic concept of statistical quality control (SQC) and Six Sigma Methodology and examines the different types of sampling methods encountered when sampling schemes are used to study certain populations. The book also covers Phase 1 Control Charts for variables and attributes; Phase II Control Charts to detect small shifts; the various types of Process Capability Indices (CPI); certain aspects of Measurement System Analysis (MSA); various aspects of PRE-control; and more. This helpful guide also</p><ul><li>Focuses on the learning and understanding of statistical quality control for second and third year undergraduates and practitioners in the field</li><li>Discusses aspects of Six Sigma Methodology</li><li>Teaches readers to use MINITAB, R, JMP and Python to create and analyze charts</li><li>Requires no previous knowledge of statistical theory</li><li>Is supplemented by an instructor-only book companion site featuring data sets and a solutions manual to all problems, as well as a student book companion site that includes data sets and a solutions manual to all odd-numbered problems</li></ul><p><i>Statistical Quality Control: Using MINITAB, R, JMP and Python</i> is an excellent book for students studying engineering, statistics, management studies, and other related fields and who are interested in learning various techniques of statistical quality control. It also serves as a desk reference for practitioners who work to improve quality in various sectors, such as manufacturing, service, transportation, medical, oil, and financial institutions. It‘s also useful for those who use Six Sigma techniques to improve the quality of products in such areas.</p>

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