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Solution Manual for Introduction to Statistical Quality Control 7th Edition by Douglas C. Montgomery

By: Douglas C. Montgomery
ISBN-10: 1118146816
/ ISBN-13: 9781118146811

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Edition: 7th Edition
Authors: Douglas C. Montgomery
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1 QUALITY IMPROVEMENT IN THE MODERN BUSINESS ENVIRONMENT

    • Chapter Overview and Learning Objectives
    • 1.1 The Meaning of Quality and Quality Improvement
    • 1.1.1 Dimensions of Quality
    • 1.1.2 Quality Engineering Terminology
    • 1.2 A Brief History of Quality Control and Improvement
    • 1.3 Statistical Methods for Quality Control and Improvement
    • 1.4 Management Aspects of Quality Improvement
    • 1.4.1 Quality Philosophy and Management Strategies
    • 1.4.2 The Link Between Quality and Productivity
    • 1.4.3 Supply Chain Quality Management
    • 1.4.4 Quality Costs
    • 1.4.5 Legal Aspects of Quality
    • 1.4.6 Implementing Quality Improvement

2 THE DMAIC PROCESS

    • Chapter Overview and Learning Objectives
    • 2.1 Overview of DMAIC
    • 2.2 The Define Step
    • 2.3 The Measure Step
    • 2.4 The Analyze Step
    • 2.5 The Improve Step
    • 2.6 The Control Step
    • 2.7 Examples of DMAIC
    • 2.7.1 Litigation Documents
    • 2.7.2 Improving On-Time Delivery
    • 2.7.3 Improving Service Quality in a Bank

PART 2 STATISTICAL METHODS USEFUL IN QUALITY CONTROL AND IMPROVEMENT

3 MODELING PROCESS QUALITY

    • Chapter Overview and Learning Objectives
    • 3.1 Describing Variation
    • 3.1.1 The Stem-and-Leaf Plot
    • 3.1.2 The Histogram
    • 3.1.3 Numerical Summary of Data
    • 3.1.4 The Box Plot
    • 3.1.5 Probability Distributions
    • 3.2 Important Discrete Distributions
    • 3.2.1 The Hypergeometric Distribution
    • 3.2.2 The Binomial Distribution
    • 3.2.3 The Poisson Distribution
    • 3.2.4 The Negative Binomial and Geometric Distributions
    • 3.3 Important Continuous Distributions
    • 3.3.1 The Normal Distribution
    • 3.3.2 The Lognormal Distribution
    • 3.3.3 The Exponential Distribution
    • 3.3.4 The Gamma Distribution
    • 3.3.5 The Weibull Distribution
    • 3.4 Probability Plots
    • 3.4.1 Normal Probability Plots
    • 3.4.2 Other Probability Plots
    • 3.5 Some Useful Approximations
    • 3.5.1 The Binomial Approximation to the Hypergeometric
    • 3.5.2 The Poisson Approximation to the Binomial
    • 3.5.3 The Normal Approximation to the Binomial
    • 3.5.4 Comments on Approximations

4 INFERENCES ABOUT PROCESS QUALITY

    • Chapter Overview and Learning Objectives
    • 4.1 Statistics and Sampling Distributions
    • 4.1.1 Sampling from a Normal Distribution
    • 4.1.2 Sampling from a Bernoulli Distribution
    • 4.1.3 Sampling from a Poisson Distribution
    • 4.2 Point Estimation of Process Parameters
    • 4.3 Statistical Inference for a Single Sample
    • 4.3.1 Inference on the Mean of a Population, Variance Known
    • 4.3.2 The Use of P-Values for Hypothesis Testing
    • 4.3.3 Inference on the Mean of a Normal Distribution, Variance Unknown
    • 4.3.4 Inference on the Variance of a Normal Distribution
    • 4.3.5 Inference on a Population Proportion
    • 4.3.6 The Probability of Type II Error and Sample Size Decisions
    • 4.4 Statistical Inference for Two Samples
    • 4.4.1 Inference for a Difference in Means, Variances Known
    • 4.4.2 Inference for a Difference in Means of Two Normal Distributions
    • 4.4.3 Inference on the Variances of Two Normal Distributions
    • 4.4.4 Inference on Two Population Proportions
    • 4.5 What If There Are More Than Two Populations? The Analysis of Variance
    • 4.5.1 An Example
    • 4.5.2 The Analysis of Variance
    • 4.5.3 Checking Assumptions: Residual Analysis
    • 4.6 Linear Regression Models
    • 4.6.1 Estimation of the Parameters in Linear Regression Models
    • 4.6.2 Hypothesis Testing in Multiple Regression
    • 4.6.3 Confidance Intervals in Multiple Regression
    • 4.6.4 Prediction of New Observations
    • 4.6.5 Regression Model Diagnostics

PART 3 BASIC METHODS OF STATISTICAL PROCESS CONTROL AND CAPABILITY ANALYSIS

5 METHODS AND PHILOSOPHY OF STATISTICAL PROCESS CONTROL

    • Chapter Overview and Learning Objectives
    • 5.1 Introduction
    • 5.2 Chance and Assignable Causes of Quality Variation
    • 5.3 Statistical Basis of the Control Chart
    • 5.3.1 Basic Principles
    • 5.3.2 Choice of Control Limits
    • 5.3.3 Sample Size and Sampling Frequency
    • 5.3.4 Rational Subgroups
    • 5.3.5 Analysis of Patterns on Control Charts
    • 5.3.6 Discussion of Sensitizing Rules for Control Charts
    • 5.3.7 Phase I and Phase II of Control Chart Application
    • 5.4 The Rest of the Magnificent Seven
    • 5.5 Implementing SPC in a Quality Improvement Program
    • 5.6 An Application of SPC
    • 5.7 Applications of Statistical Process Control and Quality Improvement Tools in Transactional and S

6 CONTROL CHARTS FOR VARIABLES

    • Chapter Overview and Learning Objectives
    • 6.1 Introduction
    • 6.2 Control Charts for –x and R
    • 6.2.1 Statistical Basis of the Charts
    • 6.2.2 Development and Use of –x and R Charts
    • 6.2.3 Charts Based on Standard Values
    • 6.2.4 Interpretation of –x and R Charts
    • 6.2.5 The Effect of Nonnormality on –x and R Charts
    • 6.2.6 The Operating-Characteristic Function
    • 6.2.7 The Average Run Length for the –x Chart
    • 6.3 Control Charts for –x and s
    • 6.3.1 Construction and Operation of -x and s Charts
    • 6.3.2 The –x and s Control Charts with Variable Sample Size
    • 6.3.3 The s2 Control Chart
    • 6.4 The Shewhart Control Chart for Individual Measurements
    • 6.5 Summary of Procedures for –x , R, and s Charts
    • 6.6 Applications of Variables Control Charts

7 CONTROL CHARTS FOR ATTRIBUTES

    • Chapter Overview and Learning Objectives
    • 7.1 Introduction
    • 7.2 The Control Chart for Fraction Nonconforming
    • 7.2.1 Development and Operation of the Control Chart
    • 7.2.2 Variable Sample Size
    • 7.2.3 Applications in Transactional and Service Businesses
    • 7.2.4 The Operating-Characteristic Function and Average Run Length Calculations
    • 7.3 Control Charts for Nonconformities (Defects)
    • 7.3.1 Procedures with Constant Sample Size
    • 7.3.2 Procedures with Variable Sample Size
    • 7.3.3 Demerit Systems
    • 7.3.4 The Operating-Characteristic Function
    • 7.3.5 Dealing with Low Defect Levels
    • 7.3.6 Nonmanufacturing Applications
    • 7.4 Choice Between Attributes and Variables Control Charts
    • 7.5 Guidelines for Implementing Control Charts

8 PROCESS AND MEASUREMENT SYSTEM CAPABILITY ANALYSIS

    • Chapter Overview and Learning Objectives
    • 8.1 Introduction
    • 8.2 Process Capability Analysis Using a Histogram or a Probability Plot
    • 8.2.1 Using the Histogram
    • 8.2.2 Probability Plotting
    • 8.3 Process Capability Ratios
    • 8.3.1 Use and Interpretation of Cp
    • 8.3.2 Process Capability Ratio for an Off-Center Process
    • 8.3.3 Normality and the Process Capability Ratio
    • 8.3.4 More about Process Centering
    • 8.3.5 Confidence Intervals and Tests on Process Capability Ratios
    • 8.4 Process Capability Analysis Using a Control Chart
    • 8.5 Process Capability Analysis Using Designed Experiments
    • 8.6 Process Capability Analysis with Attribute Data
    • 8.7 Gauge and Measurement System Capability Studies
    • 8.7.1 Basic Concepts of Gauge Capability
    • 8.7.2 The Analysis of Variance Method
    • 8.7.3 Confidence Intervals in Gauge R & R Studies
    • 8.7.4 False Defectives and Passed Defectives
    • 8.7.5 Attribute Gauge Capability
    • 8.7.6 Comparing Customer and Supplier Measurement Systems
    • 8.8 Setting Specification Limits on Discrete Components
    • 8.8.1 Linear Combinations
    • 8.8.2 Nonlinear Combinations
    • 8.9 Estimating the Natural Tolerance Limits of a Process
    • 8.9.1 Tolerance Limits Based on the Normal Distribution
    • 8.9.2 Nonparametric Tolerance Limits

PART 4 OTHER STATISTICAL PROCESS-MONITORING AND CONTROL TECHNIQUES

9 CUMULATIVE SUM AND EXPONENTIALLY WEIGHTED MOVING AVERAGE CONTROL CHARTS

    • Chapter Overview and Learning Objectives
    • 9.1 The Cumulative Sum Control Chart
    • 9.1.1 Basic Principles: The CUSUM Control Chart for Monitoring the Process Mean
    • 9.1.2 The Tabular or Algorithmic CUSUM for Monitoring the Process Mean
    • 9.1.3 Recommendations for CUSUM Design
    • 9.1.4 The Standardized CUSUM
    • 9.1.5 Improving CUSUM Responsiveness for Large Shifts
    • 9.1.6 The Fast Initial Response or Headstart Feature
    • 9.1.7 One-Sided CUSUMs
    • 9.1.8 A CUSUM for Monitoring Process Variability
    • 9.1.9 Rational Subgroups
    • 9.1.10 CUSUMs for Other Sample Statistics
    • 9.1.11 The V-Mask Procedure
    • 9.1.12 The Self-Starting CUSUM
    • 9.2 The Exponentially Weighted Moving Average Control Chart
    • 9.2.1 The Exponentially Weighted Moving Average Control Chart for Monitoring the Process Mean
    • 9.2.2 Design of an EWMA Control Chart
    • 9.2.3 Robustness of the EWMA to Non-normality
    • 9.2.4 Rational Subgroups
    • 9.2.5 Extensions of the EWMA
    • 9.3 The Moving Average Control Chart

10 OTHER UNIVARIATE STATISTICAL PROCESS-MONITORING AND CONTROL TECHNIQUES

    • Chapter Overview and Learning Objectives
    • 10.1 Statistical Process Control for Short Production Runs
    • 10.1.1 –x and R Charts for Short Production Runs
    • 10.1.2 Attributes Control Charts for Short Production Runs
    • 10.1.3 Other Methods
    • 10.2 Modified and Acceptance Control Charts
    • 10.2.1 Modified Control Limits for the –x Chart
    • 10.2.2 Acceptance Control Charts
    • 10.3 Control Charts for Multiple-Stream Processes
    • 10.3.1 Multiple-Stream Processes
    • 10.3.2 Group Control Charts
    • 10.3.3 Other Approaches
    • 10.4 SPC With Autocorrelated Process Data
    • 10.4.1 Sources and Effects of Autocorrelation in Process Data
    • 10.4.2 Model-Based Approaches
    • 10.4.3 A Model-Free Approach
    • 10.5 Adaptive Sampling Procedures
    • 10.6 Economic Design of Control Charts
    • 10.6.1 Designing a Control Chart
    • 10.6.2 Process Characteristics
    • 10.6.3 Cost Parameters
    • 10.6.4 Early Work and Semieconomic Designs
    • 10.6.5 An Economic Model of the –x Control Chart
    • 10.6.6 Other Work
    • 10.7 Cuscore Charts
    • 10.8 The Changepoint Model for Process Monitoring
    • 10.9 Profile Monitoring
    • 10.10 Control Charts in Health Care Monitoring and Public Health Surveillance
    • 10.11 Overview of Other Procedures
    • 10.11.1 Tool Wear
    • 10.11.2 Control Charts Based on Other Sample Statistics
    • 10.11.3 Fill Control Problems
    • 10.11.4 Precontrol
    • 10.11.5 Tolerance Interval Control Charts
    • 10.11.6 Monitoring Processes with Censored Data
    • 10.11.7 Monitoring Bernoulli Processes
    • 10.11.8 Nonparametric Control Charts

11 MULTIVARIATE PROCESS MONITORING AND CONTROL

    • Chapter Overview and Learning Objectives
    • 11.1 The Multivariate Quality-Control Problem
    • 11.2 Description of Multivariate Data
    • 11.2.1 The Multivariate Normal Distribution
    • 11.2.2 The Sample Mean Vector and Covariance Matrix
    • 11.3 The Hotelling T2 Control Chart
    • 11.3.1 Subgrouped Data
    • 11.3.2 Individual Observations
    • 11.4 The Multivariate EWMA Control Chart
    • 11.5 Regression Adjustment
    • 11.6 Control Charts for Monitoring Variability
    • 11.7 Latent Structure Methods
    • 11.7.1 Principal Components
    • 11.7.2 Partial Least Squares

12 ENGINEERING PROCESS CONTROL AND SPC

    • Chapter Overview and Learning Objectives
    • 12.1 Process Monitoring and Process Regulation
    • 12.2 Process Control by Feedback Adjustment
    • 12.2.1 A Simple Adjustment Scheme: Integral Control
    • 12.2.2 The Adjustment Chart
    • 12.2.3 Variations of the Adjustment Chart
    • 12.2.4 Other Types of Feedback Controllers
    • 12.3 Combining SPC and EPC

PART 5 PROCESS DESIGN AND IMPROVEMENT WITH DESIGNED EXPERIMENTS

13 FACTORIAL AND FRACTIONAL FACTORIAL EXPERIMENTS FOR PROCESS DESIGN AND IMPROVEMENT

    • Chapter Overview and Learning Objectives
    • 13.1 What is Experimental Design?
    • 13.2 Examples of Designed Experiments In Process and Product Improvement
    • 13.3 Guidelines for Designing Experiments
    • 13.4 Factorial Experiments
    • 13.4.1 An Example
    • 13.4.2 Statistical Analysis
    • 13.4.3 Residual Analysis
    • 13.5 The 2k Factorial Design
    • 13.5.1 The 22 Design
    • 13.5.2 The 2k Design for k ≥ 3 Factors
    • 13.5.3 A Single Replicate of the 2k Design
    • 13.5.4 Addition of Center Points to the 2k Design
    • 13.5.5 Blocking and Confounding in the 2k Design
    • 13.6 Fractional Replication of the 2k Design
    • 13.6.1 The One-Half Fraction of the 2k Design
    • 13.6.2 Smaller Fractions: The 2k–p Fractional Factorial Design

14 PROCESS OPTIMIZATION WITH DESIGNED EXPERIMENTS

    • Chapter Overview and Learning Objectives
    • 14.1 Response Surface Methods and Designs
    • 14.1.1 The Method of Steepest Ascent
    • 14.1.2 Analysis of a Second-Order Response Surface
    • 14.2 Process Robustness Studies
    • 14.2.1 Background
    • 14.2.2 The Response Surface Approach to Process Robustness Studies
    • 14.3 Evolutionary Operation

PART 6 ACCEPTANCE SAMPLING

15 LOT-BY-LOT ACCEPTANCE SAMPLING FOR ATTRIBUTES

    • Chapter Overview and Learning Objectives
    • 15.1 The Acceptance-Sampling Problem
    • 15.1.1 Advantages and Disadvantages of Sampling
    • 15.1.2 Types of Sampling Plans
    • 15.1.3 Lot Formation
    • 15.1.4 Random Sampling
    • 15.1.5 Guidelines for Using Acceptance Sampling
    • 15.2 Single-Sampling Plans for Attributes
    • 15.2.1 Definition of a Single-Sampling Plan
    • 15.2.2 The OC Curve
    • 15.2.3 Designing a Single-Sampling Plan with a Specified OC Curve
    • 15.2.4 Rectifying Inspection
    • 15.3 Double, Multiple, and Sequential Sampling
    • 15.3.1 Double-Sampling Plans
    • 15.3.2 Multiple-Sampling Plans
    • 15.3.3 Sequential-Sampling Plans
    • 15.4 Military Standard 105E (ANSI/ASQC Z1.4, ISO 2859)
    • 15.4.1 Description of the Standard
    • 15.4.2 Procedure
    • 15.4.3 Discussion
    • 15.5 The Dodge–Romig Sampling Plans
    • 15.5.1 AOQL Plans
    • 15.5.2 LTPD Plans
    • 15.5.3 Estimation of Process Average

16 OTHER ACCEPTANCE-SAMPLING TECHNIQUES

    • Chapter Overview and Learning Objectives
    • 16.1 Acceptance Sampling by Variables
    • 16.1.1 Advantages and Disadvantages of Variables Sampling
    • 16.1.2 Types of Sampling Plans Available
    • 16.1.3 Caution in the Use of Variables Sampling
    • 16.2 Designing a Variables Sampling Plan with a Specified OC Curve
    • 16.3 MIL STD 414 (ANSI/ASQC Z1.9)
    • 16.3.1 General Description of the Standard
    • 16.3.2 Use of the Tables
    • 16.3.3 Discussion of MIL STD 414 and ANSI/ASQC Z1.9
    • 16.4 Other Variables Sampling Procedures
    • 16.4.1 Sampling by Variables to Give Assurance Regarding the Lot or Process Mean
    • 16.4.2 Sequential Sampling by Variables
    • 16.5 Chain Sampling
    • 16.6 Continuous Sampling
    • 16.6.1 CSP-1
    • 16.6.2 Other Continuous-Sampling Plans
    • 16.7 Skip-Lot Sampling Plans

APPENDIX

    • I. Summary of Common Probability Distributions Often Used in Statistical Quality Control
    • II. Cumulative Standard Normal Distribution
    • III. Percentage Points of the χ2 Distribution
    • IV. Percentage Points of the t Distribution
    • V. Percentage Points of the F Distribution
    • VI. Factors for Constructing Variables Control Charts
    • VII. Factors for Two-Sided Normal Tolerance Limits
    • VIII. Factors for One-Sided Normal Tolerance Limits

BIBLIOGRAPHY

ANSWERS TO SELECTED EXERCISES

INDEX

EULA


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