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Use R Six Sigma with R (Cano)

 Author(s) Emilio L. Cano, Javier M. Moguerza, Andres Redchuk Title Six Sigma with R   Statistical Engineering for Process Improvement (part of the "Use R!" series) Edition 1st Year 2012 Publisher Springer ISBN 978-1-4614-3651-5(or eBook 978-1-4614-3652-2) Website www.SixSigmaWithR.com

R code and data sets can be found at the website listed.

Part I Basics

1 Six Sigma in a Nutshell

1.1 Introduction

1.2 Brief History

1.3 What Is Six Sigma?

1.4 DMAIC Cycle

1.4.1 Define Phase

1.4.2 Measure Phase

1.4.3 Analyze Phase

1.4.4 Improve Phase

1.4.5 Control Phase

1.5 Six Sigma Operational Structure

Case Study

2 R from the Beginning

2.1 Introduction

2.2 First Steps

2.2.1 Get and Install

2.2.2 Run and Interact

2.3 Coping with Data

2.3.1 Data Types

2.3.2 Creating Data Objects

2.3.3 Accessing Data

2.3.4 Importing and Exporting Data

2.4 Objects and Functions

2.4.1 Objects

2.4.2 Functions

2.5 Operators and Functions Commonly Used

2.5.1 Operators

2.5.2 Mathematical Functions

2.5.3 Functions for Vectors

2.5.4 Loop and Summary Functions

2.6 Graphics in R

2.6.1 Plotting Functions

2.6.2 Bivariate Plots

2.6.3 Pie Plots

2.7 Statistics

2.7.1 Samples and Combinatorial Computations

2.7.2 Random Variables

Case Study

Practice

Part II R Tools for the Define Phase

3 Process Mapping with R

3.1 Introduction

3.2 Process Mapping as a Problem-solving Method

3.3 Strategies for Process Mapping

3.3.1 Two-stage Process Mapping

3.3.2 Drilling Down into the Process Steps

3.4 Step-by-Step Process Mapping

3.4.1 Identifying Inputs and Outputs

3.4.2 Listing the Project Steps

3.4.3 Identifying the Outputs of Each Step

3.4.4 Identifying the Parameters of Each Step

3.4.5 Classifying the Parameters

3.5 Drawing a Process Map with the Six Sigma Package

3.6 Why Should I Use R for Drawing Diagrams?

Case Study

Practice

4 Loss Function Analysis with R

4.1 Introduction

4.2 Cost of Poor Quality

4.3 Modeling the Loss Function

4.3.1 Some Notation

4.3.2 Taguchi Loss Function

4.4 Average Loss Function

4.5 Use of Loss Function Within DMAIC Cycle

4.6 Loss Function Analysis with Six Sigma Package

4.7 Other Models

Case Study

Practice

Part III R Tools for the Measure Phase

5 Measurement System Analysis with R

5.1 Introduction

5.1.1 Definitions

5.2 Data Analysis

5.2.1 Data Collection

5.2.2 First Approach to Analysis of Variance (ANOVA)

5.2.3 Assessing the Measurement System

5.3 Using the SixSigma Package

5.3.1 Interpreting the Charts

Case Study

Practice

6 Pareto Analysis with R

6.1 Introduction

6.2 Pareto Principle

6.2.1 Pareto Principle as a Problem-solving Technique

6.3 Pareto Analysis in Six Sigma

6.3.1 Identifying Causes

6.3.2 Measuring the Effect

6.3.3 Building a Pareto Chart

6.4 Pareto Charts in R

6.5 Other Uses of the Pareto Chart

Case Study

Practice

7 Process Capability Analysis with R

7.1 Introduction

7.2 Specifications

7.3 Process Performance

7.4 Process vs. Specifications

7.5 Capability Indices

7.6 Capability Study with SixSigma Package

Case Study

Practice

Part IV R Tools for the Analyze Phase

8 Charts with R

8.1 Introduction

8.1.1 Use of Charts

8.1.2 Background Concepts

8.2 Bar Chart

8.3 Histogram

8.4 Scatterplot

8.5 Run Chart

8.6 Tier Chart

8.7 Box–Whisker Chart

8.8 Other Charts

8.8.1 Group Chart

8.8.2 Location Charts

8.8.3 Multivariate Chart

Case Study

Practice

9 Statistics and Probability with R

9.1 Introduction

9.1.1 Variables and Observations

9.1.2 Summary Tables

9.1.3 Population and Sample

9.1.4 Special Data Values

9.2 Descriptive

9.2.1 Measures of Central Tendency

9.2.2 Measures of Variability

9.3 Probability

9.3.1 Random Variables

9.3.2 Binomial Distribution

9.3.3 Normal Distribution

9.3.4 Other Useful Distributions

Case Study

Practice

10 Statistical Inference with R

10.1 Introduction

10.2 Confidence Intervals

10.2.1 Sampling Distribution and Point Estimation

10.2.2 Proportion Confidence Interval

10.2.3 Mean Confidence Interval

10.2.4 Variance Confidence Interval

10.3 Hypothesis Testing

10.4 Regression

10.4.1 Model Identification

10.4.2 Model Fitting

10.4.3 Model Validation

10.4.4 Other Models

10.5 Analysis of Variance

10.5.1 Model Identification

10.5.2 Model Fitting and Validation

10.5.3 Additional Models and Related Tools

Case Study

Practice

Part V R Tools for the Improve Phase

11 Design of Experiments with R

11.1 Introduction

11.2 Importance of Experimenting

11.2.1 Inconsistent Data

11.2.2 Variable Value Range

11.2.3 Correlated Variables

11.3 Experimentation Strategies

11.3.1 Planning Strategies

11.3.2 Factor Levels and Replications

11.3.3 Progressive Experimentation

11.3.4 Model Assumptions

11.4 2 k-Factorial Designs

11.5 Design of Experiments for Process Improvement

Case Study

Practice

Part VI R Tools for the Control Phase

12 Process Control with R

12.1 Introduction

12.2 Mistake-proofing Strategies (Poka-Yoke)

12.3 What to Control

12.4 Control Chart Basics

12.4.1 The Chart

12.4.2 Chart Interpretation

12.4.3 Sampling Strategy

12.5 Plotting Process Control Charts

12.5.1 Notation for Computing Control Lines

12.5.2 Variable Control Charts

12.5.3 Attribute Control Charts

Case Study

Practice

Part VII Further and Beyond

13 Other Tools and Methodologies

13.1 Introduction

13.2 Failure Mode, Effects, and Criticality Analysis

13.3 Design for Six Sigma

13.4 Lean

13.5 Gantt Chart

13.6.1 Programming

13.6.2 R User Interfaces

13.6.3 Reporting

Case Study

Appendix A - R Basic Reference Guide

References

Solutions

R Packages and Functions Used in the Book

Subject Index