Rath & Strong's Six Sigma Advanced Tools Pocket Guide / Edition 1

Rath & Strong's Six Sigma Advanced Tools Pocket Guide / Edition 1

by Rath & Strong
Pub. Date:
McGraw-Hill Professional Publishing

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Rath & Strong's Six Sigma Advanced Tools Pocket Guide / Edition 1

A simple, take-along guide to achieving lasting business results

A companion to the bestselling Rath & Strong Pocket Guide to Six Sigma, Rath & Strong's Pocket Guide to Advanced Six Sigma Tools is designed to help Six Sigma black belts, green belts, and team leaders from every field to implement the most powerful tools in Six Sigma without getting bogged down in statistical theory.

This pocket-sized field guide provides practical advice on the use of advanced tools, such as: sampling, analysis of variance, multiple regression, and design of experiments. Each tool is explained in easy-to-understand language, permitting the reader to solve real-world problems in any area of business.

  • Covers step-by-step implementation of the most important Six Sigma tools.
  • Features a heavy emphasis on applying the best tools to solve practical business problems.

Explains how to use Microsoft Excel and Minitab statistical software to simplify the process.

Product Details

ISBN-13: 9780071434119
Publisher: McGraw-Hill Professional Publishing
Publication date: 06/29/2004
Edition description: Spiral
Pages: 240
Sales rank: 1,278,148
Product dimensions: 4.40(w) x 5.60(h) x 0.50(d)

Table of Contents

Chapter 1.Tool Selection Guide1
Minitab Commands3
Excel Statistical Functions7
Chapter 2.Probability Distributions9
What Is a Probability Distribution?9
Application of Probability Distributions in Six Sigma9
Discrete Probability Distributions9
Binomial Distributions10
Poisson Distribution12
Continuous Probability Distributions14
Normal Distribution14
Exponential Distribution18
Weibull Distribution20
Probability Plots22
Transforming Non-Normal Data to Normal26
Box-Cox Transformation29
How to Use Probability Distributions34
Chapter 3.Sampling37
Why Use Sampling?37
Application of Sampling in Six Sigma37
Sample Types37
Sampling Terminology38
Types of Population Data39
What Affects Sample Size?39
Sampling Techniques41
Simple Random Sample41
Stratified Random Sample41
Systematic Sampling41
Formulas Used for Determining Sample Size42
Allocating Samples for Stratified Random Sampling44
Risk-Based Allocation Approach44
Neyman Allocation Method45
Determining Sample Sizes for Hypothesis Tests46
How to Estimate Sample Size47
Chapter 4.Confidence Intervals49
What Is a Confidence Interval?49
Application of Confidence Intervals in Six Sigma49
Confidence Interval for the Mean49
Mean Estimation--Standard Deviation ([sigma]) Is Known50
Mean Estimation--Standard Deviation ([sigma]) Unknown51
Confidence Interval for Proportions52
Confidence Interval for the Variance of a Normal Distribution53
How to Determine Confidence Intervals54
Chapter 5.Hypothesis Testing55
What Is Hypothesis Testing?55
Application of Hypothesis Testing in Six Sigma55
Types of Hypothesis Tests55
Two-Tailed Hypothesis Tests56
One-Tailed Hypothesis Tests56
Decision Errors and Hypothesis Testing57
Type I Error57
Type II Error57
Significance Level and the Power of the Hypothesis Test58
Decision Rules59
Converting Alpha Risk to Z-Values61
Hypothesis Tests of the Mean63
Two-Sample Z-Test65
Paired Z-Test66
Two-Sample t-Test72
Paired t-Test75
Hypothesis Tests of Proportions78
Single Proportion Test78
Two-Sample Proportion Test80
Hypothesis Tests of Variance82
X[superscript 2] Test82
How to Perform Hypothesis Testing88
Chapter 6.Control Charts89
What Are Control Charts?89
Application of Control Charts to Six Sigma90
How to Use Control Charts90
Control Charts for Discrete (Attribute) Data95
p Chart95
np Chart98
c Chart99
u Chart102
Control Charts for Continuous Data105
Individuals Chart105
Moving Range (MR) Chart106
Range (R) Chart107
x Chart109
EWMA Chart111
How to Create and Use Control Charts114
Chapter 7.Correlation Analysis117
What Is Correlation Analysis117
Application of Correlation Analysis in Six Sigma117
Scatter Plots117
Correlation Matrix117
Significance of the Correlation Analysis121
How to Perform Correlation Analysis123
Chapter 8.Regression Analysis125
What Is Regression Analysis?125
Application of Regression Analysis in Six Sigma125
Simple Linear Regression126
How to Interpret Regression Analysis Results131
Confidence and Prediction Intervals133
How Do We Know That Our Regression Model Is Good Enough to Use?134
P-Value (X-Variable Coefficient)134
r[superscript 2]--Coefficient of Determination134
Using Residual and Normal Probability Plots to Validate Regression Models135
Interpreting Residual Plots138
Multiple Regression138
Variance Inflation Factor (VIF)140
Systematic Procedure for VIF [greater than sign] 10140
Regression ANOVA141
Model Validation142
Interpreting the Regression Output144
Interpreting the Regression Output of the Reduced Model (Weight, Volume, and Distance)146
Interpreting the Regression Output of the Reduced Model (Volume and Distance)148
Multiple Regression Analysis Using Qualitative Input Variables148
Interpreting Regression Output When Using Qualitative Variables151
Curvilinear Regression153
How to Perform Regression Analysis156
Chapter 9.Design of Experiments159
What Is Design of Experiments?159
Application of Design of Experiments in Six Sigma160
Factorial Experiments160
Design Terminology160
Design Fundamentals161
Full Factorial Design162
How Do I Know Which Process Factors Are Significant?165
Pareto Chart of Standardized Effects166
How Can We Determine the Value of the Significant Effects?168
Predicting Process Output Using the Results of Our Factorial Experiment169
Randomization and Blocking174
Randomized Block Design (Blocking)175
Fractional Factorial Designs178
Confounding and Design Resolution179
Design Resolution180
Design Notation180
How to Perform a DOE190
Chapter 10.Analysis of Variance (ANOVA)192
What Is Analysiss of Variance?192
Application of ANOVA in Six Sigma192
One-Way ANOVA192
How to Read a One-Way ANOVA Table194
Two-Way ANOVA195
Two-Way ANOVA with Replication--Interaction Effects196
How to Read a Two-Way ANOVA Table199
Nested ANOVA200
Variance Components202
Analysis of Means203
Main Effects Plots205
Interaction Plots207
Interval Plots209
Balanced ANOVA and General Linear Models (GLM)210
How to Perform Analysis of Variance (ANOVA)217
Appendix A.References219
Appendix B.Glossary of Terms220

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