Six Sigma Statistics With Excel and Minitab

by Bass, Issa

• ISBN13: 9780071489690
• ISBN10: 007148969X
• Edition: 1st
• Format: Hardcover
• Copyright: 2007-07-18
• Publisher: McGraw Hill

Summary

Master the Statistical Techniques for Six Sigma Operations, While Boosting Your Excel and Minitab Skills! Now with the help of this "one-stop" resource, operations and production managers can learn all the powerful statistical techniques for Six Sigma operations, while becoming proficient at Excel and Minitab at the same time.Six Sigma Statistics with Excel and Minitab offers a complete guide to Six Sigma statistical methods, plus expert coverage of Excel and Minitab, two of today's most popular programs for statistical analysis and data visualization.Written by a seasoned Six Sigma Master Black Belt, the book explains how to create and interpret dot plots, histograms, and box plots using Minitab...decide on sampling strategies, sample size, and confidence intervals...apply hypothesis tests to compare variance, means, and proportions...conduct a regression and residual analysis...design and analyze an experiment...and much more.Filled with clear, concise accounts of the theory for each statistical method presented, Six Sigma Statistics with Excel and Minitab features: Easy-to-follow explanations of powerful Six Sigma tools A wealth of exercises and case studies 200 graphical illustrations for Excel and Minitab Essential for achieving Six Sigma goals in any organization, Six Sigma Statistics with Excel and Minitab is a unique, skills-building toolkit for mastering a wide range of vital statistical techniques, and for capitalizing on the potential of Excel and Minitab.Six Sigma Statistical with Excel and Minitab offers operations and production managers a complete guide to Six Sigma statistical techniques, together with expert coverage of Excel and Minitab, two of today's most popular programs for statistical analysis and data visualization.Written by Issa Bass, a Six Sigma Master Black Belt with years of hands-on experience in industry, this on-target resource takes readers through the application of each Six Sigma statistical tool, while presenting a straightforward tutorial for effectively utilizing Excel and Minitab. With the help of this essential reference, managers can: Acquire the basic tools for data collection, organization, and description Learn the fundamental principles of probability Create and interpret dot plots, histograms, and box plots using Minitab Decide on sampling strategies, sample size, and confidence intervals Apply hypothesis tests to compare variance, means, and proportions Stay on top of production processes with statistical process control Use process capability analysis to ensure that processes meet customers' expectations Employ analysis of variance to make inferences about more than two population means Conduct a regression and residual analysis Design and analyze an experiment In addition, Six Sigma Statistics with Excel and Minitab enables you to develop a better understanding of the Taguchi Method...use measurement system analysis to find out if measurement processes are accurate...discover how to test ordinal or nominal data with nonparametric statistics...and apply the full range of basic quality tools.Filled with step-by-step exercises, graphical illustrations, and screen shots for performing Six Sigma techniques on Excel and Minitab, the book also provides clear, concise explanations of the theory for each of the statistical tools presented.Authoritative and comprehensive, Six Sigma Statistics with Excel and Minitab is a valuable skills-building resource for mastering all the statistical techniques for Six Sigma operations, while harnessing the power of Excel a

Author Biography

Issa Bass is a Six Sigma Master Black Belt and SixSigma project leader for Kenco Group, Inc. He is the founding editor ofSixSigmaFirst.com.

Table of Contents

Preface	p. ix
Acknowledgments	p. x
Introduction	p. 1
Six Sigma Methodology	p. 2
Define the organization	p. 2
Measure the organization	p. 6
Analyze the organization	p. 11
Improve the organization	p. 13
Statistics, Quality Control, and Six Sigma	p. 14
Poor quality defined as a deviation from engineered standards	p. 15
Sampling and quality control	p. 16
Statistical Definition of Six Sigma	p. 16
Variability: the source of defects	p. 17
Evaluation of the process performance	p. 18
Normal distribution and process capability	p. IS
An Overview of Minitab and Microsoft Excel	p. 23
Starting with Minitab	p. 23
Minitab's menus	p. 25
An Overview of Data Analysis with Excel	p. 33
Graphical display of data	p. 35
Data Analysis add-in	p. 37
Basic Tools for Data Collection, Organization and Description	p. 41
The Measures of Central Tendency Give a First Perception of Your Data	p. 42
Arithmetic mean	p. 42
Geometric mean	p. 47
Mode	p. 49
Median	p. 49
Measures of Dispersion	p. 49
Range	p. 50
Mean deviation	p. 50
Variance	p. 52
Standard deviation	p. 54
Chebycheff's theorem	p. 55
Coefficient of variation	p. 55
The Measures of Association Quantify the Level of Relatedness between Factors	p. 56
Covariance	p. 56
Correlation coefficient	p. 58
Coefficient of determination	p. 62
Graphical Representation of Data	p. 62
Histograms	p. 62
Stem-and-leaf graphs	p. 64
Box plots	p. 66
Descriptive Statistics-Minitab and Excel Summaries	p. 68
Introduction to Basic Probability	p. 73
Discrete Probability Distributions	p. 74
Binomial distribution	p. 74
Poisson distribution	p. 79
Poisson distribution, rolled throughput yield, and DPMO	p. 80
Geometric distribution	p. 84
Hypergeometric distribution	p. 85
Continuous Distributions	p. 88
Exponential distribution	p. 88
Normal distribution	p. 90
The log-normal distribution	p. 97
How to Determine, Analyze, and Interpret Your Samples	p. 99
How to Collect a Sample	p. 100
Stratified sampling	p. 100
Cluster sampling	p. 100
Systematic sampling	p. 100
Sampling Distribution of Means	p. 100
Sampling Error	p. 101
Central Limit Theorem	p. 102
Sampling from a Finite Population	p. 106
Sampling Distribution of p	p. 106
Estimating the Population Mean with Large Sample Sizes	p. 108
Estimating the Population Mean with Small Sample Sizes and [sigma] Unknown: t-Distribution	p. 113
Chi Square (x[superscript 2]) Distribution	p. 114
Estimating Sample Sizes	p. 117
Sample size when estimating the mean	p. 117
Sample size when estimating the population proportion	p. 118
Hypothesis Testing	p. 121
How to Conduct a Hypothesis Testing	p. 122
Null hypothesis	p. 122
Alternate hypothesis	p. 122
Test statistic	p. 123
Level of significance or level of risk	p. 123
Decision rule determination	p. 123
Decision making	p. 124
Testing for a Population Mean	p. 124
Large sample with known [sigma]	p. 124
What is the p-value and how is it interpreted?	p. 126
Small samples with unknown [sigma]	p. 128
Hypothesis Testing about Proportions	p. 130
Hypothesis Testing about the Variance	p. 131
Statistical Inference about Two Populations	p. 132
Inference about the difference between two means	p. 133
Small independent samples with equal variances	p. 134
Testing the hypothesis about two variances	p. 140
Testing for Normality of Data	p. 142
Statistical Process Control	p. 145
How to Build a Control Chart	p. 147
The Western Electric (WECO) Rules	p. 150
Types of Control Charts	p. 151
Attribute control charts	p. 151
Variable control charts	p. 159
Process Capability Analysis	p. 171
Process Capability with Normal Data	p. 174
Potential capabilities vs. actual capabilities	p. 176
Actual process capability indices	p. 178
Taguchi's Capability Indices C[subscript PM] and P[subscript PM]	p. 183
Process Capability and PPM	p. 185
Capability Sixpack for Normally Distributed Data	p. 193
Process Capability Analysis with Non-Normal Data	p. 194
Normality assumption and Box-Cox transformation	p. 195
Process capability using Box-Cox transformation	p. 196
Process capability using a non-normal distribution	p. 200
Analysis of Variance	p. 203
ANOVA and Hypothesis Testing	p. 203
Completely Randomized Experimental Design (One-Way ANOVA)	p. 204
Degrees of freedom	p. 206
Multiple comparison tests	p. 218
Randomized Block Design	p. 222
Analysis of Means (ANOM)	p. 226
Regression Analysis	p. 231
Building a Model with Only Two Variables: Simple Linear Regression	p. 232
Plotting the combination of x and y to visualize the relationship: scatter plot	p. 233
The regression equation	p. 240
Least squares method	p. 241
How far are the results of our analysis from the true values: residual analysis	p. 248
Standard error of estimate	p. 250
How strong is the relationship between x and y: correlation coefficient	p. 250
Coefficient of determination, or what proportion in the variation of y is explained by the changes in x	p. 255
Testing the validity of the regression line: hypothesis testing for the slope of the regression model	p. 255
Using the confidence interval to estimate the mean	p. 257
Fitted line plot	p. 258
Building a Model with More than Two Variables: Multiple Regression Analysis	p. 261
Hypothesis testing for the coefficients	p. 263
Stepwise regression	p. 266
Design of Experiment	p. 275
The Factorial Design with Two Factors	p. 276
How does ANOVA determine if the null hypothesis should be rejected or not?	p. 277
A mathematical approach	p. 279
Factorial Design with More than Two Factors (2[superscript k])	p. 285
The Taguchi Method	p. 289
Assessing the Cost of Quality	p. 289
Cost of conformance	p. 290
Cost of nonconformance	p. 290
Taguchi's Loss Function	p. 293
Variability Reduction	p. 295
Concept design	p. 297
Parameter design	p. 298
Tolerance design	p. 300
Measurement Systems Analysis-MSA: Is Your Measurement Process Lying to You?	p. 303
Variation Due to Precision: Assessing the Spread of the Measurement	p. 304
Gage repeatability & reproducibility crossed	p. 305
Gage R&R nested	p. 314
Gage Run Chart	p. 318
Variations Due to Accuracy	p. 320
Gage bias	p. 320
Gage linearity	p. 322
Nonparametric Statistics	p. 329
The Mann-Whitney U test	p. 330
The Mann-Whitney U test for small samples	p. 330
The Mann-Whitney U test for large samples	p. 333
The Chi-Square Tests	p. 336
The chi-square goodness-of-fit test	p. 336
Contingency analysis: chi-square test of independence	p. 342
Pinpointing the Vital Few Root Causes	p. 347
Pareto Analysis	p. 347
Cause and Effect Analysis	p. 350
Binominal Table P(x) = [subscript n]C[subscript x]p[superscript x]q[superscript n-x]	p. 354
Poisson Table P(x) = [lambda superscript x]e[superscript -lambda]/x	p. 357
Normal Z Table	p. 364
Student's t Table	p. 365
Chi-Square Table	p. 366
F Table [alpha] = 0.05	p. 367
Index	p. 369
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