Six sigma statistics with Excel and Minitab

Title:

Personal Author:

Bass, Issa

Publication Information:

New York : McGraw-Hill, 2007

Physical Description:

1 CD-ROM ; 12 cm.

ISBN:

9780071489690

General Note:

Accompanies text of the same title : TS156 B432 2007

Subject Term:

Six sigma (Quality control standard)

Quality control -- Data processing

Quality control -- Statistical methods -- Computer programs

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Summary

Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product.

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

Author Notes

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

Preface	p. ix
Acknowledgments	p. x
Chapter 1 Introduction	p. 1
1.1 Six Sigma Methodology	p. 2
1.1.1 Define the organization	p. 2
1.1.2 Measure the organization	p. 6
1.1.3 Analyze the organization	p. 11
1.1.4 Improve the organization	p. 13
1.2 Statistics, Quality Control, and Six Sigma	p. 14
1.2.1 Poor quality defined as a deviation from engineered standards	p. 15
1.2.2 Sampling and quality control	p. 16
1.3 Statistical Definition of Six Sigma	p. 16
1.3.1 Variability: the source of defects	p. 17
1.3.2 Evaluation of the process performance	p. 18
1.3.3 Normal distribution and process capability	p. IS
Chapter 2 An Overview of Minitab and Microsoft Excel	p. 23
2.1 Starting with Minitab	p. 23
2.1.1 Minitab's menus	p. 25
2.2 An Overview of Data Analysis with Excel	p. 33
2.2.1 Graphical display of data	p. 35
2.2.2 Data Analysis add-in	p. 37
Chapter 3 Basic Tools for Data Collection, Organization and Description	p. 41
3.1 The Measures of Central Tendency Give a First Perception of Your Data	p. 42
3.1.1 Arithmetic mean	p. 42
3.1.2 Geometric mean	p. 47
3.1.3 Mode	p. 49
3.1.4 Median	p. 49
3.2 Measures of Dispersion	p. 49
3.2.1 Range	p. 50
3.2.2 Mean deviation	p. 50
3.2.3 Variance	p. 52
3.2.4 Standard deviation	p. 54
3.2.5 Chebycheff's theorem	p. 55
3.2.6 Coefficient of variation	p. 55
3.3 The Measures of Association Quantify the Level of Relatedness between Factors	p. 56
3.3.1 Covariance	p. 56
3.3.2 Correlation coefficient	p. 58
3.3.3 Coefficient of determination	p. 62
3.4 Graphical Representation of Data	p. 62
3.4.1 Histograms	p. 62
3.4.2 Stem-and-leaf graphs	p. 64
3.4.3 Box plots	p. 66
3.5 Descriptive Statistics-Minitab and Excel Summaries	p. 68
Chapter 4 Introduction to Basic Probability	p. 73
4.1 Discrete Probability Distributions	p. 74
4.1.1 Binomial distribution	p. 74
4.1.2 Poisson distribution	p. 79
4.1.3 Poisson distribution, rolled throughput yield, and DPMO	p. 80
4.1.4 Geometric distribution	p. 84
4.1.5 Hypergeometric distribution	p. 85
4.2 Continuous Distributions	p. 88
4.2.1 Exponential distribution	p. 88
4.2.2 Normal distribution	p. 90
4.2.3 The log-normal distribution	p. 97
Chapter 5 How to Determine, Analyze, and Interpret Your Samples	p. 99
5.1 How to Collect a Sample	p. 100
5.1.1 Stratified sampling	p. 100
5.1.2 Cluster sampling	p. 100
5.1.3 Systematic sampling	p. 100
5.2 Sampling Distribution of Means	p. 100
5.3 Sampling Error	p. 101
5.4 Central Limit Theorem	p. 102
5.5 Sampling from a Finite Population	p. 106
5.6 Sampling Distribution of p	p. 106
5.7 Estimating the Population Mean with Large Sample Sizes	p. 108
5.8 Estimating the Population Mean with Small Sample Sizes and [sigma] Unknown: t-Distribution	p. 113
5.9 Chi Square (x[superscript 2]) Distribution	p. 114
5.10 Estimating Sample Sizes	p. 117
5.10.1 Sample size when estimating the mean	p. 117
5.10.2 Sample size when estimating the population proportion	p. 118
Chapter 6 Hypothesis Testing	p. 121
6.1 How to Conduct a Hypothesis Testing	p. 122
6.1.1 Null hypothesis	p. 122
6.1.2 Alternate hypothesis	p. 122
6.1.3 Test statistic	p. 123
6.1.4 Level of significance or level of risk	p. 123
6.1.5 Decision rule determination	p. 123
6.1.6 Decision making	p. 124
6.2 Testing for a Population Mean	p. 124
6.2.1 Large sample with known [sigma]	p. 124
6.2.2 What is the p-value and how is it interpreted?	p. 126
6.2.3 Small samples with unknown [sigma]	p. 128
6.3 Hypothesis Testing about Proportions	p. 130
6.4 Hypothesis Testing about the Variance	p. 131
6.5 Statistical Inference about Two Populations	p. 132
6.5.1 Inference about the difference between two means	p. 133
6.5.2 Small independent samples with equal variances	p. 134
6.5.3 Testing the hypothesis about two variances	p. 140
6.6 Testing for Normality of Data	p. 142
Chapter 7 Statistical Process Control	p. 145
7.1 How to Build a Control Chart	p. 147
7.2 The Western Electric (WECO) Rules	p. 150
7.3 Types of Control Charts	p. 151
7.3.1 Attribute control charts	p. 151
7.3.2 Variable control charts	p. 159
Chapter 8 Process Capability Analysis	p. 171
8.1 Process Capability with Normal Data	p. 174
8.1.1 Potential capabilities vs. actual capabilities	p. 176
8.1.2 Actual process capability indices	p. 178
8.2 Taguchi's Capability Indices C[subscript PM] and P[subscript PM]	p. 183
8.3 Process Capability and PPM	p. 185
8.4 Capability Sixpack for Normally Distributed Data	p. 193
8.5 Process Capability Analysis with Non-Normal Data	p. 194
8.5.1 Normality assumption and Box-Cox transformation	p. 195
8.5.2 Process capability using Box-Cox transformation	p. 196
8.5.3 Process capability using a non-normal distribution	p. 200
Chapter 9 Analysis of Variance	p. 203
9.1 ANOVA and Hypothesis Testing	p. 203
9.2 Completely Randomized Experimental Design (One-Way ANOVA)	p. 204
9.2.1 Degrees of freedom	p. 206
9.2.2 Multiple comparison tests	p. 218
9.3 Randomized Block Design	p. 222
9.4 Analysis of Means (ANOM)	p. 226
Chapter 10 Regression Analysis	p. 231
10.1 Building a Model with Only Two Variables: Simple Linear Regression	p. 232
10.1.1 Plotting the combination of x and y to visualize the relationship: scatter plot	p. 233
10.1.2 The regression equation	p. 240
10.1.3 Least squares method	p. 241
10.1.4 How far are the results of our analysis from the true values: residual analysis	p. 248
10.1.5 Standard error of estimate	p. 250
10.1.6 How strong is the relationship between x and y: correlation coefficient	p. 250
10.1.7 Coefficient of determination, or what proportion in the variation of y is explained by the changes in x	p. 255
10.1.8 Testing the validity of the regression line: hypothesis testing for the slope of the regression model	p. 255
10.1.9 Using the confidence interval to estimate the mean	p. 257
10.1.10 Fitted line plot	p. 258
10.2 Building a Model with More than Two Variables: Multiple Regression Analysis	p. 261
10.2.1 Hypothesis testing for the coefficients	p. 263
10.2.2 Stepwise regression	p. 266
Chapter 11 Design of Experiment	p. 275
11.1 The Factorial Design with Two Factors	p. 276
11.1.1 How does ANOVA determine if the null hypothesis should be rejected or not?	p. 277
11.1.2 A mathematical approach	p. 279
11.2 Factorial Design with More than Two Factors (2[superscript k])	p. 285
Chapter 12 The Taguchi Method	p. 289
12.1 Assessing the Cost of Quality	p. 289
12.1.1 Cost of conformance	p. 290
12.1.2 Cost of nonconformance	p. 290
12.2 Taguchi's Loss Function	p. 293
12.3 Variability Reduction	p. 295
12.3.1 Concept design	p. 297
12.3.2 Parameter design	p. 298
12.3.3 Tolerance design	p. 300
Chapter 13 Measurement Systems Analysis-MSA: Is Your Measurement Process Lying to You?	p. 303
13.1 Variation Due to Precision: Assessing the Spread of the Measurement	p. 304
13.1.1 Gage repeatability & reproducibility crossed	p. 305
13.1.2 Gage R&R nested	p. 314
13.2 Gage Run Chart	p. 318
13.3 Variations Due to Accuracy	p. 320
13.3.1 Gage bias	p. 320
13.3.2 Gage linearity	p. 322
Chapter 14 Nonparametric Statistics	p. 329
14.1 The Mann-Whitney U test	p. 330
14.1.1 The Mann-Whitney U test for small samples	p. 330
14.1.2 The Mann-Whitney U test for large samples	p. 333
14.2 The Chi-Square Tests	p. 336
14.2.1 The chi-square goodness-of-fit test	p. 336
14.2.2 Contingency analysis: chi-square test of independence	p. 342
Chapter 15 Pinpointing the Vital Few Root Causes	p. 347
15.1 Pareto Analysis	p. 347
15.2 Cause and Effect Analysis	p. 350
Appendix 1 Binominal Table P(x) = [subscript n]C[subscript x]p[superscript x]q[superscript n-x]	p. 354
Appendix 2 Poisson Table P(x) = [lambda superscript x]e[superscript -lambda]/x	p. 357
Appendix 3 Normal Z Table	p. 364
Appendix 4 Student's t Table	p. 365
Appendix 5 Chi-Square Table	p. 366
Appendix 6 F Table [alpha] = 0.05	p. 367
Index	p. 369

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Author Notes

Table of Contents