9780071489690

Six Sigma Statistics With Excel and Minitab

by
  • ISBN13:

    9780071489690

  • ISBN10:

    007148969X

  • Edition: 1st
  • Format: Hardcover
  • Copyright: 2007-07-18
  • Publisher: McGraw-Hill Education
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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

Prefacep. ix
Acknowledgmentsp. x
Introductionp. 1
Six Sigma Methodologyp. 2
Define the organizationp. 2
Measure the organizationp. 6
Analyze the organizationp. 11
Improve the organizationp. 13
Statistics, Quality Control, and Six Sigmap. 14
Poor quality defined as a deviation from engineered standardsp. 15
Sampling and quality controlp. 16
Statistical Definition of Six Sigmap. 16
Variability: the source of defectsp. 17
Evaluation of the process performancep. 18
Normal distribution and process capabilityp. IS
An Overview of Minitab and Microsoft Excelp. 23
Starting with Minitabp. 23
Minitab's menusp. 25
An Overview of Data Analysis with Excelp. 33
Graphical display of datap. 35
Data Analysis add-inp. 37
Basic Tools for Data Collection, Organization and Descriptionp. 41
The Measures of Central Tendency Give a First Perception of Your Datap. 42
Arithmetic meanp. 42
Geometric meanp. 47
Modep. 49
Medianp. 49
Measures of Dispersionp. 49
Rangep. 50
Mean deviationp. 50
Variancep. 52
Standard deviationp. 54
Chebycheff's theoremp. 55
Coefficient of variationp. 55
The Measures of Association Quantify the Level of Relatedness between Factorsp. 56
Covariancep. 56
Correlation coefficientp. 58
Coefficient of determinationp. 62
Graphical Representation of Datap. 62
Histogramsp. 62
Stem-and-leaf graphsp. 64
Box plotsp. 66
Descriptive Statistics-Minitab and Excel Summariesp. 68
Introduction to Basic Probabilityp. 73
Discrete Probability Distributionsp. 74
Binomial distributionp. 74
Poisson distributionp. 79
Poisson distribution, rolled throughput yield, and DPMOp. 80
Geometric distributionp. 84
Hypergeometric distributionp. 85
Continuous Distributionsp. 88
Exponential distributionp. 88
Normal distributionp. 90
The log-normal distributionp. 97
How to Determine, Analyze, and Interpret Your Samplesp. 99
How to Collect a Samplep. 100
Stratified samplingp. 100
Cluster samplingp. 100
Systematic samplingp. 100
Sampling Distribution of Meansp. 100
Sampling Errorp. 101
Central Limit Theoremp. 102
Sampling from a Finite Populationp. 106
Sampling Distribution of pp. 106
Estimating the Population Mean with Large Sample Sizesp. 108
Estimating the Population Mean with Small Sample Sizes and [sigma] Unknown: t-Distributionp. 113
Chi Square (x[superscript 2]) Distributionp. 114
Estimating Sample Sizesp. 117
Sample size when estimating the meanp. 117
Sample size when estimating the population proportionp. 118
Hypothesis Testingp. 121
How to Conduct a Hypothesis Testingp. 122
Null hypothesisp. 122
Alternate hypothesisp. 122
Test statisticp. 123
Level of significance or level of riskp. 123
Decision rule determinationp. 123
Decision makingp. 124
Testing for a Population Meanp. 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 Proportionsp. 130
Hypothesis Testing about the Variancep. 131
Statistical Inference about Two Populationsp. 132
Inference about the difference between two meansp. 133
Small independent samples with equal variancesp. 134
Testing the hypothesis about two variancesp. 140
Testing for Normality of Datap. 142
Statistical Process Controlp. 145
How to Build a Control Chartp. 147
The Western Electric (WECO) Rulesp. 150
Types of Control Chartsp. 151
Attribute control chartsp. 151
Variable control chartsp. 159
Process Capability Analysisp. 171
Process Capability with Normal Datap. 174
Potential capabilities vs. actual capabilitiesp. 176
Actual process capability indicesp. 178
Taguchi's Capability Indices C[subscript PM] and P[subscript PM]p. 183
Process Capability and PPMp. 185
Capability Sixpack for Normally Distributed Datap. 193
Process Capability Analysis with Non-Normal Datap. 194
Normality assumption and Box-Cox transformationp. 195
Process capability using Box-Cox transformationp. 196
Process capability using a non-normal distributionp. 200
Analysis of Variancep. 203
ANOVA and Hypothesis Testingp. 203
Completely Randomized Experimental Design (One-Way ANOVA)p. 204
Degrees of freedomp. 206
Multiple comparison testsp. 218
Randomized Block Designp. 222
Analysis of Means (ANOM)p. 226
Regression Analysisp. 231
Building a Model with Only Two Variables: Simple Linear Regressionp. 232
Plotting the combination of x and y to visualize the relationship: scatter plotp. 233
The regression equationp. 240
Least squares methodp. 241
How far are the results of our analysis from the true values: residual analysisp. 248
Standard error of estimatep. 250
How strong is the relationship between x and y: correlation coefficientp. 250
Coefficient of determination, or what proportion in the variation of y is explained by the changes in xp. 255
Testing the validity of the regression line: hypothesis testing for the slope of the regression modelp. 255
Using the confidence interval to estimate the meanp. 257
Fitted line plotp. 258
Building a Model with More than Two Variables: Multiple Regression Analysisp. 261
Hypothesis testing for the coefficientsp. 263
Stepwise regressionp. 266
Design of Experimentp. 275
The Factorial Design with Two Factorsp. 276
How does ANOVA determine if the null hypothesis should be rejected or not?p. 277
A mathematical approachp. 279
Factorial Design with More than Two Factors (2[superscript k])p. 285
The Taguchi Methodp. 289
Assessing the Cost of Qualityp. 289
Cost of conformancep. 290
Cost of nonconformancep. 290
Taguchi's Loss Functionp. 293
Variability Reductionp. 295
Concept designp. 297
Parameter designp. 298
Tolerance designp. 300
Measurement Systems Analysis-MSA: Is Your Measurement Process Lying to You?p. 303
Variation Due to Precision: Assessing the Spread of the Measurementp. 304
Gage repeatability & reproducibility crossedp. 305
Gage R&R nestedp. 314
Gage Run Chartp. 318
Variations Due to Accuracyp. 320
Gage biasp. 320
Gage linearityp. 322
Nonparametric Statisticsp. 329
The Mann-Whitney U testp. 330
The Mann-Whitney U test for small samplesp. 330
The Mann-Whitney U test for large samplesp. 333
The Chi-Square Testsp. 336
The chi-square goodness-of-fit testp. 336
Contingency analysis: chi-square test of independencep. 342
Pinpointing the Vital Few Root Causesp. 347
Pareto Analysisp. 347
Cause and Effect Analysisp. 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]/xp. 357
Normal Z Tablep. 364
Student's t Tablep. 365
Chi-Square Tablep. 366
F Table [alpha] = 0.05p. 367
Indexp. 369
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