Six Sigma Statistics with Excel and Minitab / Edition 1

ISBN-10: 007148969X

ISBN-13: 9780071489690

Pub. Date: 06/27/2007

Publisher: McGraw-Hill Professional Publishing

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

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Overview

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 and Minitab.

Product Details

ISBN-13:
9780071489690
Publisher:
McGraw-Hill Professional Publishing
Publication date:
06/27/2007
Pages:
374
Sales rank:
375,395
Product dimensions:
6.40(w) x 9.30(h) x 1.01(d)

Related Subjects

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

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