Design for Six Sigma statistics: 59 tools for diagnosing and solving problems in DFSS initiatives
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York
McGraw-Hill
2006
|
| Schriftenreihe: | Six sigma operational methods series
|
| Schlagworte: | |
| Online-Zugang: | Contributor biographical information Publisher description Table of contents only Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references (p. 833-836) and index |
| Beschreibung: | xxiii, 854 p. ill. 24 cm |
| ISBN: | 0071451625 |
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| 245 | 1 | 0 | |a Design for Six Sigma statistics |b 59 tools for diagnosing and solving problems in DFSS initiatives |c Andrew D. Sleeper |
| 264 | 1 | |a New York |b McGraw-Hill |c 2006 | |
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Datensatz im Suchindex
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| adam_text | CONTENTS
Foreword xiii
Preface xix
Chapter 1 Engineering in a Six Sigma Company 1
1.1 Understanding Six Sigma and DFSS Terminology 2
1.2 Laying the Foundation for DFSS 11
1.3 Choosing the Best Statistical Tool 14
1.4 Example of Statistical Tools in New Product Development 21
Chapter 2 Visualizing Data 31
2.1 Case Study: Data Graphed Out of Context Leads to Incorrect
Conclusions 34
2.2 Visualizing Time Series Data 38
2.2.1 Concealing the Story with Art 38
2.2.2 Concealing Patterns by Aggregating Data 40
2.2.3 Choosing the Aspect Ratio to Reveal Patterns 43
2.2.4 Revealing Instability with the IX, MR Control Chart 46
2.3 Visualizing the Distribution of Data 50
2.3.1 Visualizing Distributions with Dot Graphs 51
2.3.2 Visualizing Distributions with Boxplots 55
2.3.3 Visualizing Distributions with Histograms 61
2.3.4 Visualizing Distributions with Stem-and-Leaf Displays 69
2.3.5 Revealing Patterns by Transforming Data 71
2.4 Visualizing Bivariate Data 74
2.4.1 Visualizing Bivariate Data with Scatter Plots 74
2.4.2 Visualizing Both Marginal and Joint Distributions 76
2.4.3 Visualizing Paired Data 79
2.5 Visualizing Multivariate Data 85
2.5.1 Visualizing Historical Data with Scatter Plot Matrices 86
2.5.2 Visualizing Experimental Data with Multi-Vari Charts 88
2.6 Summary: Guidelines for Visualizing Data with Integrity 93
Chapter 3 Describing Random Behavior 97
3.1 Measuring Probability of Events 98
3.1.1 Describing Collections of Events 98
3.1.2 Calculating the Probability of Events 101
3.1.2.1 Calculating Probability of Combinations of Events 102
3.1.2.2 Calculating Probability of Conditional Chains of Events 103
3.1.2.3 Calculating the Joint Probability of Independent Events 104
3.1.3 Counting Possible Outcomes 106
3.1.3.1 Counting Samples with Replacement 106
3.1.3.2 Counting Ordered Samples without Replacement 107
3.1.3.3 Counting Unordered Samples without Replacement 108
3.1.4 Calculating Probabilities for Sampling Problems 109
3.1.4.1 Calculating Probability Based on a Sample Space of Equally Likely
Outcomes 109
vii
viii Contents
3.1.4.2 Calculating Sampling Probabilities from a Finite Population 110
3.1.4.3 Calculating Sampling Probabilities from Populations with a Constant
Probability of Defects 113
3.1.4.4 Calculating Sampling Probabilities from a Continuous Medium 115
3.2 Representing Random Processes by Random Variables 116
3.2.1 Describing Random Variables 117
3.2.2 Selecting the Appropriate Type of Random Variable 118
3.2.3 Specifying a Random Variable as a Member of a Parametric Family 118
3.2.4 Specifying the Cumulative Probability of a Random Variable 120
3.2.5 Specifying the Probability Values of a Discrete Random Variable 124
3.2.6 Specifying the Probability Density of a Continuous Random Variable 125
3.3 Calculating Properties of Random Variables 129
3.3.1 Calculating the Expected Value of a Random Variable 129
3.3.2 Calculating Measures of Variation of a Random Variable 135
3.3.3 Calculating Measures of Shape of a Random Variable 138
3.3.4 Calculating Quantiles of a Random Variable 139
Chapter 4 Estimating Population Properties 145
4.1 Communicating Estimation 146
4.1.1 Sampling for Accuracy and Precision 147
4.1.2 Selecting Good Estimators 153
4.2 Selecting Appropriate Distribution Models 156
4.3 Estimating Properties of a Normal Population 158
4.3.1 Estimating the Population Mean 160
4.3.2 Estimating the Population Standard Deviation 173
4.3.3 Estimating Short-Term and Long-Term Properties of a Normal
Population 184
4.3.3.1 Planning Samples to Identify Short-Term and Long-Term Properties 185
4.3.3.2 Estimating Short-Term and Long-Term Properties from Subgrouped
Data 189
4.3.3.3 Estimating Short-Term and Long-Term Properties from
Individual Data 203
4.3.4 Estimating Statistical Tolerance Bounds and Intervals 211
4.4 Estimating Properties of Failure Time Distributions 216
4.4.1 Describing Failure Time Distributions 217
4.4.2 Estimating Reliability from Complete Life Data 223
4.4.3 Estimating Reliability from Censored Life Data 230
4.4.4 Estimating Reliability from Life Data with Zero Failures 234
4.5 Estimating the Probability of Defective Units by the Binomial
Probability -n 238
4.5.1 Estimating the Probability of Defective Units it 239
4.5.2 Testing a Process for Stability in the Proportion of Defective Units 244
4.6 Estimating the Rate of Defects by the Poisson Rate Parameter 248
4.6.1 Estimating the Poisson Rate Parameter 249
4.6.2 Testing a Process for Stability in the Rate of Defects 255
Chapter 5 Assessing Measurement Systems 261
5.1 Assessing Measurement System Repeatability Using a
Control Chart 265
5.2 Assessing Measurement System Precision Using Gage R R Studies 271
5.2.1 Conducting a Gage R R Study 272
5.2.1.1 Step 1: Define Measurement System and Objective for MSA 272
Contents ix
5.2.1.2 Step 2: Select n Parts for Measurement 274
5.2.1.3 Step 3: Select k Appraisers 275
5.2.1.4 Step 4: Select r, the Number of Replications 276
5.2.1.5 Step 5: Randomize Measurement Order 279
5.2.1.6 Step 6: Perform nkr Measurements 280
5.2.1.7 Step 7: Analyze Data 281
5.2.1.8 Step 8: Compute MSA Metrics 287
5.2.1.9 Step 9: Reach Conclusions 293
5.2.2 Assessing Sensory Evaluation with Gage R R 296
5.2.3 Investigating a Broken Measurement System 301
5.3 Assessing Attribute Measurement Systems 307
5.3.1 Assessing Agreement of Attribute Measurement Systems 308
5.3.2 Assessing Bias and Repeatability of Attribute
Measurement Systems 313
Chapter 6 Measuring Process Capability 319
6.1 Verifying Process Stability 321
6.1.1 Selecting the Most Appropriate Control Chart 324
6.1.1.1 Continuous Measurement Data 324
6.1.1.2 Count Data 326
6.1.2 Interpreting Control Charts for Signs of Instability 326
6.2 Calculating Measures of Process Capability 333
6.2.1 Measuring Potential Capability 336
6.2.1.1 Measuring Potential Capability with Bilateral Tolerances 336
6.2.1.2 Measuring Potential Capability with Unilateral Tolerances 342
6.2.2 Measuring Actual Capability 346
6.2.2.1 Measuring Actual Capability with Bilateral Tolerances 346
6.2.2.2 Measuring Actual Capability with Unilateral Tolerances 359
6.3 Predicting Process Defect Rates 361
6.4 Conducting a Process Capability Study 369
6.5 Applying Process Capability Methods in a Six Sigma Company 371
6.5.1 Dealing with Inconsistent Terminology 371
6.5.2 Understanding the Mean Shift 372
6.5.3 Converting between Long-Term and Short-Term 374
6.6 Applying the DFSS Scorecard 376
6.6.1 Building a Basic DFSS Scorecard 379
Chapter 7 Detecting Changes 385
7.1 Conducting a Hypothesis Test 387
7.1.1 Define Objective and State Hypothesis 388
7.1.2 Choose Risks a and (3 and Select Sample Size n 392
7.1.3 Collect Data and Test Assumptions 400
7.1.4 Calculate Statistics and Make Decision 405
7.2 Detecting Changes in Variation 410
7.2.1 Comparing Variation to a Specific Value 410
7.2.2 Comparing Variations of Two Processes 420
7.2.3 Comparing Variations of Three or More Processes 433
7.3 Detecting Changes in Process Average 440
7.3.1 Comparing Process Average to a Specific Value 441
7.3.2 Comparing Averages of Two Processes 450
7.3.3 Comparing Repeated Measures of Process Average 459
7.3.4 Comparing Averages of Three or More Processes 467
x Contents
Chapter 8 Detecting Changes in Discrete Data 477
8.1 Detecting Changes in Proportions 478
8.1.1 Comparing a Proportion to a Specific Value 480
8.1.2 Comparing Two Proportions 490
8.2 Detecting Changes in Defect Rates 496
8.3 Detecting Associations in Categorical Data 505
Chapter 9 Detecting Changes in Nonnormal Data 517
9.1 Detecting Changes Without Assuming a Distribution 518
9.1.1 Comparing a Median to a Specific Value 521
9.1.2 Comparing Two Process Distributions 535
9.1.3 Comparing Two or More Process Medians 539
9.2 Testing for Goodness of Fit 543
9.3 Normalizing Data with Transformations 560
9.3.1 Normalizing Data with the Box-Cox Transformation 561
9.3.2 Normalizing Data with the Johnson Transformation 570
Chapter 10 Conducting Efficient Experiments 575
10.1 Conducting Simple Experiments 578
10.1.1 Changing Everything at Once 578
10.1.2 Analyzing a Simple Experiment 582
10.1.3 Insuring Against Experimental Risks 590
10.1.4 Conducting a Computer-Aided Experiment 599
10.1.5 Selecting a More Efficient Treatment Structure 613
10.2 Understanding the Terminology and Procedure for
Efficient Experiments 619
10.2.1 Understanding Experimental Terminology 619
10.2.2 Following a Procedure for Efficient Experiments 622
10.2.2.1 Step 1: Define the Objective 623
10.2.2.2 Step 2: Define the IPO Structure 624
10.2.2.3 Step 3: Select Treatment Structure 626
10.2.2.4 Step 4: Select Design Structure 627
10.2.2.5 Step 5: Select Sample Size 628
10.2.2.6 Step 6: Prepare to Collect Data 629
10.2.2.7 Step 7: Collect Data 630
10.2.2.8 Step 8: Determine Significant Effects 631
10.2.2.9 Step 9: Reach Conclusions 632
10.2.2.10 Step 10: Verify Conclusions 632
10.3 Conducting Two-Level Experiments 633
10.3.1 Selecting the Most Efficient Treatment Structure 635
10.3.2 Calculating Sample Size 643
10.3.3 Analyzing Screening Experiments 648
10.3.4 Analyzing Modeling Experiments 655
10.3.5 Testing a System for Nonlinearity with a Center Point Run 663
10.4 Conducting Three-Level Experiments 669
10.5 Improving Robustness with Experiments 680
Chapter 11 Predicting the Variation Caused by Tolerances 685
11.1 Selecting Critical to Quality (CTQ) Characteristics 692
11.2 Implementing Consistent Tolerance Design 698
11.3 Predicting the Effects of Tolerances in Linear Systems 704
Contents xi
11.3.1 Developing Linear Transfer Functions 704
11.3.2 Calculating Worst-Case Limits 711
11.3.3 Predicting the Variation of Linear Systems 716
11.3.4 Applying the Root-Sum-Square Method to Tolerances 724
11.4 Predicting the Effects of Tolerances in Nonlinear Systems 731
11.5 Predicting Variation with Dependent Components 754
11.6 Predicting Variation with Geometric Dimensioning and Tolerancing 765
11.7 Optimizing System Variation 771
Appendix 791
References 833
Index 837
|
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| spelling | Sleeper, Andrew D. Verfasser aut Design for Six Sigma statistics 59 tools for diagnosing and solving problems in DFSS initiatives Andrew D. Sleeper New York McGraw-Hill 2006 xxiii, 854 p. ill. 24 cm txt rdacontent n rdamedia nc rdacarrier Six sigma operational methods series Includes bibliographical references (p. 833-836) and index Six Sigma swd Statistische Qualitätskontrolle swd Six sigma (Quality control standard) Quality control Statistical methods http://www.loc.gov/catdir/enhancements/fy0664/2005054679-b.html Contributor biographical information http://www.loc.gov/catdir/enhancements/fy0664/2005054679-d.html Publisher description http://www.loc.gov/catdir/enhancements/fy0664/2005054679-t.html Table of contents only HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017370250&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Sleeper, Andrew D. Design for Six Sigma statistics 59 tools for diagnosing and solving problems in DFSS initiatives Six Sigma swd Statistische Qualitätskontrolle swd Six sigma (Quality control standard) Quality control Statistical methods |
| title | Design for Six Sigma statistics 59 tools for diagnosing and solving problems in DFSS initiatives |
| title_auth | Design for Six Sigma statistics 59 tools for diagnosing and solving problems in DFSS initiatives |
| title_exact_search | Design for Six Sigma statistics 59 tools for diagnosing and solving problems in DFSS initiatives |
| title_full | Design for Six Sigma statistics 59 tools for diagnosing and solving problems in DFSS initiatives Andrew D. Sleeper |
| title_fullStr | Design for Six Sigma statistics 59 tools for diagnosing and solving problems in DFSS initiatives Andrew D. Sleeper |
| title_full_unstemmed | Design for Six Sigma statistics 59 tools for diagnosing and solving problems in DFSS initiatives Andrew D. Sleeper |
| title_short | Design for Six Sigma statistics |
| title_sort | design for six sigma statistics 59 tools for diagnosing and solving problems in dfss initiatives |
| title_sub | 59 tools for diagnosing and solving problems in DFSS initiatives |
| topic | Six Sigma swd Statistische Qualitätskontrolle swd Six sigma (Quality control standard) Quality control Statistical methods |
| topic_facet | Six Sigma Statistische Qualitätskontrolle Six sigma (Quality control standard) Quality control Statistical methods |
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