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Foreword | |
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Preface | |
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Engineering in a Six Sigma Company | |
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Understanding Six Sigma and DFSS Terminology | |
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Laying the Foundation for DFSS | |
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Choosing the Best Statistical Tool | |
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Example of Statistical Tools in New Product Development | |
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Visualizing Data | |
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Case Study: Data Graphed Out of Context Leads to Incorrect Conclusions | |
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Visualizing Time Series Data | |
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Concealing the Story with Art | |
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Concealing Patterns by Aggregating Data | |
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Choosing the Aspect Ratio to Reveal Patterns | |
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Revealing Instability with the IX, MR Control Chart | |
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Visualizing the Distribution of Data | |
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Visualizing Distributions with Dot Graphs | |
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Visualizing Distributions with Boxplots | |
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Visualizing Distributions with Histograms | |
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Visualizing Distributions with Stem-and-Leaf Displays | |
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Revealing Patterns by Transforming Data | |
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Visualizing Bivariate Data | |
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Visualizing Bivariate Data with Scatter Plots | |
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Visualizing Both Marginal and Joint Distributions | |
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Visualizing Paired Data | |
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Visualizing Multivariate Data | |
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Visualizing Historical Data with Scatter Plot Matrices | |
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Visualizing Experimental Data with Multi-Vari Charts | |
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Summary: Guidelines for Visualizing Data with Integrity | |
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Describing Random Behavior | |
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Measuring Probability of Events | |
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Describing Collections of Events | |
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Calculating the Probability of Events | |
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Calculating Probability of Combinations of Events | |
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Calculating Probability of Conditional Chains of Events | |
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Calculating the Joint Probability of Independent Events | |
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Counting Possible Outcomes | |
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Counting Samples with Replacement | |
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Counting Ordered Samples without Replacement | |
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Counting Unordered Samples without Replacement | |
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Calculating Probabilities for Sampling Problems | |
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Calculating Probability Based on a Sample Space of Equally Likely Outcomes | |
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Calculating Sampling Probabilities from a Finite Population | |
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Calculating Sampling Probabilities from Populations with a Constant Probability of Defects | |
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Calculating Sampling Probabilities from a Continuous Medium | |
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Representing Random Processes by Random Variables | |
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Describing Random Variables | |
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Selecting the Appropriate Type of Random Variable | |
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Specifying a Random Variable as a Member of a Parametric Family | |
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Specifying the Cumulative Probability of a Random Variable | |
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Specifying the Probability Values of a Discrete Random Variable | |
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Specifying the Probability Density of a Continuous Random Variable | |
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Calculating Properties of Random Variables | |
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Calculating the Expected Value of a Random Variable | |
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Calculating Measures of Variation of a Random Variable | |
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Calculating Measures of Shape of a Random Variable | |
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Calculating Quantiles of a Random Variable | |
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Estimating Population Properties | |
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Communicating Estimation | |
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Sampling for Accuracy and Precision | |
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Selecting Good Estimators | |
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Selecting Appropriate Distribution Models | |
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Estimating Properties of a Normal Population | |
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Estimating the Population Mean | |
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Estimating the Population Standard Deviation | |
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Estimating Short-Term and Long-Term Properties of a Normal Population | |
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Planning Samples to Identify Short-Term and Long-Term Properties | |
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Estimating Short-Term and Long-Term Properties from Subgrouped Data | |
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Estimating Short-Term and Long-Term Properties from Individual Data | |
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Estimating Statistical Tolerance Bounds and Intervals | |
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Estimating Properties of Failure Time Distributions | |
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Describing Failure Time Distributions | |
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Estimating Reliability from Complete Life Data | |
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Estimating Reliability from Censored Life Data | |
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Estimating Reliability from Life Data with Zero Failures | |
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Estimating the Probability of Defective Units by the Binomial Probability [pi] | |
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Estimating the Probability of Defective Units [pi] | |
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Testing a Process for Stability in the Proportion of Defective Units | |
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Estimating the Rate of Defects by the Poisson Rate Parameter [lambda] | |
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Estimating the Poisson Rate Parameter [lambda] | |
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Testing a Process for Stability in the Rate of Defects | |
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Assessing Measurement Systems | |
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Assessing Measurement System Repeatability Using a Control Chart | |
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Assessing Measurement System Precision Using Gage R&R Studies | |
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Conducting a Gage R&R Study | |
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Step 1: Define Measurement System and Objective for MSA | |
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Step 2: Select n Parts for Measurement | |
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Step 3: Select k Appraisers | |
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Step 4: Select r, the Number of Replications | |
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Step 5: Randomize Measurement Order | |
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Step 6: Perform nkr Measurements | |
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Step 7: Analyze Data | |
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Step 8: Compute MSA Metrics | |
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Step 9: Reach Conclusions | |
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Assessing Sensory Evaluation with Gage R&R | |
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Investigating a Broken Measurement System | |
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Assessing Attribute Measurement Systems | |
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Assessing Agreement of Attribute Measurement Systems | |
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Assessing Bias and Repeatability of Attribute Measurement Systems | |
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Measuring Process Capability | |
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Verifying Process Stability | |
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Selecting the Most Appropriate Control Chart | |
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Continuous Measurement Data | |
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Count Data | |
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Interpreting Control Charts for Signs of Instability | |
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Calculating Measures of Process Capability | |
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Measuring Potential Capability | |
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Measuring Potential Capability with Bilateral Tolerances | |
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Measuring Potential Capability with Unilateral Tolerances | |
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Measuring Actual Capability | |
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Measuring Actual Capability with Bilateral Tolerances | |
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Measuring Actual Capability with Unilateral Tolerances | |
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Predicting Process Defect Rates | |
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Conducting a Process Capability Study | |
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Applying Process Capability Methods in a Six Sigma Company | |
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Dealing with Inconsistent Terminology | |
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Understanding the Mean Shift | |
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Converting between Long-Term and Short-Term | |
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Applying the DFSS Scorecard | |
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Building a Basic DFSS Scorecard | |
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Detecting Changes | |
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Conducting a Hypothesis Test | |
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Define Objective and State Hypothesis | |
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Choose Risks [alpha] and [beta] and Select Sample Size n | |
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Collect Data and Test Assumptions | |
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Calculate Statistics and Make Decision | |
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Detecting Changes in Variation | |
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Comparing Variation to a Specific Value | |
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Comparing Variations of Two Processes | |
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Comparing Variations of Three or More Processes | |
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Detecting Changes in Process Average | |
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Comparing Process Average to a Specific Value | |
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Comparing Averages of Two Processes | |
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Comparing Repeated Measures of Process Average | |
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Comparing Averages of Three or More Processes | |
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Detecting Changes in Discrete Data | |
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Detecting Changes in Proportions | |
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Comparing a Proportion to a Specific Value | |
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Comparing Two Proportions | |
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Detecting Changes in Defect Rates | |
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Detecting Associations in Categorical Data | |
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Detecting Changes in Nonnormal Data | |
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Detecting Changes Without Assuming a Distribution | |
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Comparing a Median to a Specific Value | |
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Comparing Two Process Distributions | |
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Comparing Two or More Process Medians | |
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Testing for Goodness of Fit | |
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Normalizing Data with Transformations | |
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Normalizing Data with the Box-Cox Transformation | |
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Normalizing Data with the Johnson Transformation | |
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Conducting Efficient Experiments | |
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Conducting Simple Experiments | |
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Changing Everything at Once | |
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Analyzing a Simple Experiment | |
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Insuring Against Experimental Risks | |
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Conducting a Computer-Aided Experiment | |
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Selecting a More Efficient Treatment Structure | |
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Understanding the Terminology and Procedure for Efficient Experiments | |
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Understanding Experimental Terminology | |
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Following a Procedure for Efficient Experiments | |
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Step 1: Define the Objective | |
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Step 2: Define the IPO Structure | |
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Step 3: Select Treatment Structure | |
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Step 4: Select Design Structure | |
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Step 5: Select Sample Size | |
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Step 6: Prepare to Collect Data | |
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Step 7: Collect Data | |
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Step 8: Determine Significant Effects | |
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Step 9: Reach Conclusions | |
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Step 10: Verify Conclusions | |
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Conducting Two-Level Experiments | |
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Selecting the Most Efficient Treatment Structure | |
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Calculating Sample Size | |
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Analyzing Screening Experiments | |
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Analyzing Modeling Experiments | |
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Testing a System for Nonlinearity with a Center Point Run | |
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Conducting Three-Level Experiments | |
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Improving Robustness with Experiments | |
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Predicting the Variation Caused by Tolerances | |
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Selecting Critical to Quality (CTQ) Characteristics | |
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Implementing Consistent Tolerance Design | |
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Predicting the Effects of Tolerances in Linear Systems | |
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Developing Linear Transfer Functions | |
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Calculating Worst-Case Limits | |
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Predicting the Variation of Linear Systems | |
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Applying the Root-Sum-Square Method to Tolerances | |
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Predicting the Effects of Tolerances in Nonlinear Systems | |
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Predicting Variation with Dependent Components | |
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Predicting Variation with Geometric Dimensioning and Tolerancing | |
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Optimizing System Variation | |
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Appendix | |
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References | |
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Index | |