engineering statistics handbook

Detailed Table of Contents

http://www.itl.nist.gov/div898/handbook/dtoc.htm[4/17/2013 7:03:11 PM]

Detailed Table of Contents for the Handbook

Chapter [1] [2] [3] [4] [5] [6] [7] [8]

1. Exploratory Data Analysis

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1. EDA Introduction [1.1.]1. What is EDA? [1.1.1.]2. How Does Exploratory Data Analysis differ from Classical Data

Analysis? [1.1.2.]1. Model [1.1.2.1.]2. Focus [1.1.2.2.]3. Techniques [1.1.2.3.]4. Rigor [1.1.2.4.]5. Data Treatment [1.1.2.5.]6. Assumptions [1.1.2.6.]

3. How Does Exploratory Data Analysis Differ from SummaryAnalysis? [1.1.3.]

4. What are the EDA Goals? [1.1.4.]5. The Role of Graphics [1.1.5.]6. An EDA/Graphics Example [1.1.6.]7. General Problem Categories [1.1.7.]

2. EDA Assumptions [1.2.]1. Underlying Assumptions [1.2.1.]2. Importance [1.2.2.]3. Techniques for Testing Assumptions [1.2.3.]4. Interpretation of 4-Plot [1.2.4.]5. Consequences [1.2.5.]

1. Consequences of Non-Randomness [1.2.5.1.]2. Consequences of Non-Fixed Location Parameter [1.2.5.2.]3. Consequences of Non-Fixed Variation Parameter [1.2.5.3.]4. Consequences Related to Distributional

Assumptions [1.2.5.4.]

3. EDA Techniques [1.3.]1. Introduction [1.3.1.]2. Analysis Questions [1.3.2.]3. Graphical Techniques: Alphabetic [1.3.3.]

1. Autocorrelation Plot [1.3.3.1.]1. Autocorrelation Plot: Random Data [1.3.3.1.1.]2. Autocorrelation Plot: Moderate

Autocorrelation [1.3.3.1.2.]3. Autocorrelation Plot: Strong Autocorrelation and

Autoregressive Model [1.3.3.1.3.]4. Autocorrelation Plot: Sinusoidal Model [1.3.3.1.4.]



2. Bihistogram [1.3.3.2.]3. Block Plot [1.3.3.3.]4. Bootstrap Plot [1.3.3.4.]5. Box-Cox Linearity Plot [1.3.3.5.]6. Box-Cox Normality Plot [1.3.3.6.]7. Box Plot [1.3.3.7.]8. Complex Demodulation Amplitude Plot [1.3.3.8.]9. Complex Demodulation Phase Plot [1.3.3.9.]

10. Contour Plot [1.3.3.10.]1. DOE Contour Plot [1.3.3.10.1.]

11. DOE Scatter Plot [1.3.3.11.]12. DOE Mean Plot [1.3.3.12.]13. DOE Standard Deviation Plot [1.3.3.13.]14. Histogram [1.3.3.14.]

1. Histogram Interpretation: Normal [1.3.3.14.1.]2. Histogram Interpretation: Symmetric, Non-Normal,

Short-Tailed [1.3.3.14.2.]3. Histogram Interpretation: Symmetric, Non-Normal,

Long-Tailed [1.3.3.14.3.]4. Histogram Interpretation: Symmetric and

Bimodal [1.3.3.14.4.]5. Histogram Interpretation: Bimodal Mixture of 2

Normals [1.3.3.14.5.]6. Histogram Interpretation: Skewed (Non-Normal)

Right [1.3.3.14.6.]7. Histogram Interpretation: Skewed (Non-Symmetric)

Left [1.3.3.14.7.]8. Histogram Interpretation: Symmetric with

Outlier [1.3.3.14.8.]15. Lag Plot [1.3.3.15.]

1. Lag Plot: Random Data [1.3.3.15.1.]2. Lag Plot: Moderate Autocorrelation [1.3.3.15.2.]3. Lag Plot: Strong Autocorrelation and Autoregressive

Model [1.3.3.15.3.]4. Lag Plot: Sinusoidal Models and Outliers [1.3.3.15.4.]

16. Linear Correlation Plot [1.3.3.16.]17. Linear Intercept Plot [1.3.3.17.]18. Linear Slope Plot [1.3.3.18.]19. Linear Residual Standard Deviation Plot [1.3.3.19.]20. Mean Plot [1.3.3.20.]21. Normal Probability Plot [1.3.3.21.]

1. Normal Probability Plot: Normally DistributedData [1.3.3.21.1.]

2. Normal Probability Plot: Data Have ShortTails [1.3.3.21.2.]

3. Normal Probability Plot: Data Have LongTails [1.3.3.21.3.]

4. Normal Probability Plot: Data are SkewedRight [1.3.3.21.4.]

22. Probability Plot [1.3.3.22.]23. Probability Plot Correlation Coefficient Plot [1.3.3.23.]24. Quantile-Quantile Plot [1.3.3.24.]25. Run-Sequence Plot [1.3.3.25.]26. Scatter Plot [1.3.3.26.]



1. Scatter Plot: No Relationship [1.3.3.26.1.]2. Scatter Plot: Strong Linear (positive correlation)

Relationship [1.3.3.26.2.]3. Scatter Plot: Strong Linear (negative correlation)

Relationship [1.3.3.26.3.]4. Scatter Plot: Exact Linear (positive correlation)

Relationship [1.3.3.26.4.]5. Scatter Plot: Quadratic Relationship [1.3.3.26.5.]6. Scatter Plot: Exponential Relationship [1.3.3.26.6.]7. Scatter Plot: Sinusoidal Relationship

(damped) [1.3.3.26.7.]8. Scatter Plot: Variation of Y Does Not Depend on X

(homoscedastic) [1.3.3.26.8.]9. Scatter Plot: Variation of Y Does Depend on X

(heteroscedastic) [1.3.3.26.9.]10. Scatter Plot: Outlier [1.3.3.26.10.]11. Scatterplot Matrix [1.3.3.26.11.]12. Conditioning Plot [1.3.3.26.12.]

27. Spectral Plot [1.3.3.27.]1. Spectral Plot: Random Data [1.3.3.27.1.]2. Spectral Plot: Strong Autocorrelation and

Autoregressive Model [1.3.3.27.2.]3. Spectral Plot: Sinusoidal Model [1.3.3.27.3.]

28. Standard Deviation Plot [1.3.3.28.]29. Star Plot [1.3.3.29.]30. Weibull Plot [1.3.3.30.]31. Youden Plot [1.3.3.31.]

1. DOE Youden Plot [1.3.3.31.1.]32. 4-Plot [1.3.3.32.]33. 6-Plot [1.3.3.33.]

4. Graphical Techniques: By Problem Category [1.3.4.]5. Quantitative Techniques [1.3.5.]

1. Measures of Location [1.3.5.1.]2. Confidence Limits for the Mean [1.3.5.2.]3. Two-Sample t-Test for Equal Means [1.3.5.3.]

1. Data Used for Two-Sample t-Test [1.3.5.3.1.]4. One-Factor ANOVA [1.3.5.4.]5. Multi-factor Analysis of Variance [1.3.5.5.]6. Measures of Scale [1.3.5.6.]7. Bartlett's Test [1.3.5.7.]8. Chi-Square Test for the Standard Deviation [1.3.5.8.]

1. Data Used for Chi-Square Test for the StandardDeviation [1.3.5.8.1.]

9. F-Test for Equality of Two Standard Deviations [1.3.5.9.]10. Levene Test for Equality of Variances [1.3.5.10.]11. Measures of Skewness and Kurtosis [1.3.5.11.]12. Autocorrelation [1.3.5.12.]13. Runs Test for Detecting Non-randomness [1.3.5.13.]14. Anderson-Darling Test [1.3.5.14.]15. Chi-Square Goodness-of-Fit Test [1.3.5.15.]16. Kolmogorov-Smirnov Goodness-of-Fit Test [1.3.5.16.]17. Grubbs' Test for Outliers [1.3.5.17.]18. Yates Analysis [1.3.5.18.]

1. Defining Models and Prediction Equations [1.3.5.18.1.]



2. Important Factors [1.3.5.18.2.]6. Probability Distributions [1.3.6.]

1. What is a Probability Distribution [1.3.6.1.]2. Related Distributions [1.3.6.2.]3. Families of Distributions [1.3.6.3.]4. Location and Scale Parameters [1.3.6.4.]5. Estimating the Parameters of a Distribution [1.3.6.5.]

1. Method of Moments [1.3.6.5.1.]2. Maximum Likelihood [1.3.6.5.2.]3. Least Squares [1.3.6.5.3.]4. PPCC and Probability Plots [1.3.6.5.4.]

6. Gallery of Distributions [1.3.6.6.]1. Normal Distribution [1.3.6.6.1.]2. Uniform Distribution [1.3.6.6.2.]3. Cauchy Distribution [1.3.6.6.3.]4. t Distribution [1.3.6.6.4.]5. F Distribution [1.3.6.6.5.]6. Chi-Square Distribution [1.3.6.6.6.]7. Exponential Distribution [1.3.6.6.7.]8. Weibull Distribution [1.3.6.6.8.]9. Lognormal Distribution [1.3.6.6.9.]

10. Fatigue Life Distribution [1.3.6.6.10.]11. Gamma Distribution [1.3.6.6.11.]12. Double Exponential Distribution [1.3.6.6.12.]13. Power Normal Distribution [1.3.6.6.13.]14. Power Lognormal Distribution [1.3.6.6.14.]15. Tukey-Lambda Distribution [1.3.6.6.15.]16. Extreme Value Type I Distribution [1.3.6.6.16.]17. Beta Distribution [1.3.6.6.17.]18. Binomial Distribution [1.3.6.6.18.]19. Poisson Distribution [1.3.6.6.19.]

7. Tables for Probability Distributions [1.3.6.7.]1. Cumulative Distribution Function of the Standard

Normal Distribution [1.3.6.7.1.]2. Upper Critical Values of the Student's-t

Distribution [1.3.6.7.2.]3. Upper Critical Values of the F Distribution [1.3.6.7.3.]4. Critical Values of the Chi-Square

Distribution [1.3.6.7.4.]5. Critical Values of the t* Distribution [1.3.6.7.5.]6. Critical Values of the Normal PPCC

Distribution [1.3.6.7.6.]

4. EDA Case Studies [1.4.]1. Case Studies Introduction [1.4.1.]2. Case Studies [1.4.2.]

1. Normal Random Numbers [1.4.2.1.]1. Background and Data [1.4.2.1.1.]2. Graphical Output and Interpretation [1.4.2.1.2.]3. Quantitative Output and Interpretation [1.4.2.1.3.]4. Work This Example Yourself [1.4.2.1.4.]

2. Uniform Random Numbers [1.4.2.2.]1. Background and Data [1.4.2.2.1.]



2. Graphical Output and Interpretation [1.4.2.2.2.]3. Quantitative Output and Interpretation [1.4.2.2.3.]4. Work This Example Yourself [1.4.2.2.4.]

3. Random Walk [1.4.2.3.]1. Background and Data [1.4.2.3.1.]2. Test Underlying Assumptions [1.4.2.3.2.]3. Develop A Better Model [1.4.2.3.3.]4. Validate New Model [1.4.2.3.4.]5. Work This Example Yourself [1.4.2.3.5.]

4. Josephson Junction Cryothermometry [1.4.2.4.]1. Background and Data [1.4.2.4.1.]2. Graphical Output and Interpretation [1.4.2.4.2.]3. Quantitative Output and Interpretation [1.4.2.4.3.]4. Work This Example Yourself [1.4.2.4.4.]

5. Beam Deflections [1.4.2.5.]1. Background and Data [1.4.2.5.1.]2. Test Underlying Assumptions [1.4.2.5.2.]3. Develop a Better Model [1.4.2.5.3.]4. Validate New Model [1.4.2.5.4.]5. Work This Example Yourself [1.4.2.5.5.]

6. Filter Transmittance [1.4.2.6.]1. Background and Data [1.4.2.6.1.]2. Graphical Output and Interpretation [1.4.2.6.2.]3. Quantitative Output and Interpretation [1.4.2.6.3.]4. Work This Example Yourself [1.4.2.6.4.]

7. Standard Resistor [1.4.2.7.]1. Background and Data [1.4.2.7.1.]2. Graphical Output and Interpretation [1.4.2.7.2.]3. Quantitative Output and Interpretation [1.4.2.7.3.]4. Work This Example Yourself [1.4.2.7.4.]

8. Heat Flow Meter 1 [1.4.2.8.]1. Background and Data [1.4.2.8.1.]2. Graphical Output and Interpretation [1.4.2.8.2.]3. Quantitative Output and Interpretation [1.4.2.8.3.]4. Work This Example Yourself [1.4.2.8.4.]

9. Fatigue Life of Aluminum Alloy Specimens [1.4.2.9.]1. Background and Data [1.4.2.9.1.]2. Graphical Output and Interpretation [1.4.2.9.2.]

10. Ceramic Strength [1.4.2.10.]1. Background and Data [1.4.2.10.1.]2. Analysis of the Response Variable [1.4.2.10.2.]3. Analysis of the Batch Effect [1.4.2.10.3.]4. Analysis of the Lab Effect [1.4.2.10.4.]5. Analysis of Primary Factors [1.4.2.10.5.]6. Work This Example Yourself [1.4.2.10.6.]

3. References For Chapter 1: Exploratory Data Analysis [1.4.3.]

2. Measurement Process Characterization

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1. Characterization [2.1.]



1. What are the issues for characterization? [2.1.1.]1. Purpose [2.1.1.1.]2. Reference base [2.1.1.2.]3. Bias and Accuracy [2.1.1.3.]4. Variability [2.1.1.4.]

2. What is a check standard? [2.1.2.]1. Assumptions [2.1.2.1.]2. Data collection [2.1.2.2.]3. Analysis [2.1.2.3.]

2. Statistical control of a measurement process [2.2.]1. What are the issues in controlling the measurement

process? [2.2.1.]2. How are bias and variability controlled? [2.2.2.]

1. Shewhart control chart [2.2.2.1.]1. EWMA control chart [2.2.2.1.1.]

2. Data collection [2.2.2.2.]3. Monitoring bias and long-term variability [2.2.2.3.]4. Remedial actions [2.2.2.4.]

3. How is short-term variability controlled? [2.2.3.]1. Control chart for standard deviations [2.2.3.1.]2. Data collection [2.2.3.2.]3. Monitoring short-term precision [2.2.3.3.]4. Remedial actions [2.2.3.4.]

3. Calibration [2.3.]1. Issues in calibration [2.3.1.]

1. Reference base [2.3.1.1.]2. Reference standards [2.3.1.2.]

2. What is artifact (single-point) calibration? [2.3.2.]3. What are calibration designs? [2.3.3.]

1. Elimination of special types of bias [2.3.3.1.]1. Left-right (constant instrument) bias [2.3.3.1.1.]2. Bias caused by instrument drift [2.3.3.1.2.]

2. Solutions to calibration designs [2.3.3.2.]1. General matrix solutions to calibration

designs [2.3.3.2.1.]3. Uncertainties of calibrated values [2.3.3.3.]

1. Type A evaluations for calibration designs [2.3.3.3.1.]2. Repeatability and level-2 standard

deviations [2.3.3.3.2.]3. Combination of repeatability and level-2 standard

deviations [2.3.3.3.3.]4. Calculation of standard deviations for 1,1,1,1

design [2.3.3.3.4.]5. Type B uncertainty [2.3.3.3.5.]6. Expanded uncertainties [2.3.3.3.6.]

4. Catalog of calibration designs [2.3.4.]1. Mass weights [2.3.4.1.]

1. Design for 1,1,1 [2.3.4.1.1.]2. Design for 1,1,1,1 [2.3.4.1.2.]3. Design for 1,1,1,1,1 [2.3.4.1.3.]4. Design for 1,1,1,1,1,1 [2.3.4.1.4.]5. Design for 2,1,1,1 [2.3.4.1.5.]



6. Design for 2,2,1,1,1 [2.3.4.1.6.]7. Design for 2,2,2,1,1 [2.3.4.1.7.]8. Design for 5,2,2,1,1,1 [2.3.4.1.8.]9. Design for 5,2,2,1,1,1,1 [2.3.4.1.9.]

10. Design for 5,3,2,1,1,1 [2.3.4.1.10.]11. Design for 5,3,2,1,1,1,1 [2.3.4.1.11.]12. Design for 5,3,2,2,1,1,1 [2.3.4.1.12.]13. Design for 5,4,4,3,2,2,1,1 [2.3.4.1.13.]14. Design for 5,5,2,2,1,1,1,1 [2.3.4.1.14.]15. Design for 5,5,3,2,1,1,1 [2.3.4.1.15.]16. Design for 1,1,1,1,1,1,1,1 weights [2.3.4.1.16.]17. Design for 3,2,1,1,1 weights [2.3.4.1.17.]18. Design for 10 and 20 pound weights [2.3.4.1.18.]

2. Drift-elimination designs for gage blocks [2.3.4.2.]1. Doiron 3-6 Design [2.3.4.2.1.]2. Doiron 3-9 Design [2.3.4.2.2.]3. Doiron 4-8 Design [2.3.4.2.3.]4. Doiron 4-12 Design [2.3.4.2.4.]5. Doiron 5-10 Design [2.3.4.2.5.]6. Doiron 6-12 Design [2.3.4.2.6.]7. Doiron 7-14 Design [2.3.4.2.7.]8. Doiron 8-16 Design [2.3.4.2.8.]9. Doiron 9-18 Design [2.3.4.2.9.]

10. Doiron 10-20 Design [2.3.4.2.10.]11. Doiron 11-22 Design [2.3.4.2.11.]

3. Designs for electrical quantities [2.3.4.3.]1. Left-right balanced design for 3 standard

cells [2.3.4.3.1.]2. Left-right balanced design for 4 standard



cells [2.3.4.3.4.]5. Left-right balanced design for 4 references and 4 test

items [2.3.4.3.5.]6. Design for 8 references and 8 test items [2.3.4.3.6.]7. Design for 4 reference zeners and 2 test

zeners [2.3.4.3.7.]8. Design for 4 reference zeners and 3 test

zeners [2.3.4.3.8.]9. Design for 3 references and 1 test resistor [2.3.4.3.9.]

10. Design for 4 references and 1 test resistor [2.3.4.3.10.]4. Roundness measurements [2.3.4.4.]

1. Single trace roundness design [2.3.4.4.1.]2. Multiple trace roundness designs [2.3.4.4.2.]

5. Designs for angle blocks [2.3.4.5.]1. Design for 4 angle blocks [2.3.4.5.1.]2. Design for 5 angle blocks [2.3.4.5.2.]3. Design for 6 angle blocks [2.3.4.5.3.]

6. Thermometers in a bath [2.3.4.6.]7. Humidity standards [2.3.4.7.]

1. Drift-elimination design for 2 reference weights and 3cylinders [2.3.4.7.1.]



5. Control of artifact calibration [2.3.5.]1. Control of precision [2.3.5.1.]

1. Example of control chart for precision [2.3.5.1.1.]2. Control of bias and long-term variability [2.3.5.2.]

1. Example of Shewhart control chart for masscalibrations [2.3.5.2.1.]

2. Example of EWMA control chart for masscalibrations [2.3.5.2.2.]

6. Instrument calibration over a regime [2.3.6.]1. Models for instrument calibration [2.3.6.1.]2. Data collection [2.3.6.2.]3. Assumptions for instrument calibration [2.3.6.3.]4. What can go wrong with the calibration procedure [2.3.6.4.]

1. Example of day-to-day changes incalibration [2.3.6.4.1.]

5. Data analysis and model validation [2.3.6.5.]1. Data on load cell #32066 [2.3.6.5.1.]

6. Calibration of future measurements [2.3.6.6.]7. Uncertainties of calibrated values [2.3.6.7.]

1. Uncertainty for quadratic calibration using propagationof error [2.3.6.7.1.]

2. Uncertainty for linear calibration using checkstandards [2.3.6.7.2.]

3. Comparison of check standard analysis and propagationof error [2.3.6.7.3.]

7. Instrument control for linear calibration [2.3.7.]1. Control chart for a linear calibration line [2.3.7.1.]

4. Gauge R & R studies [2.4.]1. What are the important issues? [2.4.1.]2. Design considerations [2.4.2.]3. Data collection for time-related sources of variability [2.4.3.]

1. Simple design [2.4.3.1.]2. 2-level nested design [2.4.3.2.]3. 3-level nested design [2.4.3.3.]

4. Analysis of variability [2.4.4.]1. Analysis of repeatability [2.4.4.1.]2. Analysis of reproducibility [2.4.4.2.]3. Analysis of stability [2.4.4.3.]

1. Example of calculations [2.4.4.4.4.]5. Analysis of bias [2.4.5.]

1. Resolution [2.4.5.1.]2. Linearity of the gauge [2.4.5.2.]3. Drift [2.4.5.3.]4. Differences among gauges [2.4.5.4.]5. Geometry/configuration differences [2.4.5.5.]6. Remedial actions and strategies [2.4.5.6.]

6. Quantifying uncertainties from a gauge study [2.4.6.]

5. Uncertainty analysis [2.5.]1. Issues [2.5.1.]2. Approach [2.5.2.]

1. Steps [2.5.2.1.]3. Type A evaluations [2.5.3.]



1. Type A evaluations of random components [2.5.3.1.]1. Type A evaluations of time-dependent

effects [2.5.3.1.1.]2. Measurement configuration within the

laboratory [2.5.3.1.2.]2. Material inhomogeneity [2.5.3.2.]

1. Data collection and analysis [2.5.3.2.1.]3. Type A evaluations of bias [2.5.3.3.]

1. Inconsistent bias [2.5.3.3.1.]2. Consistent bias [2.5.3.3.2.]3. Bias with sparse data [2.5.3.3.3.]

4. Type B evaluations [2.5.4.]1. Standard deviations from assumed distributions [2.5.4.1.]

5. Propagation of error considerations [2.5.5.]1. Formulas for functions of one variable [2.5.5.1.]2. Formulas for functions of two variables [2.5.5.2.]3. Propagation of error for many variables [2.5.5.3.]

6. Uncertainty budgets and sensitivity coefficients [2.5.6.]1. Sensitivity coefficients for measurements on the test

item [2.5.6.1.]2. Sensitivity coefficients for measurements on a check

standard [2.5.6.2.]3. Sensitivity coefficients for measurements from a 2-level

design [2.5.6.3.]4. Sensitivity coefficients for measurements from a 3-level

design [2.5.6.4.]5. Example of uncertainty budget [2.5.6.5.]

7. Standard and expanded uncertainties [2.5.7.]1. Degrees of freedom [2.5.7.1.]

8. Treatment of uncorrected bias [2.5.8.]1. Computation of revised uncertainty [2.5.8.1.]

6. Case studies [2.6.]1. Gauge study of resistivity probes [2.6.1.]

1. Background and data [2.6.1.1.]1. Database of resistivity measurements [2.6.1.1.1.]

2. Analysis and interpretation [2.6.1.2.]3. Repeatability standard deviations [2.6.1.3.]4. Effects of days and long-term stability [2.6.1.4.]5. Differences among 5 probes [2.6.1.5.]6. Run gauge study example using Dataplot [2.6.1.6.]7. Dataplot macros [2.6.1.7.]

2. Check standard for resistivity measurements [2.6.2.]1. Background and data [2.6.2.1.]

1. Database for resistivity check standard [2.6.2.1.1.]2. Analysis and interpretation [2.6.2.2.]

1. Repeatability and level-2 standarddeviations [2.6.2.2.1.]

3. Control chart for probe precision [2.6.2.3.]4. Control chart for bias and long-term variability [2.6.2.4.]5. Run check standard example yourself [2.6.2.5.]6. Dataplot macros [2.6.2.6.]

3. Evaluation of type A uncertainty [2.6.3.]1. Background and data [2.6.3.1.]



1. Database of resistivity measurements [2.6.3.1.1.]2. Measurements on wiring configurations [2.6.3.1.2.]

2. Analysis and interpretation [2.6.3.2.]1. Difference between 2 wiring configurations [2.6.3.2.1.]

3. Run the type A uncertainty analysis usingDataplot [2.6.3.3.]

4. Dataplot macros [2.6.3.4.]4. Evaluation of type B uncertainty and propagation of error [2.6.4.]

7. References [2.7.]

3. Production Process Characterization

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1. Introduction to Production Process Characterization [3.1.]1. What is PPC? [3.1.1.]2. What are PPC Studies Used For? [3.1.2.]3. Terminology/Concepts [3.1.3.]

1. Distribution (Location, Spread and Shape) [3.1.3.1.]2. Process Variability [3.1.3.2.]

1. Controlled/Uncontrolled Variation [3.1.3.2.1.]3. Propagating Error [3.1.3.3.]4. Populations and Sampling [3.1.3.4.]5. Process Models [3.1.3.5.]6. Experiments and Experimental Design [3.1.3.6.]

4. PPC Steps [3.1.4.]

2. Assumptions / Prerequisites [3.2.]1. General Assumptions [3.2.1.]2. Continuous Linear Model [3.2.2.]3. Analysis of Variance Models (ANOVA) [3.2.3.]

1. One-Way ANOVA [3.2.3.1.]1. One-Way Value-Splitting [3.2.3.1.1.]

2. Two-Way Crossed ANOVA [3.2.3.2.]1. Two-way Crossed Value-Splitting Example [3.2.3.2.1.]

3. Two-Way Nested ANOVA [3.2.3.3.]1. Two-Way Nested Value-Splitting Example [3.2.3.3.1.]

4. Discrete Models [3.2.4.]

3. Data Collection for PPC [3.3.]1. Define Goals [3.3.1.]2. Process Modeling [3.3.2.]3. Define Sampling Plan [3.3.3.]

1. Identifying Parameters, Ranges and Resolution [3.3.3.1.]2. Choosing a Sampling Scheme [3.3.3.2.]3. Selecting Sample Sizes [3.3.3.3.]4. Data Storage and Retrieval [3.3.3.4.]5. Assign Roles and Responsibilities [3.3.3.5.]

4. Data Analysis for PPC [3.4.]



1. First Steps [3.4.1.]2. Exploring Relationships [3.4.2.]

1. Response Correlations [3.4.2.1.]2. Exploring Main Effects [3.4.2.2.]3. Exploring First Order Interactions [3.4.2.3.]

3. Building Models [3.4.3.]1. Fitting Polynomial Models [3.4.3.1.]2. Fitting Physical Models [3.4.3.2.]

4. Analyzing Variance Structure [3.4.4.]5. Assessing Process Stability [3.4.5.]6. Assessing Process Capability [3.4.6.]7. Checking Assumptions [3.4.7.]

5. Case Studies [3.5.]1. Furnace Case Study [3.5.1.]

1. Background and Data [3.5.1.1.]2. Initial Analysis of Response Variable [3.5.1.2.]3. Identify Sources of Variation [3.5.1.3.]4. Analysis of Variance [3.5.1.4.]5. Final Conclusions [3.5.1.5.]6. Work This Example Yourself [3.5.1.6.]

2. Machine Screw Case Study [3.5.2.]1. Background and Data [3.5.2.1.]2. Box Plots by Factors [3.5.2.2.]3. Analysis of Variance [3.5.2.3.]4. Throughput [3.5.2.4.]5. Final Conclusions [3.5.2.5.]6. Work This Example Yourself [3.5.2.6.]


4. Process Modeling - Detailed Table of Contents

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1. Introduction to Process Modeling [4.1.]1. What is process modeling? [4.1.1.]2. What terminology do statisticians use to describe process

models? [4.1.2.]3. What are process models used for? [4.1.3.]

1. Estimation [4.1.3.1.]2. Prediction [4.1.3.2.]3. Calibration [4.1.3.3.]4. Optimization [4.1.3.4.]

4. What are some of the different statistical methods for modelbuilding? [4.1.4.]

1. Linear Least Squares Regression [4.1.4.1.]2. Nonlinear Least Squares Regression [4.1.4.2.]3. Weighted Least Squares Regression [4.1.4.3.]4. LOESS (aka LOWESS) [4.1.4.4.]



2. Underlying Assumptions for Process Modeling [4.2.]1. What are the typical underlying assumptions in process

modeling? [4.2.1.]1. The process is a statistical process. [4.2.1.1.]2. The means of the random errors are zero. [4.2.1.2.]3. The random errors have a constant standard

deviation. [4.2.1.3.]4. The random errors follow a normal distribution. [4.2.1.4.]5. The data are randomly sampled from the process. [4.2.1.5.]6. The explanatory variables are observed without

error. [4.2.1.6.]

3. Data Collection for Process Modeling [4.3.]1. What is design of experiments (DOE)? [4.3.1.]2. Why is experimental design important for process

modeling? [4.3.2.]3. What are some general design principles for process

modeling? [4.3.3.]4. I've heard some people refer to "optimal" designs, shouldn't I use

those? [4.3.4.]5. How can I tell if a particular experimental design is good for my

application? [4.3.5.]

4. Data Analysis for Process Modeling [4.4.]1. What are the basic steps for developing an effective process

model? [4.4.1.]2. How do I select a function to describe my process? [4.4.2.]

1. Incorporating Scientific Knowledge into FunctionSelection [4.4.2.1.]

2. Using the Data to Select an Appropriate Function [4.4.2.2.]3. Using Methods that Do Not Require Function

Specification [4.4.2.3.]3. How are estimates of the unknown parameters obtained? [4.4.3.]

1. Least Squares [4.4.3.1.]2. Weighted Least Squares [4.4.3.2.]

4. How can I tell if a model fits my data? [4.4.4.]1. How can I assess the sufficiency of the functional part of the

model? [4.4.4.1.]2. How can I detect non-constant variation across the

data? [4.4.4.2.]3. How can I tell if there was drift in the measurement

process? [4.4.4.3.]4. How can I assess whether the random errors are independent

from one to the next? [4.4.4.4.]5. How can I test whether or not the random errors are

distributed normally? [4.4.4.5.]6. How can I test whether any significant terms are missing or

misspecified in the functional part of the model? [4.4.4.6.]7. How can I test whether all of the terms in the functional part

of the model are necessary? [4.4.4.7.]5. If my current model does not fit the data well, how can I improve

it? [4.4.5.]1. Updating the Function Based on Residual Plots [4.4.5.1.]2. Accounting for Non-Constant Variation Across the



Data [4.4.5.2.]3. Accounting for Errors with a Non-Normal

Distribution [4.4.5.3.]

5. Use and Interpretation of Process Models [4.5.]1. What types of predictions can I make using the model? [4.5.1.]

1. How do I estimate the average response for a particular set ofpredictor variable values? [4.5.1.1.]

2. How can I predict the value and and estimate the uncertaintyof a single response? [4.5.1.2.]

2. How can I use my process model for calibration? [4.5.2.]1. Single-Use Calibration Intervals [4.5.2.1.]

3. How can I optimize my process using the process model? [4.5.3.]

6. Case Studies in Process Modeling [4.6.]1. Load Cell Calibration [4.6.1.]

1. Background & Data [4.6.1.1.]2. Selection of Initial Model [4.6.1.2.]3. Model Fitting - Initial Model [4.6.1.3.]4. Graphical Residual Analysis - Initial Model [4.6.1.4.]5. Interpretation of Numerical Output - Initial Model [4.6.1.5.]6. Model Refinement [4.6.1.6.]7. Model Fitting - Model #2 [4.6.1.7.]8. Graphical Residual Analysis - Model #2 [4.6.1.8.]9. Interpretation of Numerical Output - Model #2 [4.6.1.9.]

10. Use of the Model for Calibration [4.6.1.10.]11. Work This Example Yourself [4.6.1.11.]

2. Alaska Pipeline [4.6.2.]1. Background and Data [4.6.2.1.]2. Check for Batch Effect [4.6.2.2.]3. Initial Linear Fit [4.6.2.3.]4. Transformations to Improve Fit and Equalize

Variances [4.6.2.4.]5. Weighting to Improve Fit [4.6.2.5.]6. Compare the Fits [4.6.2.6.]7. Work This Example Yourself [4.6.2.7.]

3. Ultrasonic Reference Block Study [4.6.3.]1. Background and Data [4.6.3.1.]2. Initial Non-Linear Fit [4.6.3.2.]3. Transformations to Improve Fit [4.6.3.3.]4. Weighting to Improve Fit [4.6.3.4.]5. Compare the Fits [4.6.3.5.]6. Work This Example Yourself [4.6.3.6.]

4. Thermal Expansion of Copper Case Study [4.6.4.]1. Background and Data [4.6.4.1.]2. Rational Function Models [4.6.4.2.]3. Initial Plot of Data [4.6.4.3.]4. Quadratic/Quadratic Rational Function Model [4.6.4.4.]5. Cubic/Cubic Rational Function Model [4.6.4.5.]6. Work This Example Yourself [4.6.4.6.]

7. References For Chapter 4: Process Modeling [4.7.]

8. Some Useful Functions for Process Modeling [4.8.]



1. Univariate Functions [4.8.1.]1. Polynomial Functions [4.8.1.1.]

1. Straight Line [4.8.1.1.1.]2. Quadratic Polynomial [4.8.1.1.2.]3. Cubic Polynomial [4.8.1.1.3.]

2. Rational Functions [4.8.1.2.]1. Constant / Linear Rational Function [4.8.1.2.1.]2. Linear / Linear Rational Function [4.8.1.2.2.]3. Linear / Quadratic Rational Function [4.8.1.2.3.]4. Quadratic / Linear Rational Function [4.8.1.2.4.]5. Quadratic / Quadratic Rational Function [4.8.1.2.5.]6. Cubic / Linear Rational Function [4.8.1.2.6.]7. Cubic / Quadratic Rational Function [4.8.1.2.7.]8. Linear / Cubic Rational Function [4.8.1.2.8.]9. Quadratic / Cubic Rational Function [4.8.1.2.9.]

10. Cubic / Cubic Rational Function [4.8.1.2.10.]11. Determining m and n for Rational Function

Models [4.8.1.2.11.]

5. Process Improvement

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1. Introduction [5.1.]1. What is experimental design? [5.1.1.]2. What are the uses of DOE? [5.1.2.]3. What are the steps of DOE? [5.1.3.]

2. Assumptions [5.2.]1. Is the measurement system capable? [5.2.1.]2. Is the process stable? [5.2.2.]3. Is there a simple model? [5.2.3.]4. Are the model residuals well-behaved? [5.2.4.]

3. Choosing an experimental design [5.3.]1. What are the objectives? [5.3.1.]2. How do you select and scale the process variables? [5.3.2.]3. How do you select an experimental design? [5.3.3.]

1. Completely randomized designs [5.3.3.1.]2. Randomized block designs [5.3.3.2.]

1. Latin square and related designs [5.3.3.2.1.]2. Graeco-Latin square designs [5.3.3.2.2.]3. Hyper-Graeco-Latin square designs [5.3.3.2.3.]

3. Full factorial designs [5.3.3.3.]1. Two-level full factorial designs [5.3.3.3.1.]2. Full factorial example [5.3.3.3.2.]3. Blocking of full factorial designs [5.3.3.3.3.]

4. Fractional factorial designs [5.3.3.4.]1. A 23-1 design (half of a 23) [5.3.3.4.1.]2. Constructing the 23-1 half-fraction design [5.3.3.4.2.]3. Confounding (also called aliasing) [5.3.3.4.3.]



4. Fractional factorial design specifications and designresolution [5.3.3.4.4.]

5. Use of fractional factorial designs [5.3.3.4.5.]6. Screening designs [5.3.3.4.6.]7. Summary tables of useful fractional factorial

designs [5.3.3.4.7.]5. Plackett-Burman designs [5.3.3.5.]6. Response surface designs [5.3.3.6.]

1. Central Composite Designs (CCD) [5.3.3.6.1.]2. Box-Behnken designs [5.3.3.6.2.]3. Comparisons of response surface designs [5.3.3.6.3.]4. Blocking a response surface design [5.3.3.6.4.]

7. Adding centerpoints [5.3.3.7.]8. Improving fractional factorial design resolution [5.3.3.8.]

1. Mirror-Image foldover designs [5.3.3.8.1.]2. Alternative foldover designs [5.3.3.8.2.]

9. Three-level full factorial designs [5.3.3.9.]10. Three-level, mixed-level and fractional factorial

designs [5.3.3.10.]

4. Analysis of DOE data [5.4.]1. What are the steps in a DOE analysis? [5.4.1.]2. How to "look" at DOE data [5.4.2.]3. How to model DOE data [5.4.3.]4. How to test and revise DOE models [5.4.4.]5. How to interpret DOE results [5.4.5.]6. How to confirm DOE results (confirmatory runs) [5.4.6.]7. Examples of DOE's [5.4.7.]

1. Full factorial example [5.4.7.1.]2. Fractional factorial example [5.4.7.2.]3. Response surface model example [5.4.7.3.]

5. Advanced topics [5.5.]1. What if classical designs don't work? [5.5.1.]2. What is a computer-aided design? [5.5.2.]

1. D-Optimal designs [5.5.2.1.]2. Repairing a design [5.5.2.2.]

3. How do you optimize a process? [5.5.3.]1. Single response case [5.5.3.1.]

1. Single response: Path of steepest ascent [5.5.3.1.1.]2. Single response: Confidence region for search

path [5.5.3.1.2.]3. Single response: Choosing the step length [5.5.3.1.3.]4. Single response: Optimization when there is adequate

quadratic fit [5.5.3.1.4.]5. Single response: Effect of sampling error on optimal

solution [5.5.3.1.5.]6. Single response: Optimization subject to experimental

region constraints [5.5.3.1.6.]2. Multiple response case [5.5.3.2.]

1. Multiple responses: Path of steepest ascent [5.5.3.2.1.]2. Multiple responses: The desirability

approach [5.5.3.2.2.]3. Multiple responses: The mathematical programming



approach [5.5.3.2.3.]4. What is a mixture design? [5.5.4.]

1. Mixture screening designs [5.5.4.1.]2. Simplex-lattice designs [5.5.4.2.]3. Simplex-centroid designs [5.5.4.3.]4. Constrained mixture designs [5.5.4.4.]5. Treating mixture and process variables together [5.5.4.5.]

5. How can I account for nested variation (restrictedrandomization)? [5.5.5.]

6. What are Taguchi designs? [5.5.6.]7. What are John's 3/4 fractional factorial designs? [5.5.7.]8. What are small composite designs? [5.5.8.]9. An EDA approach to experimental design [5.5.9.]

1. Ordered data plot [5.5.9.1.]2. DOE scatter plot [5.5.9.2.]3. DOE mean plot [5.5.9.3.]4. Interaction effects matrix plot [5.5.9.4.]5. Block plot [5.5.9.5.]6. DOE Youden plot [5.5.9.6.]7. |Effects| plot [5.5.9.7.]

1. Statistical significance [5.5.9.7.1.]2. Engineering significance [5.5.9.7.2.]3. Numerical significance [5.5.9.7.3.]4. Pattern significance [5.5.9.7.4.]

8. Half-normal probability plot [5.5.9.8.]9. Cumulative residual standard deviation plot [5.5.9.9.]

1. Motivation: What is a Model? [5.5.9.9.1.]2. Motivation: How do we Construct a Goodness-of-fit

Metric for a Model? [5.5.9.9.2.]3. Motivation: How do we Construct a Good

Model? [5.5.9.9.3.]4. Motivation: How do we Know When to Stop Adding

Terms? [5.5.9.9.4.]5. Motivation: What is the Form of the

Model? [5.5.9.9.5.]6. Motivation: What are the Advantages of the

LinearCombinatoric Model? [5.5.9.9.6.]7. Motivation: How do we use the Model to Generate

Predicted Values? [5.5.9.9.7.]8. Motivation: How do we Use the Model Beyond the

Data Domain? [5.5.9.9.8.]9. Motivation: What is the Best Confirmation Point for

Interpolation? [5.5.9.9.9.]10. Motivation: How do we Use the Model for

Interpolation? [5.5.9.9.10.]11. Motivation: How do we Use the Model for

Extrapolation? [5.5.9.9.11.]10. DOE contour plot [5.5.9.10.]

1. How to Interpret: Axes [5.5.9.10.1.]2. How to Interpret: Contour Curves [5.5.9.10.2.]3. How to Interpret: Optimal Response Value [5.5.9.10.3.]4. How to Interpret: Best Corner [5.5.9.10.4.]5. How to Interpret: Steepest Ascent/Descent [5.5.9.10.5.]6. How to Interpret: Optimal Curve [5.5.9.10.6.]



7. How to Interpret: Optimal Setting [5.5.9.10.7.]

6. Case Studies [5.6.]1. Eddy Current Probe Sensitivity Case Study [5.6.1.]

1. Background and Data [5.6.1.1.]2. Initial Plots/Main Effects [5.6.1.2.]3. Interaction Effects [5.6.1.3.]4. Main and Interaction Effects: Block Plots [5.6.1.4.]5. Estimate Main and Interaction Effects [5.6.1.5.]6. Modeling and Prediction Equations [5.6.1.6.]7. Intermediate Conclusions [5.6.1.7.]8. Important Factors and Parsimonious Prediction [5.6.1.8.]9. Validate the Fitted Model [5.6.1.9.]

10. Using the Fitted Model [5.6.1.10.]11. Conclusions and Next Step [5.6.1.11.]12. Work This Example Yourself [5.6.1.12.]

2. Sonoluminescent Light Intensity Case Study [5.6.2.]1. Background and Data [5.6.2.1.]2. Initial Plots/Main Effects [5.6.2.2.]3. Interaction Effects [5.6.2.3.]4. Main and Interaction Effects: Block Plots [5.6.2.4.]5. Important Factors: Youden Plot [5.6.2.5.]6. Important Factors: |Effects| Plot [5.6.2.6.]7. Important Factors: Half-Normal Probability Plot [5.6.2.7.]8. Cumulative Residual Standard Deviation Plot [5.6.2.8.]9. Next Step: DOE Contour Plot [5.6.2.9.]

10. Summary of Conclusions [5.6.2.10.]11. Work This Example Yourself [5.6.2.11.]

7. A Glossary of DOE Terminology [5.7.]


6. Process or Product Monitoring and Control

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1. Introduction [6.1.]1. How did Statistical Quality Control Begin? [6.1.1.]2. What are Process Control Techniques? [6.1.2.]3. What is Process Control? [6.1.3.]4. What to do if the process is "Out of Control"? [6.1.4.]5. What to do if "In Control" but Unacceptable? [6.1.5.]6. What is Process Capability? [6.1.6.]

2. Test Product for Acceptability: Lot Acceptance Sampling [6.2.]1. What is Acceptance Sampling? [6.2.1.]2. What kinds of Lot Acceptance Sampling Plans (LASPs) are

there? [6.2.2.]3. How do you Choose a Single Sampling Plan? [6.2.3.]

1. Choosing a Sampling Plan: MIL Standard 105D [6.2.3.1.]



2. Choosing a Sampling Plan with a given OC Curve [6.2.3.2.]4. What is Double Sampling? [6.2.4.]5. What is Multiple Sampling? [6.2.5.]6. What is a Sequential Sampling Plan? [6.2.6.]7. What is Skip Lot Sampling? [6.2.7.]

3. Univariate and Multivariate Control Charts [6.3.]1. What are Control Charts? [6.3.1.]2. What are Variables Control Charts? [6.3.2.]

1. Shewhart X-bar and R and S Control Charts [6.3.2.1.]2. Individuals Control Charts [6.3.2.2.]3. Cusum Control Charts [6.3.2.3.]

1. Cusum Average Run Length [6.3.2.3.1.]4. EWMA Control Charts [6.3.2.4.]

3. What are Attributes Control Charts? [6.3.3.]1. Counts Control Charts [6.3.3.1.]2. Proportions Control Charts [6.3.3.2.]

4. What are Multivariate Control Charts? [6.3.4.]1. Hotelling Control Charts [6.3.4.1.]2. Principal Components Control Charts [6.3.4.2.]3. Multivariate EWMA Charts [6.3.4.3.]

4. Introduction to Time Series Analysis [6.4.]1. Definitions, Applications and Techniques [6.4.1.]2. What are Moving Average or Smoothing Techniques? [6.4.2.]

1. Single Moving Average [6.4.2.1.]2. Centered Moving Average [6.4.2.2.]

3. What is Exponential Smoothing? [6.4.3.]1. Single Exponential Smoothing [6.4.3.1.]2. Forecasting with Single Exponential Smoothing [6.4.3.2.]3. Double Exponential Smoothing [6.4.3.3.]4. Forecasting with Double Exponential

Smoothing(LASP) [6.4.3.4.]5. Triple Exponential Smoothing [6.4.3.5.]6. Example of Triple Exponential Smoothing [6.4.3.6.]7. Exponential Smoothing Summary [6.4.3.7.]

4. Univariate Time Series Models [6.4.4.]1. Sample Data Sets [6.4.4.1.]

1. Data Set of Monthly CO2 Concentrations [6.4.4.1.1.]2. Data Set of Southern Oscillations [6.4.4.1.2.]

2. Stationarity [6.4.4.2.]3. Seasonality [6.4.4.3.]

1. Seasonal Subseries Plot [6.4.4.3.1.]4. Common Approaches to Univariate Time Series [6.4.4.4.]5. Box-Jenkins Models [6.4.4.5.]6. Box-Jenkins Model Identification [6.4.4.6.]

1. Model Identification for Southern OscillationsData [6.4.4.6.1.]

2. Model Identification for the CO2 ConcentrationsData [6.4.4.6.2.]

3. Partial Autocorrelation Plot [6.4.4.6.3.]7. Box-Jenkins Model Estimation [6.4.4.7.]8. Box-Jenkins Model Diagnostics [6.4.4.8.]



1. Box-Ljung Test [6.4.4.8.1.]9. Example of Univariate Box-Jenkins Analysis [6.4.4.9.]

10. Box-Jenkins Analysis on Seasonal Data [6.4.4.10.]5. Multivariate Time Series Models [6.4.5.]

1. Example of Multivariate Time Series Analysis [6.4.5.1.]

5. Tutorials [6.5.]1. What do we mean by "Normal" data? [6.5.1.]2. What do we do when data are "Non-normal"? [6.5.2.]3. Elements of Matrix Algebra [6.5.3.]

1. Numerical Examples [6.5.3.1.]2. Determinant and Eigenstructure [6.5.3.2.]

4. Elements of Multivariate Analysis [6.5.4.]1. Mean Vector and Covariance Matrix [6.5.4.1.]2. The Multivariate Normal Distribution [6.5.4.2.]3. Hotelling's T squared [6.5.4.3.]

1. T2 Chart for Subgroup Averages -- Phase I [6.5.4.3.1.]2. T2 Chart for Subgroup Averages -- Phase II [6.5.4.3.2.]3. Chart for Individual Observations -- Phase I [6.5.4.3.3.]4. Chart for Individual Observations -- Phase

II [6.5.4.3.4.]5. Charts for Controlling Multivariate

Variability [6.5.4.3.5.]6. Constructing Multivariate Charts [6.5.4.3.6.]

5. Principal Components [6.5.5.]1. Properties of Principal Components [6.5.5.1.]2. Numerical Example [6.5.5.2.]

6. Case Studies in Process Monitoring [6.6.]1. Lithography Process [6.6.1.]

1. Background and Data [6.6.1.1.]2. Graphical Representation of the Data [6.6.1.2.]3. Subgroup Analysis [6.6.1.3.]4. Shewhart Control Chart [6.6.1.4.]5. Work This Example Yourself [6.6.1.5.]

2. Aerosol Particle Size [6.6.2.]1. Background and Data [6.6.2.1.]2. Model Identification [6.6.2.2.]3. Model Estimation [6.6.2.3.]4. Model Validation [6.6.2.4.]5. Work This Example Yourself [6.6.2.5.]


7. Product and Process Comparisons

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1. Introduction [7.1.]1. What is the scope? [7.1.1.]2. What assumptions are typically made? [7.1.2.]



3. What are statistical tests? [7.1.3.]1. Critical values and p values [7.1.3.1.]

4. What are confidence intervals? [7.1.4.]5. What is the relationship between a test and a confidence

interval? [7.1.5.]6. What are outliers in the data? [7.1.6.]7. What are trends in sequential process or product data? [7.1.7.]

2. Comparisons based on data from one process [7.2.]1. Do the observations come from a particular distribution? [7.2.1.]

1. Chi-square goodness-of-fit test [7.2.1.1.]2. Kolmogorov- Smirnov test [7.2.1.2.]3. Anderson-Darling and Shapiro-Wilk tests [7.2.1.3.]

2. Are the data consistent with the assumed process mean? [7.2.2.]1. Confidence interval approach [7.2.2.1.]2. Sample sizes required [7.2.2.2.]

3. Are the data consistent with a nominal standard deviation? [7.2.3.]1. Confidence interval approach [7.2.3.1.]2. Sample sizes required [7.2.3.2.]

4. Does the proportion of defectives meet requirements? [7.2.4.]1. Confidence intervals [7.2.4.1.]2. Sample sizes required [7.2.4.2.]

5. Does the defect density meet requirements? [7.2.5.]6. What intervals contain a fixed percentage of the population

values? [7.2.6.]1. Approximate intervals that contain most of the population

values [7.2.6.1.]2. Percentiles [7.2.6.2.]3. Tolerance intervals for a normal distribution [7.2.6.3.]4. Tolerance intervals based on the largest and smallest

observations [7.2.6.4.]

3. Comparisons based on data from two processes [7.3.]1. Do two processes have the same mean? [7.3.1.]

1. Analysis of paired observations [7.3.1.1.]2. Confidence intervals for differences between means [7.3.1.2.]

2. Do two processes have the same standard deviation? [7.3.2.]3. How can we determine whether two processes produce the same

proportion of defectives? [7.3.3.]4. Assuming the observations are failure times, are the failure rates (or

Mean Times To Failure) for two distributions the same? [7.3.4.]5. Do two arbitrary processes have the same central tendency? [7.3.5.]

4. Comparisons based on data from more than two processes [7.4.]1. How can we compare several populations with unknown

distributions (the Kruskal-Wallis test)? [7.4.1.]2. Assuming the observations are normal, do the processes have the

same variance? [7.4.2.]3. Are the means equal? [7.4.3.]

1. 1-Way ANOVA overview [7.4.3.1.]2. The 1-way ANOVA model and assumptions [7.4.3.2.]3. The ANOVA table and tests of hypotheses about

means [7.4.3.3.]4. 1-Way ANOVA calculations [7.4.3.4.]



5. Confidence intervals for the difference of treatmentmeans [7.4.3.5.]

6. Assessing the response from any factor combination [7.4.3.6.]7. The two-way ANOVA [7.4.3.7.]8. Models and calculations for the two-way ANOVA [7.4.3.8.]

4. What are variance components? [7.4.4.]5. How can we compare the results of classifying according to several

categories? [7.4.5.]6. Do all the processes have the same proportion of defects? [7.4.6.]7. How can we make multiple comparisons? [7.4.7.]

1. Tukey's method [7.4.7.1.]2. Scheffe's method [7.4.7.2.]3. Bonferroni's method [7.4.7.3.]4. Comparing multiple proportions: The Marascuillo

procedure [7.4.7.4.]


8. Assessing Product Reliability

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1. Introduction [8.1.]1. Why is the assessment and control of product reliability

important? [8.1.1.]1. Quality versus reliability [8.1.1.1.]2. Competitive driving factors [8.1.1.2.]3. Safety and health considerations [8.1.1.3.]

2. What are the basic terms and models used for reliabilityevaluation? [8.1.2.]

1. Repairable systems, non-repairable populations and lifetimedistribution models [8.1.2.1.]

2. Reliability or survival function [8.1.2.2.]3. Failure (or hazard) rate [8.1.2.3.]4. "Bathtub" curve [8.1.2.4.]5. Repair rate or ROCOF [8.1.2.5.]

3. What are some common difficulties with reliability data and how arethey overcome? [8.1.3.]

1. Censoring [8.1.3.1.]2. Lack of failures [8.1.3.2.]

4. What is "physical acceleration" and how do we model it? [8.1.4.]5. What are some common acceleration models? [8.1.5.]

1. Arrhenius [8.1.5.1.]2. Eyring [8.1.5.2.]3. Other models [8.1.5.3.]

6. What are the basic lifetime distribution models used for non-repairable populations? [8.1.6.]

1. Exponential [8.1.6.1.]2. Weibull [8.1.6.2.]3. Extreme value distributions [8.1.6.3.]



4. Lognormal [8.1.6.4.]5. Gamma [8.1.6.5.]6. Fatigue life (Birnbaum-Saunders) [8.1.6.6.]7. Proportional hazards model [8.1.6.7.]

7. What are some basic repair rate models used for repairablesystems? [8.1.7.]

1. Homogeneous Poisson Process (HPP) [8.1.7.1.]2. Non-Homogeneous Poisson Process (NHPP) - power

law [8.1.7.2.]3. Exponential law [8.1.7.3.]

8. How can you evaluate reliability from the "bottom-up" (componentfailure mode to system failure rate)? [8.1.8.]

1. Competing risk model [8.1.8.1.]2. Series model [8.1.8.2.]3. Parallel or redundant model [8.1.8.3.]4. R out of N model [8.1.8.4.]5. Standby model [8.1.8.5.]6. Complex systems [8.1.8.6.]

9. How can you model reliability growth? [8.1.9.]1. NHPP power law [8.1.9.1.]2. Duane plots [8.1.9.2.]3. NHPP exponential law [8.1.9.3.]

10. How can Bayesian methodology be used for reliabilityevaluation? [8.1.10.]

2. Assumptions/Prerequisites [8.2.]1. How do you choose an appropriate life distribution model? [8.2.1.]

1. Based on failure mode [8.2.1.1.]2. Extreme value argument [8.2.1.2.]3. Multiplicative degradation argument [8.2.1.3.]4. Fatigue life (Birnbaum-Saunders) model [8.2.1.4.]5. Empirical model fitting - distribution free (Kaplan-Meier)

approach [8.2.1.5.]2. How do you plot reliability data? [8.2.2.]

1. Probability plotting [8.2.2.1.]2. Hazard and cum hazard plotting [8.2.2.2.]3. Trend and growth plotting (Duane plots) [8.2.2.3.]

3. How can you test reliability model assumptions? [8.2.3.]1. Visual tests [8.2.3.1.]2. Goodness of fit tests [8.2.3.2.]3. Likelihood ratio tests [8.2.3.3.]4. Trend tests [8.2.3.4.]

4. How do you choose an appropriate physical accelerationmodel? [8.2.4.]

5. What models and assumptions are typically made when Bayesianmethods are used for reliability evaluation? [8.2.5.]

3. Reliability Data Collection [8.3.]1. How do you plan a reliability assessment test? [8.3.1.]

1. Exponential life distribution (or HPP model) tests [8.3.1.1.]2. Lognormal or Weibull tests [8.3.1.2.]3. Reliability growth (Duane model) [8.3.1.3.]4. Accelerated life tests [8.3.1.4.]5. Bayesian gamma prior model [8.3.1.5.]



4. Reliability Data Analysis [8.4.]1. How do you estimate life distribution parameters from censored

data? [8.4.1.]1. Graphical estimation [8.4.1.1.]2. Maximum likelihood estimation [8.4.1.2.]3. A Weibull maximum likelihood estimation example [8.4.1.3.]

2. How do you fit an acceleration model? [8.4.2.]1. Graphical estimation [8.4.2.1.]2. Maximum likelihood [8.4.2.2.]3. Fitting models using degradation data instead of

failures [8.4.2.3.]3. How do you project reliability at use conditions? [8.4.3.]4. How do you compare reliability between two or more

populations? [8.4.4.]5. How do you fit system repair rate models? [8.4.5.]

1. Constant repair rate (HPP/exponential) model [8.4.5.1.]2. Power law (Duane) model [8.4.5.2.]3. Exponential law model [8.4.5.3.]

6. How do you estimate reliability using the Bayesian gamma priormodel? [8.4.6.]

7. References For Chapter 8: Assessing Product Reliability [8.4.7.]


http://www.itl.nist.gov/div898/handbook/eda/eda.htm[4/17/2013 7:03:18 PM]


This chapter presents the assumptions, principles, and techniques necessaryto gain insight into data via EDA--exploratory data analysis.

1. EDA Introduction

1. What is EDA? 2. EDA vs Classical &

Bayesian3. EDA vs Summary4. EDA Goals5. The Role of Graphics6. An EDA/Graphics Example 7. General Problem Categories

2. EDA Assumptions

1. Underlying Assumptions2. Importance3. Techniques for Testing

Assumptions4. Interpretation of 4-Plot5. Consequences

3. EDA Techniques

1. Introduction2. Analysis Questions3. Graphical Techniques:

Alphabetical4. Graphical Techniques: By

Problem Category5. Quantitative Techniques6. Probability Distributions

4. EDA Case Studies

1. Introduction2. By Problem Category

Detailed Chapter Table of ContentsReferencesDataplot Commands for EDA Techniques

1.1. EDA Introduction

http://www.itl.nist.gov/div898/handbook/eda/section1/eda1.htm[4/17/2013 7:03:18 PM]


1.1. EDA Introduction

Summary What is exploratory data analysis? How did it begin? Howand where did it originate? How is it differentiated from otherdata analysis approaches, such as classical and Bayesian? IsEDA the same as statistical graphics? What role doesstatistical graphics play in EDA? Is statistical graphicsidentical to EDA?

These questions and related questions are dealt with in thissection. This section answers these questions and provides thenecessary frame of reference for EDA assumptions, principles,and techniques.

Table ofContentsfor Section1

1. What is EDA?2. EDA versus Classical and Bayesian

1. Models2. Focus3. Techniques4. Rigor5. Data Treatment6. Assumptions

3. EDA vs Summary4. EDA Goals5. The Role of Graphics6. An EDA/Graphics Example7. General Problem Categories

1.1.1. What is EDA?


1. Exploratory Data Analysis1.1. EDA Introduction

1.1.1. What is EDA?

Approach Exploratory Data Analysis (EDA) is an approach/philosophyfor data analysis that employs a variety of techniques (mostlygraphical) to

1. maximize insight into a data set;2. uncover underlying structure;3. extract important variables;4. detect outliers and anomalies;5. test underlying assumptions;6. develop parsimonious models; and7. determine optimal factor settings.

Focus The EDA approach is precisely that--an approach--not a set oftechniques, but an attitude/philosophy about how a dataanalysis should be carried out.

Philosophy EDA is not identical to statistical graphics although the twoterms are used almost interchangeably. Statistical graphics is acollection of techniques--all graphically based and allfocusing on one data characterization aspect. EDAencompasses a larger venue; EDA is an approach to dataanalysis that postpones the usual assumptions about what kindof model the data follow with the more direct approach ofallowing the data itself to reveal its underlying structure andmodel. EDA is not a mere collection of techniques; EDA is aphilosophy as to how we dissect a data set; what we look for;how we look; and how we interpret. It is true that EDAheavily uses the collection of techniques that we call"statistical graphics", but it is not identical to statisticalgraphics per se.

History The seminal work in EDA is Exploratory Data Analysis,Tukey, (1977). Over the years it has benefitted from othernoteworthy publications such as Data Analysis andRegression, Mosteller and Tukey (1977), Interactive DataAnalysis, Hoaglin (1977), The ABC's of EDA, Velleman andHoaglin (1981) and has gained a large following as "the" wayto analyze a data set.

Techniques Most EDA techniques are graphical in nature with a fewquantitative techniques. The reason for the heavy reliance on

1.1.1. What is EDA?


graphics is that by its very nature the main role of EDA is toopen-mindedly explore, and graphics gives the analystsunparalleled power to do so, enticing the data to reveal itsstructural secrets, and being always ready to gain some new,often unsuspected, insight into the data. In combination withthe natural pattern-recognition capabilities that we all possess,graphics provides, of course, unparalleled power to carry thisout.

The particular graphical techniques employed in EDA areoften quite simple, consisting of various techniques of:

1. Plotting the raw data (such as data traces, histograms,bihistograms, probability plots, lag plots, block plots,and Youden plots.

2. Plotting simple statistics such as mean plots, standarddeviation plots, box plots, and main effects plots of theraw data.

3. Positioning such plots so as to maximize our naturalpattern-recognition abilities, such as using multipleplots per page.

1.1.2. How Does Exploratory Data Analysis differ from Classical Data Analysis?



1.1.2. How Does Exploratory Data Analysisdiffer from Classical Data Analysis?

DataAnalysisApproaches

EDA is a data analysis approach. What other data analysisapproaches exist and how does EDA differ from these otherapproaches? Three popular data analysis approaches are:

1. Classical2. Exploratory (EDA)3. Bayesian

Paradigmsfor AnalysisTechniques

These three approaches are similar in that they all start witha general science/engineering problem and all yieldscience/engineering conclusions. The difference is thesequence and focus of the intermediate steps.

For classical analysis, the sequence is

Problem => Data => Model => Analysis =>Conclusions

For EDA, the sequence is

Problem => Data => Analysis => Model =>Conclusions

For Bayesian, the sequence is

Problem => Data => Model => Prior Distribution =>Analysis => Conclusions

Method ofdealing withunderlyingmodel forthe datadistinguishesthe 3approaches

Thus for classical analysis, the data collection is followed bythe imposition of a model (normality, linearity, etc.) and theanalysis, estimation, and testing that follows are focused onthe parameters of that model. For EDA, the data collection isnot followed by a model imposition; rather it is followedimmediately by analysis with a goal of inferring what modelwould be appropriate. Finally, for a Bayesian analysis, theanalyst attempts to incorporate scientific/engineeringknowledge/expertise into the analysis by imposing a data-independent distribution on the parameters of the selectedmodel; the analysis thus consists of formally combining boththe prior distribution on the parameters and the collected

1.1.2. How Does Exploratory Data Analysis differ from Classical Data Analysis?


data to jointly make inferences and/or test assumptions aboutthe model parameters.

In the real world, data analysts freely mix elements of all ofthe above three approaches (and other approaches). Theabove distinctions were made to emphasize the majordifferences among the three approaches.

Furtherdiscussion ofthedistinctionbetween theclassical andEDAapproaches

Focusing on EDA versus classical, these two approachesdiffer as follows:

1. Models2. Focus3. Techniques4. Rigor5. Data Treatment6. Assumptions

1.1.2.1. Model


1. Exploratory Data Analysis1.1. EDA Introduction1.1.2. How Does Exploratory Data Analysis differ from Classical Data Analysis?

1.1.2.1. Model

Classical The classical approach imposes models (both deterministicand probabilistic) on the data. Deterministic models include,for example, regression models and analysis of variance(ANOVA) models. The most common probabilistic modelassumes that the errors about the deterministic model arenormally distributed--this assumption affects the validity ofthe ANOVA F tests.

Exploratory The Exploratory Data Analysis approach does not imposedeterministic or probabilistic models on the data. On thecontrary, the EDA approach allows the data to suggestadmissible models that best fit the data.

1.1.2.2. Focus



1.1.2.2. Focus

Classical The two approaches differ substantially in focus. For classicalanalysis, the focus is on the model--estimating parameters ofthe model and generating predicted values from the model.

Exploratory For exploratory data analysis, the focus is on the data--itsstructure, outliers, and models suggested by the data.

1.1.2.3. Techniques



1.1.2.3. Techniques

Classical Classical techniques are generally quantitative in nature. Theyinclude ANOVA, t tests, chi-squared tests, and F tests.

Exploratory EDA techniques are generally graphical. They include scatterplots, character plots, box plots, histograms, bihistograms,probability plots, residual plots, and mean plots.

1.1.2.4. Rigor



1.1.2.4. Rigor

Classical Classical techniques serve as the probabilistic foundation ofscience and engineering; the most important characteristic ofclassical techniques is that they are rigorous, formal, and"objective".

Exploratory EDA techniques do not share in that rigor or formality. EDAtechniques make up for that lack of rigor by being verysuggestive, indicative, and insightful about what theappropriate model should be.

EDA techniques are subjective and depend on interpretationwhich may differ from analyst to analyst, althoughexperienced analysts commonly arrive at identicalconclusions.

1.1.2.5. Data Treatment



1.1.2.5. Data Treatment

Classical Classical estimation techniques have the characteristic oftaking all of the data and mapping the data into a fewnumbers ("estimates"). This is both a virtue and a vice. Thevirtue is that these few numbers focus on importantcharacteristics (location, variation, etc.) of the population. Thevice is that concentrating on these few characteristics canfilter out other characteristics (skewness, tail length,autocorrelation, etc.) of the same population. In this sensethere is a loss of information due to this "filtering" process.

Exploratory The EDA approach, on the other hand, often makes use of(and shows) all of the available data. In this sense there is nocorresponding loss of information.

1.1.2.6. Assumptions



1.1.2.6. Assumptions

Classical The "good news" of the classical approach is that tests basedon classical techniques are usually very sensitive--that is, if atrue shift in location, say, has occurred, such tests frequentlyhave the power to detect such a shift and to conclude thatsuch a shift is "statistically significant". The "bad news" isthat classical tests depend on underlying assumptions (e.g.,normality), and hence the validity of the test conclusionsbecomes dependent on the validity of the underlyingassumptions. Worse yet, the exact underlying assumptionsmay be unknown to the analyst, or if known, untested. Thusthe validity of the scientific conclusions becomes intrinsicallylinked to the validity of the underlying assumptions. Inpractice, if such assumptions are unknown or untested, thevalidity of the scientific conclusions becomes suspect.

Exploratory Many EDA techniques make little or no assumptions--theypresent and show the data--all of the data--as is, with fewerencumbering assumptions.

1.1.3. How Does Exploratory Data Analysis Differ from Summary Analysis?



1.1.3. How Does Exploratory Data AnalysisDiffer from Summary Analysis?

Summary A summary analysis is simply a numeric reduction of ahistorical data set. It is quite passive. Its focus is in the past.Quite commonly, its purpose is to simply arrive at a few keystatistics (for example, mean and standard deviation) whichmay then either replace the data set or be added to the dataset in the form of a summary table.

Exploratory In contrast, EDA has as its broadest goal the desire to gaininsight into the engineering/scientific process behind the data.Whereas summary statistics are passive and historical, EDAis active and futuristic. In an attempt to "understand" theprocess and improve it in the future, EDA uses the data as a"window" to peer into the heart of the process that generatedthe data. There is an archival role in the research andmanufacturing world for summary statistics, but there is anenormously larger role for the EDA approach.

1.1.4. What are the EDA Goals?



1.1.4. What are the EDA Goals?

PrimaryandSecondaryGoals

The primary goal of EDA is to maximize the analyst's insightinto a data set and into the underlying structure of a data set,while providing all of the specific items that an analyst wouldwant to extract from a data set, such as:

1. a good-fitting, parsimonious model2. a list of outliers3. a sense of robustness of conclusions4. estimates for parameters5. uncertainties for those estimates6. a ranked list of important factors7. conclusions as to whether individual factors are

statistically significant8. optimal settings

Insightinto theData

Insight implies detecting and uncovering underlying structurein the data. Such underlying structure may not be encapsulatedin the list of items above; such items serve as the specifictargets of an analysis, but the real insight and "feel" for a dataset comes as the analyst judiciously probes and explores thevarious subtleties of the data. The "feel" for the data comesalmost exclusively from the application of various graphicaltechniques, the collection of which serves as the window intothe essence of the data. Graphics are irreplaceable--there areno quantitative analogues that will give the same insight aswell-chosen graphics.

To get a "feel" for the data, it is not enough for the analyst toknow what is in the data; the analyst also must know what isnot in the data, and the only way to do that is to draw on ourown human pattern-recognition and comparative abilities inthe context of a series of judicious graphical techniquesapplied to the data.

1.1.5. The Role of Graphics




Quantitative/Graphical

Statistics and data analysis procedures can broadly be splitinto two parts:

quantitativegraphical

Quantitative Quantitative techniques are the set of statistical proceduresthat yield numeric or tabular output. Examples ofquantitative techniques include:

hypothesis testinganalysis of variancepoint estimates and confidence intervalsleast squares regression

These and similar techniques are all valuable and aremainstream in terms of classical analysis.

Graphical On the other hand, there is a large collection of statisticaltools that we generally refer to as graphical techniques.These include:

scatter plotshistogramsprobability plotsresidual plotsbox plotsblock plots

EDAApproachReliesHeavily onGraphicalTechniques

The EDA approach relies heavily on these and similargraphical techniques. Graphical procedures are not just toolsthat we could use in an EDA context, they are tools that wemust use. Such graphical tools are the shortest path togaining insight into a data set in terms of

testing assumptionsmodel selectionmodel validationestimator selectionrelationship identificationfactor effect determination



outlier detection

If one is not using statistical graphics, then one is forfeitinginsight into one or more aspects of the underlying structureof the data.

1.1.6. An EDA/Graphics Example




AnscombeExample

A simple, classic (Anscombe) example of the central rolethat graphics play in terms of providing insight into a dataset starts with the following data set:

Data X Y10.00 8.04 8.00 6.9513.00 7.58 9.00 8.8111.00 8.3314.00 9.96 6.00 7.24 4.00 4.2612.00 10.84 7.00 4.82 5.00 5.68

SummaryStatistics

If the goal of the analysis is to compute summary statisticsplus determine the best linear fit for Y as a function of X,the results might be given as:

N = 11Mean of X = 9.0Mean of Y = 7.5Intercept = 3Slope = 0.5Residual standard deviation = 1.237Correlation = 0.816

The above quantitative analysis, although valuable, gives usonly limited insight into the data.

Scatter Plot In contrast, the following simple scatter plot of the data



suggests the following:

1. The data set "behaves like" a linear curve with somescatter;

2. there is no justification for a more complicated model(e.g., quadratic);

3. there are no outliers;4. the vertical spread of the data appears to be of equal

height irrespective of the X-value; this indicates thatthe data are equally-precise throughout and so a"regular" (that is, equi-weighted) fit is appropriate.

ThreeAdditionalData Sets

This kind of characterization for the data serves as the corefor getting insight/feel for the data. Such insight/feel doesnot come from the quantitative statistics; on the contrary,calculations of quantitative statistics such as intercept andslope should be subsequent to the characterization and willmake sense only if the characterization is true. To illustratethe loss of information that results when the graphicsinsight step is skipped, consider the following three datasets [Anscombe data sets 2, 3, and 4]:

X2 Y2 X3 Y3 X4 Y410.00 9.14 10.00 7.46 8.00 6.58 8.00 8.14 8.00 6.77 8.00 5.7613.00 8.74 13.00 12.74 8.00 7.71 9.00 8.77 9.00 7.11 8.00 8.8411.00 9.26 11.00 7.81 8.00 8.4714.00 8.10 14.00 8.84 8.00 7.04 6.00 6.13 6.00 6.08 8.00 5.25 4.00 3.10 4.00 5.39 19.00 12.5012.00 9.13 12.00 8.15 8.00 5.56 7.00 7.26 7.00 6.42 8.00 7.91 5.00 4.74 5.00 5.73 8.00 6.89

QuantitativeStatistics forData Set 2

A quantitative analysis on data set 2 yields

N = 11Mean of X = 9.0Mean of Y = 7.5Intercept = 3



Slope = 0.5Residual standard deviation = 1.237Correlation = 0.816

which is identical to the analysis for data set 1. One mightnaively assume that the two data sets are "equivalent" sincethat is what the statistics tell us; but what do the statisticsnot tell us?

QuantitativeStatistics forData Sets 3and 4

Remarkably, a quantitative analysis on data sets 3 and 4also yields

N = 11Mean of X = 9.0Mean of Y = 7.5Intercept = 3Slope = 0.5Residual standard deviation = 1.236Correlation = 0.816 (0.817 for data set 4)

which implies that in some quantitative sense, all four ofthe data sets are "equivalent". In fact, the four data sets arefar from "equivalent" and a scatter plot of each data set,which would be step 1 of any EDA approach, would tell usthat immediately.

Scatter Plots

Interpretationof ScatterPlots

Conclusions from the scatter plots are:

1. data set 1 is clearly linear with some scatter.2. data set 2 is clearly quadratic.3. data set 3 clearly has an outlier.4. data set 4 is obviously the victim of a poor

experimental design with a single point far removedfrom the bulk of the data "wagging the dog".

Importance These points are exactly the substance that provide and



ofExploratoryAnalysis

define "insight" and "feel" for a data set. They are the goalsand the fruits of an open exploratory data analysis (EDA)approach to the data. Quantitative statistics are not wrongper se, but they are incomplete. They are incompletebecause they are numeric summaries which in thesummarization operation do a good job of focusing on aparticular aspect of the data (e.g., location, intercept, slope,degree of relatedness, etc.) by judiciously reducing the datato a few numbers. Doing so also filters the data, necessarilyomitting and screening out other sometimes crucialinformation in the focusing operation. Quantitative statisticsfocus but also filter; and filtering is exactly what makes thequantitative approach incomplete at best and misleading atworst.

The estimated intercepts (= 3) and slopes (= 0.5) for datasets 2, 3, and 4 are misleading because the estimation isdone in the context of an assumed linear model and thatlinearity assumption is the fatal flaw in this analysis.

The EDA approach of deliberately postponing the modelselection until further along in the analysis has manyrewards, not the least of which is the ultimate convergenceto a much-improved model and the formulation of validand supportable scientific and engineering conclusions.

1.1.7. General Problem Categories




ProblemClassification

The following table is a convenient way to classify EDAproblems.

Univariateand Control UNIVARIATE

Data:

A single column ofnumbers, Y.

Model:

y = constant + error

Output:

1. A number (theestimated constant inthe model).

2. An estimate ofuncertainty for theconstant.

3. An estimate of thedistribution for theerror.

Techniques:

4-PlotProbability PlotPPCC Plot

CONTROL

Data:

A single column ofnumbers, Y.

Model:

y = constant + error

Output:

A "yes" or "no" to thequestion "Is thesystem out of control?".

Techniques:

Control Charts

ComparativeandScreening

COMPARATIVE

Data:

A single responsevariable and kindependent variables(Y, X1, X2, ... , Xk),primary focus is on

SCREENING

Data:

A single responsevariable and kindependent variables(Y, X1, X2, ... , Xk).



one (the primaryfactor) of theseindependent variables.

Model:

y = f(x1, x2, ..., xk) +error

Output:

A "yes" or "no" to thequestion "Is theprimary factorsignificant?".

Techniques:

Block PlotScatter PlotBox Plot

Model:

y = f(x1, x2, ..., xk) +error

Output:

1. A ranked list (frommost important toleast important) offactors.

2. Best settings for thefactors.

3. A goodmodel/predictionequation relating Y tothe factors.

Techniques:

Block PlotProbability PlotBihistogram

OptimizationandRegression

OPTIMIZATION

Data:

A single responsevariable and kindependent variables(Y, X1, X2, ... , Xk).

Model:

y = f(x1, x2, ..., xk) +error

Output:

Best settings for thefactor variables.

Techniques:

Block PlotLeast Squares FittingContour Plot

REGRESSION

Data:

A single responsevariable and kindependent variables(Y, X1, X2, ... , Xk).The independentvariables can becontinuous.

Model:

y = f(x1, x2, ..., xk) +error

Output:

A goodmodel/predictionequation relating Y tothe factors.

Techniques:

Least Squares FittingScatter Plot



6-Plot

Time SeriesandMultivariate

TIME SERIES

Data:

A column oftime dependentnumbers, Y. Inaddition, time isan indpendentvariable. Thetime variablecan be eitherexplicit orimplied. If thedata are notequi-spaced, thetime variableshould beexplicitlyprovided.

Model:

yt = f(t) + error The model canbe either a timedomain based orfrequencydomain based.

Output:

A goodmodel/predictionequation relatingY to previousvalues of Y.

Techniques:

AutocorrelationPlotSpectrumComplexDemodulationAmplitude PlotComplexDemodulationPhase PlotARIMA Models

MULTIVARIATE

Data:

k factor variables (X1, X2, ..., Xk).

Model:

The model is not explicit.

Output:

Identify underlyingcorrelation structure in thedata.

Techniques:

Star PlotScatter Plot MatrixConditioning PlotProfile PlotPrincipal ComponentsClusteringDiscrimination/Classification

Note that multivarate analysis isonly covered lightly in thisHandbook.

1.2. EDA Assumptions



1.2. EDA Assumptions

Summary The gamut of scientific and engineering experimentation isvirtually limitless. In this sea of diversity is there any commonbasis that allows the analyst to systematically and validlyarrive at supportable, repeatable research conclusions?

Fortunately, there is such a basis and it is rooted in the factthat every measurement process, however complicated, hascertain underlying assumptions. This section deals with whatthose assumptions are, why they are important, how to goabout testing them, and what the consequences are if theassumptions do not hold.

Table ofContentsfor Section2

1. Underlying Assumptions2. Importance3. Testing Assumptions4. Importance of Plots5. Consequences

1.2.1. Underlying Assumptions


1. Exploratory Data Analysis1.2. EDA Assumptions


AssumptionsUnderlying aMeasurementProcess

There are four assumptions that typically underlie allmeasurement processes; namely, that the data from theprocess at hand "behave like":

1. random drawings;2. from a fixed distribution;3. with the distribution having fixed location; and4. with the distribution having fixed variation.

Univariate orSingleResponseVariable

The "fixed location" referred to in item 3 above differs fordifferent problem types. The simplest problem type isunivariate; that is, a single variable. For the univariateproblem, the general model

response = deterministic component + randomcomponent

becomes

response = constant + error

AssumptionsforUnivariateModel

For this case, the "fixed location" is simply the unknownconstant. We can thus imagine the process at hand to beoperating under constant conditions that produce a singlecolumn of data with the properties that

the data are uncorrelated with one another;the random component has a fixed distribution;the deterministic component consists of only aconstant; andthe random component has fixed variation.

Extrapolationto a Functionof ManyVariables

The universal power and importance of the univariate modelis that it can easily be extended to the more general casewhere the deterministic component is not just a constant,but is in fact a function of many variables, and theengineering objective is to characterize and model thefunction.

Residuals The key point is that regardless of how many factors there



Will BehaveAccording toUnivariateAssumptions

are, and regardless of how complicated the function is, ifthe engineer succeeds in choosing a good model, then thedifferences (residuals) between the raw response data andthe predicted values from the fitted model shouldthemselves behave like a univariate process. Furthermore,the residuals from this univariate process fit will behavelike:

random drawings;from a fixed distribution;with fixed location (namely, 0 in this case); andwith fixed variation.

Validation ofModel

Thus if the residuals from the fitted model do in fact behavelike the ideal, then testing of underlying assumptionsbecomes a tool for the validation and quality of fit of thechosen model. On the other hand, if the residuals from thechosen fitted model violate one or more of the aboveunivariate assumptions, then the chosen fitted model isinadequate and an opportunity exists for arriving at animproved model.

1.2.2. Importance



1.2.2. Importance

PredictabilityandStatisticalControl

Predictability is an all-important goal in science andengineering. If the four underlying assumptions hold, thenwe have achieved probabilistic predictability--the ability tomake probability statements not only about the process inthe past, but also about the process in the future. In short,such processes are said to be "in statistical control".

Validity ofEngineeringConclusions

Moreover, if the four assumptions are valid, then theprocess is amenable to the generation of valid scientific andengineering conclusions. If the four assumptions are notvalid, then the process is drifting (with respect to location,variation, or distribution), unpredictable, and out of control.A simple characterization of such processes by a locationestimate, a variation estimate, or a distribution "estimate"inevitably leads to engineering conclusions that are notvalid, are not supportable (scientifically or legally), andwhich are not repeatable in the laboratory.

1.2.3. Techniques for Testing Assumptions




TestingUnderlyingAssumptionsHelps Assure theValidity ofScientific andEngineeringConclusions

Because the validity of the final scientific/engineeringconclusions is inextricably linked to the validity of theunderlying univariate assumptions, it naturally follows thatthere is a real necessity that each and every one of theabove four assumptions be routinely tested.

Four Techniquesto TestUnderlyingAssumptions

The following EDA techniques are simple, efficient, andpowerful for the routine testing of underlyingassumptions:

1. run sequence plot (Yi versus i)2. lag plot (Yi versus Yi-1)3. histogram (counts versus subgroups of Y)4. normal probability plot (ordered Y versus theoretical

ordered Y)

Plot on a SinglePage for aQuickCharacterizationof the Data

The four EDA plots can be juxtaposed for a quick look atthe characteristics of the data. The plots below are orderedas follows:

1. Run sequence plot - upper left2. Lag plot - upper right3. Histogram - lower left4. Normal probability plot - lower right

Sample Plot:AssumptionsHold



This 4-plot reveals a process that has fixed location, fixedvariation, is random, apparently has a fixed approximatelynormal distribution, and has no outliers.

Sample Plot:Assumptions DoNot Hold

If one or more of the four underlying assumptions do nothold, then it will show up in the various plots asdemonstrated in the following example.

This 4-plot reveals a process that has fixed location, fixedvariation, is non-random (oscillatory), has a non-normal,U-shaped distribution, and has several outliers.

1.2.4. Interpretation of 4-Plot




Interpretationof EDAPlots:Flat andEqui-Banded,Random,Bell-Shaped,and Linear

The four EDA plots discussed on the previous page areused to test the underlying assumptions:

1. Fixed Location:If the fixed location assumption holds, then the runsequence plot will be flat and non-drifting.

2. Fixed Variation:If the fixed variation assumption holds, then thevertical spread in the run sequence plot will be theapproximately the same over the entire horizontalaxis.

3. Randomness:If the randomness assumption holds, then the lag plotwill be structureless and random.

4. Fixed Distribution:If the fixed distribution assumption holds, inparticular if the fixed normal distribution holds, then

1. the histogram will be bell-shaped, and2. the normal probability plot will be linear.

Plots Utilizedto Test theAssumptions

Conversely, the underlying assumptions are tested using theEDA plots:

Run Sequence Plot:If the run sequence plot is flat and non-drifting, thefixed-location assumption holds. If the run sequenceplot has a vertical spread that is about the same overthe entire plot, then the fixed-variation assumptionholds.

Lag Plot:If the lag plot is structureless, then the randomnessassumption holds.

Histogram:If the histogram is bell-shaped, the underlyingdistribution is symmetric and perhaps approximatelynormal.



Normal Probability Plot:If the normal probability plot is linear, the underlyingdistribution is approximately normal.

If all four of the assumptions hold, then the process is saiddefinitionally to be "in statistical control".

1.2.5. Consequences



1.2.5. Consequences

What IfAssumptionsDo Not Hold?

If some of the underlying assumptions do not hold, whatcan be done about it? What corrective actions can betaken? The positive way of approaching this is to view thetesting of underlying assumptions as a framework forlearning about the process. Assumption-testing promotesinsight into important aspects of the process that may nothave surfaced otherwise.

Primary Goalis Correctand ValidScientificConclusions

The primary goal is to have correct, validated, andcomplete scientific/engineering conclusions flowing fromthe analysis. This usually includes intermediate goals suchas the derivation of a good-fitting model and thecomputation of realistic parameter estimates. It shouldalways include the ultimate goal of an understanding and a"feel" for "what makes the process tick". There is no morepowerful catalyst for discovery than the bringing togetherof an experienced/expert scientist/engineer and a data setripe with intriguing "anomalies" and characteristics.

Consequencesof InvalidAssumptions

The following sections discuss in more detail theconsequences of invalid assumptions:

1. Consequences of non-randomness2. Consequences of non-fixed location parameter3. Consequences of non-fixed variation4. Consequences related to distributional assumptions

1.2.5.1. Consequences of Non-Randomness


1. Exploratory Data Analysis1.2. EDA Assumptions1.2.5. Consequences


RandomnessAssumption

There are four underlying assumptions:

1. randomness;2. fixed location;3. fixed variation; and4. fixed distribution.

The randomness assumption is the most critical but theleast tested.

Consequeces ofNon-Randomness

If the randomness assumption does not hold, then

1. All of the usual statistical tests are invalid.2. The calculated uncertainties for commonly used

statistics become meaningless.3. The calculated minimal sample size required for a

pre-specified tolerance becomes meaningless.4. The simple model: y = constant + error becomes

invalid.5. The parameter estimates become suspect and non-

supportable.

Non-RandomnessDue toAutocorrelation

One specific and common type of non-randomness isautocorrelation. Autocorrelation is the correlationbetween Yt and Yt-k, where k is an integer that defines thelag for the autocorrelation. That is, autocorrelation is atime dependent non-randomness. This means that thevalue of the current point is highly dependent on theprevious point if k = 1 (or k points ago if k is not 1).Autocorrelation is typically detected via anautocorrelation plot or a lag plot.

If the data are not random due to autocorrelation, then

1. Adjacent data values may be related.2. There may not be n independent snapshots of the

phenomenon under study.3. There may be undetected "junk"-outliers.4. There may be undetected "information-rich"-

outliers.

1.2.5.2. Consequences of Non

engineering statistics handbook

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