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1.   Exploratory Data Analysis - Detailed Table of Contents  [1.]


This chapter presents the assumptions, principles, and techniques necessary to gain insight into data via EDA--exploratory data analysis.
  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 Summary Analysis?  [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 Distributed Data  [1.3.3.21.1.]
        2. Normal Probability Plot: Data Have Short Tails  [1.3.3.21.2.]
        3. Normal Probability Plot: Data Have Long Tails  [1.3.3.21.3.]
        4. Normal Probability Plot: Data are Skewed Right  [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 Standard Deviation  [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.]
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