ECE 695A Lecture 31: Collecting and Plotting Data

By Muhammad Alam

Electrical and Computer Engineering, Purdue University, West Lafayette, IN

Published on

Abstract

Outline:

  • Origin of data, Field Acceleration vs. Statistical Inference
  • Nonparametric information
  • Preparing data for projection: Hazen formula
  • Preparing data for projection: Kaplan formula
  • Conclusion

Cite this work

Researchers should cite this work as follows:

  • Muhammad Alam (2013), "ECE 695A Lecture 31: Collecting and Plotting Data," http://nanohub.org/resources/17582.

    BibTex | EndNote

Time

Location

EE 226, Purdue University, West Lafayette, IN

Tags

ECE 695A Lecture 31: Collecting and Plotting Data
  • Lecture 31: Collecting and Plotting Data 1. Lecture 31: Collecting and Plo… 0
    00:00/00:00
  • copyright 2011 2. copyright 2011 151.7183850517184
    00:00/00:00
  • Outline 3. Outline 153.48682015348683
    00:00/00:00
  • Where do data come from: TDDB Example 4. Where do data come from: TDDB … 157.22389055722391
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  • Where do data come from: TDDB Example 5. Where do data come from: TDDB … 290.52385719052387
    00:00/00:00
  • Issues with data 6. Issues with data 399.1991991991992
    00:00/00:00
  • Outline 7. Outline 502.46913580246917
    00:00/00:00
  • Moments of the Experimental Data (or discrete distribution) 8. Moments of the Experimental Da… 505.53887220553889
    00:00/00:00
  • Population vs. Sample Distribution 9. Population vs. Sample Distribu… 874.30764097430767
    00:00/00:00
  • Distribution of the Sample Statistic/Moment (e.g. Mean) 10. Distribution of the Sample Sta… 961.594928261595
    00:00/00:00
  • Problem with Sample Moments Quantiles and robust data description 11. Problem with Sample Moments Qu… 1366.066066066066
    00:00/00:00
  • Box plot 12. Box plot 1517.2172172172172
    00:00/00:00
  • Removing Outliers: The logic of 1.5 IQR in Box plot 13. Removing Outliers: The logic o… 1649.2492492492493
    00:00/00:00
  • Removing outliers based on Chauvenet's Criteria 14. Removing outliers based on Cha… 1753.1197864531198
    00:00/00:00
  • Stem and leaf display: Pre-histogram 15. Stem and leaf display: Pre-his… 1833.5001668335003
    00:00/00:00
  • Aside: Derivation of Scott's formula for histogram size 16. Aside: Derivation of Scott's f… 2125.0583917250583
    00:00/00:00
  • Drawing lines resistant to outliers 17. Drawing lines resistant to out… 2198.1648314981649
    00:00/00:00
  • Drawing lines resistant to outliers 18. Drawing lines resistant to out… 2431.8651985318652
    00:00/00:00
  • Outline 19. Outline 2470.8375041708377
    00:00/00:00
  • Problem of data plotting and numerical CDF 20. Problem of data plotting and n… 2474.0407073740407
    00:00/00:00
  • … there is a problem (Failure time is statistical) 21. … there is a problem (Failur… 2548.8488488488488
    00:00/00:00
  • Relationship among various formula 22. Relationship among various for… 2594.6613279946614
    00:00/00:00
  • Aside: Derivation of Hazen Formula 23. Aside: Derivation of Hazen For… 2784.1841841841842
    00:00/00:00
  • Outline 24. Outline 2801.001001001001
    00:00/00:00
  • Censored data and imperfect sampling 25. Censored data and imperfect sa… 2803.9706373039708
    00:00/00:00
  • Hazen (approximate) formula for censored data 26. Hazen (approximate) formula fo… 2917.5842509175845
    00:00/00:00
  • Kaplan-Meier (proper) Formula 27. Kaplan-Meier (proper) Formula 2964.9983316649987
    00:00/00:00
  • For uncensored traditional data … 28. For uncensored traditional dat… 3013.3466800133469
    00:00/00:00
  • For censored data 29. For censored data 3133.8004671338008
    00:00/00:00
  • Summary 30. Summary 3307.6743410076747
    00:00/00:00
  • Larger sample 31. Larger sample 3349.2158825492161
    00:00/00:00
  • Conclusions 32. Conclusions 3402.7694361027698
    00:00/00:00
  • References 33. References 3490.2902902902906
    00:00/00:00