References

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  2. Batini, C., and M. Scannapieco. 2006. Data Quality: Concepts, Methodologies and Techniques. Berlin: Springer.
  3. Chiang, F., and M. J. Renee. 2008. “Discovering Data Quality Rules.” ­Proceedings of the 34th International Conference on Very Large Data Bases. Auckland, New Zealand.
  4. Deming, W. E. 1960., Sample Design in Business Research. New York: John Wiley & Sons.
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  6. Duhigg, Charles. 2012. The Power of Habit: Why We Do What We Do in Life and Business. New York: Random House.
  7. Gartner, Inc. 2012. Big Data Strategy Components: IT Essentials. ­Stamford, CT: Gartner, Inc.
  8. Harrington, James. 2013. The Five Pillars of Organizational Excellence. Quality Digest. www.qualitydigest.com/aug06/articles/05_article.shtml.
  9. Haug, Anders, Frederik Zachariassen, and Dennis van Liempd. 2011. “The Costs of Poor Data Quality.” Journal of Industrial Engineering and Management 4(2):168–193.
  10. Herzog, T. N., F. J. Scheuren, and W. E. Winkler. 2007. Data Quality and Record Linkage Techniques. New York: Springer.
  11. Institute of International Finance (IIF) and McKinsey & Company. 2011. Risk IT and Operations: Strengthening Capabilities Manual. Johnson, Richard A., and Dean W. Wichern. 1992. Applied Multivariate Statistical Analysis. Englewood Cliffs, NJ: Prentice Hall.
  12. Jugulum, Rajesh and Samuel, Philip (2008). Design for Lean Six Sigma: A Holistic Approach to Design and Innovation, New York: John Wiley & Sons.
  13. Juran, Joseph M., and A. Blanton Godfrey. 1999. Juran's Quality Handbook. New York: McGraw-Hill.
  14. Maydanchik, Arkady. 2007. Data Quality Assessment. Bradley Beach, NJ: Technics Publications.
  15. McFadden, Fred R. 1993. “Six-Sigma Quality Programs.”Quality Progress 26(6):37–42.
  16. Montgomery, Douglas C., and Elizabeth A. Peck. 1982. Introduction to Linear Regression Analysis. New York: John Wiley & Sons.
  17. Rao, C. R. 1973. Linear Statistical Inference and Its Applications. New York: John Wiley & Sons.
  18. Redman, T. C. 1998. “The Impact of Poor Data Quality on the Typical ­Enterprise.” Communications of ACM 41(2):191–204.
  19. Shanks, G., and P. Darke. 1998. “Understanding Data Quality in a Data Warehouse.” The Australian Computer Journal 30:122–128.
  20. Taguchi, Genichi. 1986. Introduction to Quality Engineering. Tokyo: Asian Productivity Organization.
  21. ———. 1987. System of Experimental Design. Vols. 1 and 2. White Plains, NY: ASI and Quality Resources.
  22. Taguchi, Genichi, and Rajesh Jugulum. 1999. “Role of S/N Ratios in Multivariate Diagnosis.” Journal of Japanese Quality Engineering Society 7(6):63–69.
  23. Taguchi, Genichi, and Rajesh Jugulum. 2002 The Mahalanobis-Taguchi Strategy: A Pattern Technology. Hoboken: John Wiley & Sons.
  24. Tan, P.-N., M. Steinbach, and V. Kumar. 2005. Introduction to Data ­Mining. Reading, MA: Addison-Wesley.
  25. Wang, R. Y., and D. M. Strong. 1996. “Beyond Accuracy: What Data Quality Means to Data Consumers.” Journal of Management Information Systems 12(4): 5–33.

Further Resources

  1. Anderson T. W. 1984. An Introduction to Multivariate Statistical Analysis. 2nd ed. New York: John Wiley & Sons.
  2. Blake, R., and P. Mangiameli. 2011. “The Effects and Interactions of Data Quality and Problem Complexity on Classification.” Journal of Data and Information Quality, 2(2):1–28.
  3. Box, G. E. P. 1999. “Statistics as a Catalyst to Learning by the Scientific Method Part II—A Discussion.” Journal of Quality Technology 31(1):16–29.
  4. Brown, William C. 1991. Matrices and Vector Spaces. New York: Marcel Dekker, Inc.
  5. Clausing, Don. 1994. Total Quality Development: A Step-by-Step Guide to World-Class Concurrent Engineering. New York: ASME Press.
  6. Cong, G., W. Fan, F. Geerts, X. Jia, and S. Ma. 2007. “Improving Data Quality: Consistency and Accuracy.” Proceedings of the 33rd International Conference on Very Large Data Bases. Vienna, Austria.
  7. Creveling, C. M., J. L. Slutsky, and D. Antis, Jr. 2002. Design for Six Sigma in Technology and Product Development. Upper Saddle River, NJ: ­Prentice Hall.
  8. Dasgupta, Somesh. 1993. “The Evolution of the D2-Statistic of ­Mahalanobis.” Sankhya 55:442–459.
  9. Davenport, Thomas H., and Jeanne G. Harris. 2007. Competing on ­Analytics: The New Science of Winning. Boston: Harvard Business School Publishing.
  10. English, L. 1999. Improving Data Warehouse and Business Information Quality. New York: John Wiley & Sons.
  11. English, Larry P. 2009. Information Quality Applied: Best Practices for Improving Business Information, Processes, and Systems. Hoboken: John Wiley & Sons.
  12. Grant, Eugene L., and Richard S. Leavenworth. 1996. Statistical Quality Control. New York: McGraw-Hill.
  13. Hohn, Franz E. 1967. Elementary Matrix Algebra. New York: Macmillan.
  14. Huang, K., T. Lee, and R. Y. Wang. 1999. Quality Information and ­Knowledge. Englewood Cliffs, NJ: Prentice Hall.
  15. Jugulum, Rajesh. 2000. “New Dimensions in Multivariate Diagnosis to ­Facilitate Decision Making Process,” Ph.D. Diss., Wayne State University.
  16. Jugulum, Rajesh, Suneeta Ijari, and Madan Mohan Chakravarthy. 1996. “Six Sigma Quality Programs—Indian Case Examples.” Japanese ­Union of Scientists and Engineers (JUSE) Conference Proceedings, Japan.
  17. Jugulum, Rajesh, Shin Taguchi, and Kai Yang. 1999. “New ­Developments in Multivariate Diagnosis: A Comparison between Two Methods.”Journal of Japanese Quality Engineering Society 7(5):62–72.
  18. Kim, W. 2002. “On Three Major Holes in Data Warehousing Today.” ­Journal of Objective Technology 1(4):39–47.
  19. Kim, W., and B. Choi. 2003. “Towards Quantifying Data Quality Costs.” Journal of Objective Technology 2(4):69–76.
  20. Leitnaker, Mary G., Richard D. Sanders, and Chery1 Hild. 1996. The Power of Statistical Thinking: Improving Industrial Processes. Reading, MA: Addison-Wesley.
  21. Madnick, S., R. Y. Wang, Y. W. Lee, and H. Zhu. 2009. “Overview and Framework for Data and Information Quality Research.” Journal of Data and Information Quality 1(1):1–22.
  22. Madnick, S., and H. Zhu. 2006. “Improving Data Quality through ­Effective Use of Data Semantics.” Data and Knowledge Engineering 59(2):460–475.
  23. Mahalanobis, P. C. 1936. “On the Generalized Distance in Statistics.” ­Proceedings, National Institute of Science of India 2: 49–55.
  24. Montgomery, Douglas C. 1996. Introduction to Statistical Quality Control. New York: John Wiley & Sons.
  25. Morrison, Donald F. 1967. Multivariate Statistical Methods. New York: McGraw-Hill.
  26. ———. 1990. Multivariate Statistical Methods. 3rd ed. McGraw-Hill ­Series in Probability and Statistics. New York: McGraw-Hill.
  27. Park, Sung H. 1996. Robust Design and Analysis for Quality Engineering. London: Chapman & Hall.
  28. Phadke, Madhav S. 1989. Quality Engineering Using Robust Design. ­Englewood Cliffs, NJ: Prentice Hall.
  29. Phadke, M. S., and Genichi Taguchi. 1987. “Selection of Quality Characteristics and S/N Ratios for Robust Design.” Conference Record, GLOBECOM 87, IEEE Communication Society, Tokyo, Japan, 1002–1007.
  30. Rao, C. R. 1997. Statistics and Truth: Putting Chance to Work. Singapore: World Scientific Publishing Co.
  31. Redman, T. C. 1996. Data Quality for the Information Age. Boston: Artech House.
  32. Siegel, Eric, and Thomas H. Davenport. 2013. Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die. Hoboken: John Wiley & Sons.
  33. Suh, N. P. 2001. Axiomatic Design: Advances and Applications. New York: Oxford University Press.
  34. ———. 2005. Complexity: Theory and Applications. New York: Oxford University Press.
  35. Taguchi, Genichi. 1988. “The Development of Quality Engineering.” The ASI Journal 1(1):5–29.
  36. Taguchi, Genichi. 1993. Taguchi on Robust Technology Development. New York: ASME Press.
  37. Taguchi, Genichi, and Rajesh Jugulum. 2000. “New Trends in Multivariate Diagnosis.” Sankhya, Indian Journal of Statistics, Series B, Part 2:233–248.
  38. Taguchi, Genichi, and Rajesh Jugulum. 2000. “Taguchi Methods for Software Testing.” Proceedings of JUSE Software Quality Conference I, Japan.
  39. Taguchi, G., R. Jugulum, and S. Taguchi. 2004. Computer Based Robust Engineering: Essentials for DFSS. Milwaukee, WI: ASQ Quality Press.
  40. Taguchi, Genichi, and Jikken Kiekakuho. (1976–77). Design of Experiments. Vols. I and II. Tokyo: Maruzen Co.
  41. Taguchi, Genichi, and Yuin Wu. 1985. Introduction to Off-Line Quality Control. Central Japan Quality Control Association, Tokyo, Japan.
  42. Talburt, J. 2011. Entity Resolution and Information Quality. Burlington, MA: Morgan Kaufmann (Elsevier).
  43. Tracy, N. D., J. C. Young, and R. L. .Mason. 1992. “Multivariate Control Charts for Individual Observations.” Journal of Quality Technology 24:88–95.
  44. Wu, C. F. J., and M. Hamada. 2000. Experiments: Planning, Analysis, and Parameter Design Optimization. New York: John Wiley & Sons.
  45. Yang, Kai, and Basem S. EI-Haik. 2003. Design for Six Sigma: A Roadmap for Product Development. New York: McGraw-Hill.
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