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Data Mining & Statistical Analysis Using SQL Paperback – September 17, 2001


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Editorial Reviews

Amazon.com Review

Data Mining and Statistical Analysis Using SQL concerns itself with the interface between applied mathematics--the discipline of statistical analysis--and really applied mathematics in the form of Structured Query Language (SQL) code that carries out such analysis. It's a subject that deserves careful coverage in a book, and the authors of this one--both working analysts with distinguished academic backgrounds--have done great work. If you're faced with a need to derive meaning from large quantities of data (from retail sales, industrial processes, or even scientific observations), and canned analysis tools aren't cutting it for you, take time to study what Robert Trueblood and John Lovett have to say.

Though some background in statistics will help you pick up on what Trueblood and Lovett have written, a low-level university class (even one far in your past) should be enough. Their approach to all of the analysis techniques they teach is to explain terms and concepts with prose, then with graphs, then with formulas. Then, they translate the formulas into SQL queries for Microsoft Access and show variations on the code that yield differently tweaked results. Finally, T-SQL source code (for Microsoft SQL Server 2000) is listed, though most readers will prefer to grab this code from the book's companion Web site. Additional coverage of graphics would make this book better, but in its present state it's great reading for people who want to interpret their mountains of data. --David Wall

Topics covered: Statistical analysis as a set of mathematical tools that may be implemented in Structured Query Language (SQL), specifically SQL variants for Microsoft database products. Chapters explain how to use hypothesis testing, curve fitting, scatter plots, measurements of central tendency, and regression analysis to spot significant characteristics of data.

About the Author

John N. Lovett, Jr. is a senior engineering consultant at QuantiTech, Inc. and co-owner, with his anthropologist/archaeologist wife, Jane, of Falls Mill and Museum in Belvedere, Tennessee. He has a Ph.D. in industrial engineering, a master's degree in operations research, and a bachelor's degree in mathematics.

Robert P. Trueblood is an analyst at QuantiTech, Inc. He has a Ph.D. in computer science and applications, and taught at the university level for 18 years before transitioning into private industry. He enjoys designing and implementing systems in Visual Basic.
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Product Details

  • Paperback: 250 pages
  • Publisher: Apress; 1 edition (September 17, 2001)
  • Language: English
  • ISBN-10: 1893115542
  • ISBN-13: 978-1893115545
  • Product Dimensions: 7.5 x 1 x 9.2 inches
  • Shipping Weight: 1.5 pounds (View shipping rates and policies)
  • Average Customer Review: 3.0 out of 5 stars  See all reviews (6 customer reviews)
  • Amazon Best Sellers Rank: #2,072,095 in Books (See Top 100 in Books)

Customer Reviews

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Most Helpful Customer Reviews

17 of 17 people found the following review helpful By Larry Bahney on June 6, 2002
Format: Paperback
My perspective is that I do not consider basic statistics a part of Data Mining. By basic statistics, I mean ANOVA, regression, etc. Therefore, I feel the title is misleading. Only basic statistical analysis is shown using SQL. Data Mining techniques are not covered.
As far as statistical analysis .... The Design of Experiments chapter does not cover replicated ANOVA, and does not even mention factorial designs. The Time Series chapter does not cover autoregressive-moving average models.
If you want a book that implements some of Excel's Analysis Tools routines in SQL, then this book is for you. If you want an introduction to data mining techniques, look elsewhere.
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10 of 10 people found the following review helpful By John Jiang on September 16, 2001
Format: Paperback
This would be a good book for Statistical Analysis using SQL, but it lacks the other side of data mining-data visualization. I hope the authors would include some non-statistical techniques of data mining in their future edition.
At the meanwhile, reader may read other books, such as C. Westphal's Data Mining Solutions, for non-statistical data visualization techniques.
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10 of 12 people found the following review helpful By Phillip I. Good on November 19, 2002
Format: Paperback
This is yet another introductory statistics text--a particularly old-fashioned one. The words "Data Mining" in the title are toitally misleading and I'm sorry I borrowed it. It is useless as a text book as it has nothing on resampling methods, the bootstrap and permutation tests.
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