Data Mining & Statistical Analysis Using SQL [Paperback]

John N. Lovett (Author), John Jr. Lovett (Author)

Book Description

Publication Date: October 15, 2001

This book is not just another theoretical text on statistics or data mining. Instead, it's designed for DBAs, database administrators, who want to buttress their understanding of statistics to support data mining and customer relationship management analytics and who want to use SQL, Structured Query Language. Each chapter is independent and self-contained with examples tailored to business applications. Each analysis technique is expressed in a mathematical format that lends itself to coding either as a database query or as a Visual Basic procedure using SQL.
Each chapter includes: formulas (how to perform the required analysis, numerical example using data from a database, data visualization and presentation options (graphs, charts, tables), SQL procedures for extracting the desired results, and data mining techniques.
About the authors:
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 going into private industry. He enjoys designing and implementing systems in Visual Basic.
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, TN. He has a Ph.D. in industrial engineering, with a BS in mathematics and MS in operations research.

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.

Product Details

• Paperback: 250 pages
• Publisher: Apress; 1 edition (October 15, 2001)
• Language: English
• ISBN-10: 1893115542
• ISBN-13: 978-1893115545
• Product Dimensions: 9.1 x 7.9 x 1.1 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: #1,030,189 in Books (See Top 100 in Books)
  • #83 in Books > Computers & Technology > Programming > Languages & Tools > Compilers

Customer Reviews

Most Helpful Customer Reviews

15 of 15 people found the following review helpful:

3.0 out of 5 stars Should be called "Basic Statistical Analysis Using SQL", June 6, 2002

By 

Larry Bahney "WharfRat" (Canton, OH United States) - See all my reviews
(REAL NAME)   

This review is from: Data Mining & Statistical Analysis Using SQL (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.

10 of 10 people found the following review helpful:

3.0 out of 5 stars Data Mining and Statistical Analysis Using SQL, September 16, 2001

By 

John Jiang (Shelton, CT United States) - See all my reviews

This review is from: Data Mining & Statistical Analysis Using SQL (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.

10 of 12 people found the following review helpful:

1.0 out of 5 stars Liar, liar, pants on fire--no data mining in this book, November 19, 2002

By 

Phillip I. Good (Huntington Beach, CA United States) - See all my reviews
(REAL NAME)   

This review is from: Data Mining & Statistical Analysis Using SQL (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.