Format:Kindle eBook Language:English (Published) Media:Kindle Edition Edition:1 Pages:426 Number Of Items:1
ASIN:B001IDYO8C
Publication Date:August 1, 2001
Editorial Reviews:
Product Description 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.
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.
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