Book Image

Data Analysis Using SQL and Excel - Second Edition

By : Gordon S. S. Linoff
Book Image

Data Analysis Using SQL and Excel - Second Edition

By: Gordon S. S. Linoff

Overview of this book

Data Analysis Using SQL and Excel, 2nd Edition shows you how to leverage the two most popular tools for data query and analysis—SQL and Excel—to perform sophisticated data analysis without the need for complex and expensive data mining tools. Written by a leading expert on business data mining, this book shows you how to extract useful business information from relational databases. You'll learn the fundamental techniques before moving into the "where" and "why" of each analysis, and then learn how to design and perform these analyses using SQL and Excel. Examples include SQL and Excel code, and the appendix shows how non-standard constructs are implemented in other major databases, including Oracle and IBM DB2/UDB. The companion website includes datasets and Excel spreadsheets, and the book provides hints, warnings, and technical asides to help you every step of the way. Data Analysis Using SQL and Excel, 2nd Edition shows you how to perform a wide range of sophisticated analyses using these simple tools, sparing you the significant expense of proprietary data mining tools like SAS.
Table of Contents (18 chapters)
Free Chapter
1
Foreword
17
EULA

Lessons Learned

This chapter introduces association rules—automatically generated rules about the items most likely to appear together in an order. This is one of the most detailed ways of analyzing transaction information.

Simple one-way association rules specify that when a customer purchases one product (the left-hand side), then the customer is likely to purchase another product (the right-hand side) in the same order. The traditional way of measuring the goodness of these rules is with three measures: support, confidence, and lift. Support measures the proportion of orders where the rule is true. Confidence measures how often the rule is true when it applies. And lift specifies how much better the rule works rather than just guessing.

A better measure, however, is based on the chi-square value introduced in Chapter 3. This gives an indication of how likely it is that the rule is random—and when this likelihood is very small, then the rule is important.

Association rules...