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

The Hazard Calculation

The rest of this chapter explores and explains various calculations used in survival analysis, with particular emphasis on using SQL and Excel to do them. The examples in the rest of the chapter use the subscription dataset, which consists of customers of a mobile phone company in three markets.

The hazard calculation is the foundation of survival analysis, and it depends on the start date, stop date, and stop type columns. This section first explores these columns and then goes into the calculation of the hazard itself.

Data Investigation

Survival analysis fundamentally relies on two pieces of information about each customer, the stop flag (whether the customer is stopped or active) and the tenure (how long the customer was active). Often, these columns must be derived from other columns in the database. In Subscribers, for instance, the tenure is already calculated but the stop flag must be derived from other columns.

Because this information is so important...