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

The previous chapter introduced survival analysis and the calculation of hazard and survival probabilities using SQL and Excel. This chapter extends these ideas, showing ways to calculate survival in other situations and to measure the effects of covariates on survival.

The chapter starts by showing how to understand the effects on survival of variables known at the beginning of the customer relationship. The effects might change over time, even though the variables remain constant during each customer’s lifetime. Hazard ratios capture the effects for categorical variables by taking the ratio of the hazards. For numeric variables, the right measure is the average of a numeric variable at different points in the survival curve for active and stopped customers.

One of the biggest challenges in using survival analysis is calculating unbiased hazard probabilities. This is particularly challenging when the data is left truncated—that is, when customers who stopped...