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 strives to answer questions of the genre “how different is different.” Such questions necessarily bring up the subject of statistics, which has been studying ways to answer such questions for almost two centuries.

The normal distribution, which is defined by an average and a standard deviation, is very important in statistics. Measuring how far a value is from the average, in terms of standard deviations, is the z-score. Large z-scores (regardless of sign) have a very low confidence. That is, the value is probably not produced by a random process, so something is happening.

Counts are very important in customer databases. There are three approaches to determining whether counts for different groups are the same or different. The binomial distribution counts every possible combination, so it is quite precise. The standard error of proportions is useful for getting z-scores. And, the chi-square test directly compares counts across multiple dimensions...