What is included with this book?
Crunch Big Data to optimize marketing and more!
Overwhelmed by all the Big Data now available to you? Not sure what questions to ask or how to ask them? Using Microsoft Excel and proven decision analytics techniques, you can distill all that data into manageable sets—and use them to optimize a wide variety of business and investment decisions. In Decision Analytics: Microsoft Excel, best selling statistics expert and consultant Conrad Carlberg will show you how—hands-on and step-by-step.
Carlberg guides you through using decision analytics to segment customers (or anything else) into sensible and actionable groups and clusters. Next, you’ll learn practical ways to optimize a wide spectrum of decisions in business and beyond—from pricing to cross-selling, hiring to investments—even facial recognition software uses the techniques discussed in this book!
Through realistic examples, Carlberg helps you understand the techniques and assumptions that underlie decision analytics and use simple Excel charts to intuitively grasp the results. With this foundation in place, you can perform your own analyses in Excel and work with results produced by advanced stats packages such as SAS and SPSS.
This book comes with an extensive collection of downloadable Excel workbooks you can easily adapt to your own unique requirements, plus VBA code to streamline several of its most complex techniques.
Register your book for access to all sample workbooks, updates, and corrections as they become available at quepublishing.com/title/9780789751683.
Conrad Carlberg lives near San Diego with his wife, not too far from the beach, but high enough that the rise in the sea level is unlikely to convert their home to waterfront property. Two cats round out the indoor menagerie; the three rabbits are required to stay outside.
1. Components of Decision Analytics
2. Logistic Regression
3. Univariate Analysis of Variance (ANOVA)
4. Multivariate Analysis of Variance (MANOVA)
5. Discriminant Function Analysis. The Basics
6. Discriminant Function Analysis. Further Issues
7. Principal Components
8. Cluster Analysis: The Basics
9. Cluster Analysis: Further Issues