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SRG Sites > NewTechReview > News > Decision Analytics: Microsoft Excel from Que Publishing
Decision Analytics: Microsoft Excel from Que Publishing
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Que has published Decision Analytics: Microsoft Excel. 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.

* Classify data according to existing categories or naturally occurring clusters of predictor variables
* Cut massive numbers of variables and records down to size, so you can get the answers you really need
* Utilize cluster analysis to find patterns of similarity for market research and many other applications
* Learn how multiple discriminant analysis helps you classify cases
* Use MANOVA to decide whether groups differ on multivariate centroids
* Use principal components to explore data, find patterns, and identify latent factors

For more information, visit QuePublishing.com.
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