Carlberg C. - More Predictive Analytics. Microsoft Excel

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Conrad Carlberg

800 E. 96th Street
Indianapolis, Indiana 46240

More Predictive Analytics: Microsoft Excel

Copyright 2016 by Que Publishing

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ISBN-13: 978-0-7897-5614-5
ISBN-10: 0-7897-5614-5

Library of Congress Control Number: 2015941441

Printed in the United States of America

First Printing: August 2015

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Conrad Carlberg (www.conradcarlberg.com) is a nationally recognized expert on quantitative analysis and on data analysis and management applications such as Microsoft Excel, SAS, and Oracle. He holds a Ph.D. in statistics from the University of Colorado and is a many-time recipient of Microsofts Excel MVP designation.

Carlberg is a Southern California native. After college he moved to Colorado, where he worked for a succession of startups and attended graduate school. He spent two years in the Middle East, teaching computer science and dodging surly camels. After finishing graduate school, Carlberg worked at US West (a Baby Bell) in product management and at Motorola.

In 1995 he started a small consulting business, which provides design and analysis services to companies that want to guide their business decisions by means of quantitative analysisapproaches that today we group under the term analytics. He enjoys writing about those techniques and, in particular, how to carry them out using the worlds most popular numeric analysis application, Microsoft Excel.

In loving memory of Peter M. Messer, 1942-2015.

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In 2011, I wrote Predictive Analytics: Microsoft Excel. The book went into techniques that are heavily used in the field of predictive analytics but that we dont necessarily think of as predictive.

Those techniques included logistic regression, a technique thats often used in place of least squares regression when the outcome variable is measured with categories; principal components analysis, which groups not records but variables into mutually independent components of related variables; and factor rotation, a technique that helps make the interpretation of principal components more straightforward.

These topics are important ones in predictive analytics, and I personally find them intrinsically interesting and fun to write about. But they soft-pedal the predictive part. You can use logistic regression to predicton the basis of such variables as state of residence, amount loaned, and annual incomewho is likely to repay a loan and who isnt. But thats not the main purpose of logistic regression.

You can use principal components and factor analysis to reduce a database that contains hundreds of related variables down to a manageable few, without significant loss of information. Doing so often makes a forecast feasible, whenusing hundreds of variablesit would not have been sensible even to attempt one.