Predictive Analytics: Microsoft® Excel 2016

Conrad Carlberg  
QUE Publishing
Total pages
July 2017
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Microsoft Excel MVP Conrad Carlberg shows readers how to use Excel predictive analytics to solve real-world problems in areas ranging from sales and marketing to operations. Carlberg offers unprecedented insight into building powerful, credible, and reliable forecasts, showing how to gain deep insights from Excel that would be difficult to uncover with costly tools such as SAS or SPSS.


Readers will get an extensive collection of downloadable Excel workbooks that can be easily adapted to their own unique requirements, plus VBA code—much of it open-source—to streamline several of this book’s most complex techniques.


Step by step, readers build on Excel skills they already have, learning advanced techniques that can help them increase revenue, reduce costs, and improve productivity.


  • The revised complete guide to state-of-the-art predictive analytics with the newest version of the tool that everyone has: Excel!
  • Demystifies advanced techniques and helps readers apply them to real business problems, from sales and marketing to operations
  • Provides hands-on learning with Excel spreadsheets

New to this Edition

Chapter 1 in the first edition of the proposed book concerned itself with how to use
Excel and VBA to download periodically (e.g., by second, by minute, by hour, by day, etc.) sales and related information from web sites. This information did not get much comment in Amazon reviews (there are 42 reviews as of November 2016) and what comment there was split itself between those who like to know how to do that sort of data acquisition, and those whose background in VBA is far too sketchy to understand what was going on. In the interim, many such sites have instituted bot blockers that refuse to complete a query not sent by a browser. I think that it makes sense to drop this chapter but could be talked into retaining it.

The existing Chapter 4 contains information on two types of exponential smoothing: simple smoothing and Holt's linear smoothing, which takes account of trend in a time series. I would expect to expand the chapter to provide more information on Holt's method and add a discussion of Holt-Winters smoothing, which accounts for seasonality in a time series. I might add some material on damped trend forecasts, which help prevent trends from getting out of control beyond the normal forecast horizon.

I also intend to add material to the existing chapter 7 on more advanced issues in logistic regression. At present, the discussion covers situations in which the outcome variable has two values only. Logistic regression also deals with situations in which the outcome variable has 3 or more categorical values. That's a considerably more complex situation. It's difficult to illustrate in Excel and the R syntax to deal with it is esoteric. But the situation arises frequently, particularly in consumer choice situations, and it should get coverage in this book.

Table of Contents

  • 1 Building a Collector
  • 2 Linear Regression
  • 3 Forecasting with Moving Averages
  • 4 Forecasting a Time Series: Smoothing
  • 5 More Advanced Smoothing Models
  • 6 Forecasting a Time Series: Regression
  • 7 Logistic Regression: The Basics
  • 8 Logistic Regression: Further Issues 
  • 9 Multinomial Logistic Regression
  • 10 Principal Components Analysis
  • 11 Box-Jenkins ARIMA Models
  • 12 Varimax Factor Rotation in Excel


Conrad Carlberg is a nationally recognised 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 Microsoft’s 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 that provides design and analysis services to companies that want to guide their business decisions by means of quantitative analysis—approaches that today we group under the term “analytics.” He enjoys writing about those techniques and, in particular, how to carry them out using the world’s most popular numeric analysis application, Microsoft Excel.