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Predictive Analytics: Microsoft Excelby Conrad Carlberg
Synopses & Reviews
Excel predictive analytics for serious data crunchers!
The movie Moneyball made predictive analytics famous: Now you can apply the same techniques to help your business win. You don’t need multimillion-dollar software: All the tools you need are available in Microsoft Excel, and all the knowledge and skills are right here, in this book!
Microsoft Excel MVP Conrad Carlberg shows you 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.
You’ll get an extensive collection of downloadable Excel workbooks you can easily adapt to your own unique requirements, plus VBA code—much of it open-source—to streamline several of this book’s most complex techniques.
Step by step, you’ll build on Excel skills you already have, learning advanced techniques that can help you increase revenue, reduce costs, and improve productivity. By mastering predictive analytics, you’ll gain a powerful competitive advantage for your company and yourself.
• Learn both the “how” and “why” of using data to make better tactical decisions
• Choose the right analytics technique for each problem
• Use Excel to capture live real-time data from diverse sources, including third-party websites
• Use logistic regression to predict behaviors such as “will buy” versus “won’t buy”
• Distinguish random data bounces from real, fundamental changes
• Forecast time series with smoothing and regression
• Construct more accurate predictions by using Solver to find maximum likelihood estimates
• Manage huge numbers of variables and enormous datasets with principal components analysis and Varimax factor rotation
• Apply ARIMA (Box-Jenkins) techniques to build better forecasts and understand their meaning
"Moneyball" helped make predictive analytics famous, but companies of all types are discovering these techniques' immense value for improving decision-making and profitability. Not everyone has access to expensive predictive analytics tools such as SAS, but virtually every business professional does have software that can serve the purpose admirably: Microsoft Excel. In this complete, hands-on tutorial, Microsoft Excel MVP Conrad Carlberg shows business professionals exactly how to solve real-world business problems with Excel predictive analytics, in areas ranging from sales and marketing to operations. Building on skills they already have, experienced Excel users will master techniques ranging from least squares regression and moving averages through smoothing, ARIMA, and logistic regression. Carlberg helps Excel users avoid pitfalls associated with simply "plugging" numbers into Excel's Data Analysis add-in (formerly "Analysis ToolPak"), showing how to create more credible, reliable forecasts. His forecasting coverage is more thorough and sophisticated than that of any other book. Carlberg also provides downloadable Excel workbooks that can be easily adapted to readers' unique requirements. This book's techniques are highly prized by companies seeking to increase revenues, reduce costs, and improve productivity; businesspeople who master these skills will have a major competitive advantage.
About the Author
Counting conservatively, this is Conrad Carlberg’s eleventh book about quantitative analysis using Microsoft Excel, which he still regards with a mix of awe and exasperation. A look back at the “About the Author” paragraph in Carlberg’s first book, published in 1995, shows that the only word that remains accurate is “He.” Scary.
Table of Contents
1. Components of Predictive Analytics
2. Forecasting Issues
3. Least Squares Regression
4. Moving Average Approaches
5. Smoothing Approaches
7. Classifying Issues
8. Principal Components
9. Logistic Regression
10. Multiple Logistic Regression and Multinomial Logistic Regression
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