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[书籍介绍] Predictive Analytics : Microsoft Excel, Second Edition (PDF) [推广有奖]

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igs816 在职认证  发表于 2018-8-6 15:01:18 |显示全部楼层
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English | 2017 | ISBN: 0789758350 | 382 Pages | PDF
Now, you can apply cutting-edge predictive analytics techniques to help your business win–and you don’t need multimillion-dollar software to do it. All the tools you need are available in Microsoft Excel 2016, 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 problems in areas ranging from sales and marketing to operations. Carlberg offers unprecedented insight into building powerful, credible, and reliable forecasts, helping you gain deep insights from Excel that would be difficult to uncover with costly tools such as SAS or SPSS.

Fully updated for Excel 2016, this guide contains valuable new coverage of accounting for seasonality and managing complex consumer choice scenarios. Throughout, Carlberg provides downloadable Excel 2016 workbooks you can easily adapt to your own needs, plus VBA code–much of it open-source–to streamline especially 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 the “how” and “why” of using data to make better decisions, and choose the right technique for each problem
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
Account for trends and seasonality via Holt-Winters smoothing
Prevent trends from running out of control over long time horizons
Construct more accurate predictions by using Solver
Manage large numbers of variables and unwieldy datasets with principal components analysis and Varimax factor rotation
Apply ARIMA (Box-Jenkins) techniques to build better forecasts and clarify their meaning
Handle complex consumer choice problems with advanced logistic regression
Benchmark Excel results against R results

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