More Predictive Analytics

Author: Conrad Carlberg
Publisher: Que Publishing
ISBN: 9780134070933
Size: 18.64 MB
Format: PDF, Mobi
View: 22

Accurate, practical Excel predictive analysis: powerful smoothing techniques for serious data crunchers! In More Predictive Analytics, Microsoft Excel® MVP Conrad Carlberg shows how to use intuitive smoothing techniques to make remarkably accurate predictions. You won’t have to write a line of code--all you need is Excel and this all-new, crystal-clear tutorial. Carlberg goes beyond his highly-praised Predictive Analytics, introducing proven methods for creating more specific, actionable forecasts. You’ll learn how to predict what customers will spend on a given product next year… project how many patients your hospital will admit next quarter… tease out the effects of seasonality (or patterns that recur over a day, year, or any other period)… distinguish real trends from mere “noise.” Drawing on more than 20 years of experience, Carlberg helps you master powerful techniques such as autocorrelation, differencing, Holt-Winters, backcasting, polynomial regression, exponential smoothing, and multiplicative modeling. Step by step, you’ll learn how to make the most of built-in Excel tools to gain far deeper insights from your data. To help you get better results faster, Carlberg provides downloadable Excel workbooks you can easily adapt for your own projects. If you’re ready to make better forecasts for better decision-making, you’re ready for More Predictive Analytics. Discover when and how to use smoothing instead of regression Test your data for trends and seasonality Compare sets of observations with the autocorrelation function Analyze trended time series with Excel’s Solver and Analysis ToolPak Use Holt's linear exponential smoothing to forecast the next level and trend, and extend forecasts further into the future Initialize your forecasts with a solid baseline Improve your initial forecasts with backcasting and optimization Fully reflect simple or complex seasonal patterns in your forecasts Account for sudden, unexpected changes in trends, from fads to new viral infections Use range names to control complex forecasting models more easily Compare additive and multiplicative models, and use the right model for each task

Predictive Analytics

Author: Conrad Carlberg
Publisher: Que Publishing
ISBN: 9780132967259
Size: 13.52 MB
Format: PDF, Docs
View: 29

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

Decision Analytics

Author: Conrad Carlberg
Publisher: Que Publishing
ISBN: 9780133481686
Size: 13.58 MB
Format: PDF, Mobi
View: 59

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 Register your book for access to all sample workbooks, updates, and corrections as they become available at quepublishing.com/title/9780789751683.

Foundations Of Predictive Analytics

Author: James Wu
Publisher: CRC Press
ISBN: 9781439869482
Size: 17.21 MB
Format: PDF
View: 57

Drawing on the authors’ two decades of experience in applied modeling and data mining, Foundations of Predictive Analytics presents the fundamental background required for analyzing data and building models for many practical applications, such as consumer behavior modeling, risk and marketing analytics, and other areas. It also discusses a variety of practical topics that are frequently missing from similar texts. The book begins with the statistical and linear algebra/matrix foundation of modeling methods, from distributions to cumulant and copula functions to Cornish–Fisher expansion and other useful but hard-to-find statistical techniques. It then describes common and unusual linear methods as well as popular nonlinear modeling approaches, including additive models, trees, support vector machine, fuzzy systems, clustering, naïve Bayes, and neural nets. The authors go on to cover methodologies used in time series and forecasting, such as ARIMA, GARCH, and survival analysis. They also present a range of optimization techniques and explore several special topics, such as Dempster–Shafer theory. An in-depth collection of the most important fundamental material on predictive analytics, this self-contained book provides the necessary information for understanding various techniques for exploratory data analysis and modeling. It explains the algorithmic details behind each technique (including underlying assumptions and mathematical formulations) and shows how to prepare and encode data, select variables, use model goodness measures, normalize odds, and perform reject inference. Web Resource The book’s website at www.DataMinerXL.com offers the DataMinerXL software for building predictive models. The site also includes more examples and information on modeling.

Big Data Work

Author: Thomas H. Davenport
Publisher: Vahlen
ISBN: 9783800648153
Size: 16.89 MB
Format: PDF, ePub, Mobi
View: 51

Big Data in Unternehmen. Dieses neue Buch gibt Managern ein umfassendes Verständnis dafür, welche Bedeutung Big Data für Unternehmen zukünftig haben wird und wie Big Data tatsächlich genutzt werden kann. Am Ende jedes Kapitels aktivieren Fragen, selbst nach Lösungen für eine erfolgreiche Implementierung und Nutzung von Big Data im eigenen Unternehmen zu suchen. Die Schwerpunkte - Warum Big Data für Sie und Ihr Unternehmen wichtig ist - Wie Big Data Ihre Arbeit, Ihr Unternehmen und Ihre Branche verändern - - wird - Entwicklung einer Big Data-Strategie - Der menschliche Aspekt von Big Data - Technologien für Big Data - Wie Sie erfolgreich mit Big Data arbeiten - Was Sie von Start-ups und Online-Unternehmen lernen können - Was Sie von großen Unternehmen lernen können: Big Data und Analytics 3.0 Der Experte Thomas H. Davenport ist Professor für Informationstechnologie und -management am Babson College und Forschungswissenschaftler am MIT Center for Digital Business. Zudem ist er Mitbegründer und Forschungsdirektor am International Institute for Analytics und Senior Berater von Deloitte Analytics.

Microsoft Excel 2013 Data Analysis And Business Modeling

Author: Wayne Winston
Publisher: Pearson Education
ISBN: 9780735681071
Size: 16.65 MB
Format: PDF, Kindle
View: 94

Master business modeling and analysis techniques with Microsoft Excel 2013, and transform data into bottom-line results. Written by award-winning educator Wayne Winston, this hands-on, scenario-focused guide shows you how to use the latest Excel tools to integrate data from multiple tables—and how to effectively build a relational data source inside an Excel workbook. Solve real business problems with Excel—and sharpen your edge Summarize data with PivotTables and Descriptive Statistics Explore new trends in predictive and prescriptive analytics Use Excel Trend Curves, multiple regression, and exponential smoothing Master advanced Excel functions such as OFFSET and INDIRECT Delve into key financial, statistical, and time functions Make your charts more effective with the Power View tool Tame complex optimization problems with Excel Solver Run Monte Carlo simulations on stock prices and bidding models Apply important modeling tools such as the Inquire add-in

Business Intelligence In Microsoft Sharepoint 2013

Author: Norm Warren
Publisher: Pearson Education
ISBN: 9780735675872
Size: 18.68 MB
Format: PDF, Mobi
View: 43

Dive into the business intelligence features in SharePoint 2013—and use the right combination of tools to deliver compelling solutions. Take control of business intelligence (BI) with the tools offered by SharePoint 2013 and Microsoft SQL Server 2012. Led by a group of BI and SharePoint experts, you’ll get step-by-step instructions for understanding how to use these technologies best in specific BI scenarios—whether you’re a SharePoint administrator, SQL Server developer, or business analyst. Discover how to: Manage the entire BI lifecycle, from determining key performance indicators to building dashboards Use web-based Microsoft Excel services and publish workbooks on a SharePoint Server Mash up data from multiple sources and create Data Analysis Expressions (DAX) using PowerPivot Create data-driven diagrams that provide interactive processes and context with Microsoft Visio Services Use dashboards, scorecards, reports, and key performance indicators to monitor and analyze your business Use SharePoint to view BI reports side by side, no matter which tools were used to produced them