Book Description
EXCEL 2016 PREDICTIVE ANALYTICS FOR SERIOUS DATA CRUNCHERS!
Now, you can apply cuttingedge predictive analytics techniques to help your business win–and you don’t need multimilliondollar 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 opensource–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 realtime data from diverse sources, including thirdparty 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 HoltWinters 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 (BoxJenkins) techniques to build better forecasts and clarify their meaning
 Handle complex consumer choice problems with advanced logistic regression
 Benchmark Excel results against R results
Table of Contents
 About This EBook
 Title Page
 Contents at a Glance
 Copyright Page
 Contents
 Introduction to the 2013 Edition
 Introduction to this Edition
 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
 The Notion of a Principal Component

Using the Principal Components AddIn
 The R Matrix
 The Inverse of the R Matrix
 Matrices, Matrix Inverses, and Identity Matrices
 Features of the Correlation Matrix’s Inverse
 Matrix Inverses and Beta Coefficients
 Singular Matrices
 Testing for Uncorrelated Variables
 Using Eigenvalues
 Using Component Eigenvectors
 Factor Loadings
 Factor Score Coefficients
 Principal Components Distinguished from Factor Analysis
 11 BoxJenkins ARIMA Models
 12 Varimax Factor Rotation in Excel
 Index
 Code Snippets
Product Information
 Title: Predictive Analytics: Microsoft® Excel 2016, 2nd Edition
 Author(s):
 Release date: July 2017
 Publisher(s): Que
 ISBN: 9780134682921