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Decision Analytics: Microsoft® Excel®

Book Description

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.

    Table of Contents

    1. About This eBook
    2. Title Page
    3. Copyright Page
    4. Contents at a Glance
    5. Table of Contents
    6. About the Author
    7. Dedication
    8. Acknowledgments
    9. We Want to Hear from You!
    10. Reader Services
    11. Introduction
      1. What’s in the Book
      2. Why Use Excel?
    12. 1. Components of Decision Analytics
      1. Classifying According to Existing Categories
      2. Classifying According to Naturally Occurring Clusters
      3. Some Terminology Problems
    13. 2. Logistic Regression
      1. The Rationale for Logistic Regression
      2. The Distribution of the Residuals
      3. Using Logistic Regression
      4. Maximizing the Log Likelihood
      5. The Rationale for Log Likelihood
      6. The Statistical Significance of the Log Likelihood
    14. 3. Univariate Analysis of Variance (ANOVA)
      1. The Logic of ANOVA
      2. Single Factor ANOVA
      3. Using the Data Analysis Add-In
      4. Understanding the ANOVA Output
      5. The Regression Approach
    15. 4. Multivariate Analysis of Variance (MANOVA)
      1. The Rationale for MANOVA
      2. Visualizing Multivariate ANOVA
      3. From ANOVA to MANOVA
      4. Getting to a Multivariate F-Ratio
      5. Wilks’ Lambda and the F-Ratio
      6. Running a MANOVA in Excel
      7. After the Multivariate Test
    16. 5. Discriminant Function Analysis: The Basics
      1. Treating a Category as a Number
      2. The Rationale for Discriminant Analysis
      3. Discriminant Analysis and Multiple Regression
      4. The Discriminant Function and the Regression Equation
      5. Wrapping It Up
    17. 6. Discriminant Function Analysis: Further Issues
      1. Using the Discriminant Workbook
      2. Why Run a Discriminant Analysis on Irises?
      3. Benchmarking with R
      4. The Results of the Discrim Add-In
      5. Classifying the Cases
      6. Training Samples: The Classification Is Known Beforehand
    18. 7. Principal Components Analysis
      1. Establishing a Conceptual Framework for Principal Components Analysis
      2. Using the Principal Components Add-In
      3. Counting Eigenvalues, Calculating Coefficients and Understanding Communalities
      4. Relationships Between the Individual Results
      5. Getting the Eigenvalues and Eigenvectors
      6. Rotating Factors to a Meaningful Solution
      7. Classification Examples
    19. 8. Cluster Analysis: The Basics
      1. Cluster Analysis, Discriminant Analysis, and Logistic Regression
      2. Euclidean Distance
      3. Finding Clusters: The Single Linkage Method
      4. The Self-Selecting Nature of Cluster Analysis
      5. Finding Clusters: The Complete Linkage Method
      6. Finding Clusters: The K-means Method
      7. Benchmarking K-means with R
    20. 9. Cluster Analysis: Further Issues
      1. Using the K-means Workbook
      2. Cluster Analysis Using Principal Components
    21. Index