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R for Microsoft® Excel Users: Making the Transition for Statistical Analysis

Book Description

This is the Rough Cut version of the printed book.

Conrad Carlberg's Statistical Analysis with R and Microsoft® Excel is the first complete guide to performing modern statistical analyses with Excel, R, or both. Drawing on his immense experience helping organizations gain value from statistical methods, Carlberg shows when and how to use Excel, when and how to use R instead, and how to use them together to get the best from both.

Writing in clear, understandable English, Carlberg combines an exploration of statistical theory with a hands-on description of how to perform many common statistical analyses with both Excel and R. Through examples, you'll gain practical insights into each tool's strengths and weaknesses in a wide variety of common analytic scenarios. Coverage includes:

  • Preparing data for analysis

  • Performing simple descriptive analyses

  • Using Excel and R to perform regressions

  • Analyzing variance and covariance

  • Running logistic regressions

  • Analyzing time series and principal components

  • Moving comfortably between R and Excel

  • Statistical Analysis with R and Microsoft® Excel will be especially valuable for Excel users who:

  • Have complex analytical problems that can't easily be solved with Excel's built-in tools

  • Don't want to write custom Visual Basic or C code to perform advanced Excel analyses

  • Want to combine R's power with Excel's simplicity and intuitive visual reports

  • Want to access all the power of a professional-quality statistical package without the expense

  • Table of Contents

    1. Title Page
    2. Copyright Page
    3. Contents at a Glance
    4. Table of Contents
    5. Foreword [This content is currently in development.]
    6. Preface [This content is currently in development.]
    7. Dedication [This content is currently in development.]
    8. About the Author
    9. Acknowledgments
    10. We Want to Hear from You!
    11. Reader Services
    12. Introduction
    13. 1. Making the Transition
      1. Adjusting Your Expectations
      2. The User Interface
      3. Special Characters
      4. Obtaining R
      5. Contributed Packages
      6. Running Scripts
      7. Importing Data into R from Excel
      8. Exporting Data from R to Excel
    14. 2. Descriptive Statistics
      1. Descriptive Statistics in Excel
      2. Using R's DescTools Package
      3. Entering Some Useful Commands
      4. Running Bivariate Analyses with Desc
    15. 3. Regression Analysis in Excel and R
      1. Worksheet Functions
      2. Functions for Statistical Inference
      3. Other Sources of Regression Analysis in Excel
      4. Regression Analysis in R
    16. 4. Analysis of Variance and Covariance in Excel and R
      1. Single-Factor Analysis of Variance
      2. Single-Factor ANOVA Using R
      3. The Factorial ANOVA
      4. Analyzing Unbalanced Two-Factor Designs in Excel and R
      5. Multiple Comparison Procedures in Excel and R
      6. Analysis of Covariance in Excel and R
    17. 5. Logistic Regression in Excel and R
      1. Problems with Linear Regression and Nominal Variables
      2. From the Log Odds to the Probabilities
      3. Deploying Solver
      4. Statistical Tests in Logistic Regression
      5. Logistic Regression with R's mlogit Package
      6. Using R's glm Function
    18. 6. Principal Components Analysis
      1. Principal Components Using Excel
      2. Rotated Factors in Excel
      3. Principal Components Analysis Using R