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

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

eBook (Watermarked)

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  • List Price: $31.99
  • Estimated Release: Nov 18, 2016
  • Includes EPUB, MOBI, and PDF
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  • Copyright 2017
  • Dimensions: 7" x 9-1/8"
  • Pages: 272
  • Edition: 1st
  • eBook (Watermarked)
  • ISBN-10: 0-13-457187-8
  • ISBN-13: 978-0-13-457187-4

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

Sample Content

Table of Contents

1. Preparing Data for Analysis
2. Simple Descriptive Analysis
3. Regression Analysis
4. Analysis of Variance and Covariance
5. Logistic Regression
6. Time Series Analysis
7. Principal Components Analysis


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