Statistics and Data Visualization Using R
eBook - ePub

Statistics and Data Visualization Using R

The Art and Practice of Data Analysis

  1. English
  2. ePUB (mobile friendly)
  3. Available on iOS & Android
eBook - ePub

Statistics and Data Visualization Using R

The Art and Practice of Data Analysis

About this book

Designed to introduce students to quantitative methods in a way that can be applied to all kinds of data in all kinds of situations, Statistics and Data Visualization Using R: The Art and Practice of Data Analysis by David S. Brown teaches students statistics through charts, graphs, and displays of data that help students develop intuition around statistics as well as data visualization skills. By focusing on the visual nature of statistics instead of mathematical proofs and derivations, students can see the relationships between variables that are the foundation of quantitative analysis. Using the latest tools in R and R RStudio® for calculations and data visualization, students learn valuable skills they can take with them into a variety of future careers in the public sector, the private sector, or academia. Starting at the most basic introduction to data and going through most crucial statistical methods, this introductory textbook quickly gets students new to statistics up to speed running analyses and interpreting data from social science research.

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Yes, you can access Statistics and Data Visualization Using R by David S. Brown in PDF and/or ePUB format, as well as other popular books in Social Sciences & Statistics for Business & Economics. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Half Title
  3. Acknowledgements
  4. Publisher Note
  5. Title Page
  6. Copyright Page
  7. Brief Contents
  8. Detailed Contents
  9. Preface
  10. Acknowledgments
  11. About the Author
  12. 1 Getting Started
  13. 2 An Introduction to Data Analysis
  14. 3 Describing Data
  15. 4 Central Tendency and Dispersion
  16. 5 Univariate and Bivariate Descriptions of Data
  17. 6 Transforming Data
  18. 7 Some Principles of Displaying Data
  19. 8 The Essentials of Probability Theory
  20. 9 Confidence Intervals and Testing Hypotheses
  21. 10 Making Comparisons
  22. 11 Controlled Comparisons
  23. 12 Linear Regression
  24. 13 Multiple Regression
  25. 14 Dummies and Interactions
  26. 15 Diagnostics I: Is Ordinary Least Squares Appropriate?
  27. 16 Diagnostics II: Residuals, Leverages, and Measures of Influence
  28. 17 Logistic Regression
  29. Appendix: Developing Empirical Implications
  30. Glossary
  31. References
  32. Index