Linear Regression Using R - An Introduction to Data Modeling

by David Lilja

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Linear Regression Using R: An Introduction to Data Modeling presents one of the fundamental data modeling techniques in an informal tutorial style. Learn how to predict system outputs from measured data using a detailed step-by-step process to develop, train, and test reliable regression models. Key modeling and programming concepts are intuitively described using the R programming language.

Description from LibreTexts.

Publisher
University of Minnesota
Subjects
Statistics, Computing and Modeling
Licence
CC BY-NC 4.0, as LibreTexts states it. Non-commercial use only.
Who can read
Tweens profiles and older, judged from its title and subjects

Contents

LibreTexts writes equations in TeX, and here they show as TeX; the PDF has them typeset.

  1. Front Matter
  2. TitlePage
  3. InfoPage
  4. Table of Contents
  5. Licensing
  6. 1: Introduction
  7. 1.1: Prelude to Linear Regression
  8. 1.2: What is a Linear Regression Model?
  9. 1.3: What is R?
  10. 1.4: What's Next?
  11. 2: Understand Your Data
  12. 2.1: Missing Values
  13. 2.2: Sanity Checking and Data Cleaning
  14. 2.3: The Example Data
  15. 2.4: Data Frames
  16. 2.5: Accessing a Data Frame
  17. 3: One-Factor Regression
  18. 3.1: Visualize the Data
  19. 3.2: The Linear Model Function
  20. 3.3: Evaluating the Quality of the Model
  21. 3.4: Residual Analysis
  22. 4: Multi-factor Regression
  23. 4.1: Visualizing the Relationships in the Data
  24. 4.2: Identifying Potential Predictors
  25. 4.3: The Backward Elimination Process
  26. 4.4: An Example of the Backward Elimination Process
  27. 4.5: Residual Analysis
  28. 4.6: When Things Go Wrong
  29. 5: Predicting Responses
  30. 5.1: Data Splitting for Training and Testing
  31. 5.2: Training and Testing
  32. 5.3: Predicting Across Data Sets
  33. 5.4: Section 5-
  34. 5.5: Section 6-
  35. 6: Reading Data into the R Environment
  36. 6.1: Reading CSV files
  37. 7: Summary
  38. 8: A Few Things to Try Next
  39. Back Matter
  40. Index
  41. Glossary
  42. Detailed Licensing