Chemometrics Using R

by David Harvey

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The goal of this textbook is to provide an introduction to chemometrics suitable for the undergraduate chemistry curriculum at the junior or senior level. Although you may not yet know what we mean by the term chemometrics, you almost certainly make routine use of chemometric techniques in your classes and labs: reporting an average result for several trials of an experiment or creating a calibration curve.

Description from LibreTexts.

Publisher
DePauw University
Subjects
Chemistry, Analytical Chemistry
Licence
CC BY-NC-SA 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. What is Chemometrics and Why Study it?
  7. 1: R and RStudio
  8. 1.1: Installing and Accessing R and RStudio
  9. 1.2: The Basics of Working With R
  10. 1.3: Exercises
  11. 2: Types of Data
  12. 2.1: Ways to Describe Data
  13. 2.2: Using R to Organize and Manipulate Data
  14. 2.3: Exercises
  15. 3: Visualizing Data
  16. 3.1: Types of Visualizations
  17. 3.2: Using R to Visualize Data
  18. 3.3: Creating Plots From Scratch in R Using Base Graphics
  19. 3.4: Exercises
  20. 4: Summarizing Data
  21. 4.1: Ways to Summarize Data
  22. 4.2: Using R to Summarize Data
  23. 4.3: Exercises
  24. 5: The Distribution of Data
  25. 5.1: Terminology
  26. 5.2: Theoretical Models for the Distribution of Data
  27. 5.3: The Central Limit Theorem
  28. 5.4: Modeling Distributions Using R
  29. 5.5: Exercises
  30. 6: Uncertainty of Data
  31. 6.1: Properties of a Normal Distribution
  32. 6.2: Confidence Intervals
  33. 6.3: Using R to Model Properties of a Normal Distribution
  34. 6.4: Using R to Find Confidence Intervals
  35. 6.5: Exercises
  36. 7: Testing the Significance of Data
  37. 7.1: Significance Testing
  38. 7.2: Significance Tests for Normal Distributions
  39. 7.3: Analysis of Variance
  40. 7.4: Non-Parametric Significance Tests
  41. 7.5: Using R for Significance Testing and Analysis of Variance
  42. 7.6: Exercises
  43. 8: Calibrating Data
  44. 8.1: Unweighted Linear Regression With Errors in y
  45. 8.2: Weighted Linear Regression with Errors in y
  46. 8.3: Weighted Linear Regression With Errors in Both x and y
  47. 8.4: Curvilinear, Multivariable, and Multivariate Regression
  48. 8.5: Using R for a Linear Regression Analysis
  49. 8.6: Exercises
  50. 9: Optimizing Data
  51. 9.1: Response Surfaces
  52. 9.2: Searching Algorithms
  53. 9.3: One-Factor-at-a-Time Optimizations
  54. 9.4: Simplex Optimization
  55. 9.5: Mathematical Models of Response Surfaces
  56. 9.6: Using R to Model a Response Surface (Multiple Regression)
  57. 9.7: Exercises
  58. 10: Cleaning Up Data
  59. 10.1: Signals and Noise
  60. 10.2: Improving the Signal-to-Noise Ratio
  61. 10.3: Background Removal
  62. 10.4: Using R to Clean Up Data
  63. 10.5: Exercises
  64. 11: Finding Structure in Data
  65. 11.1: What Do We Mean By Structure?
  66. 11.2: Cluster Analysis
  67. 11.3: Principal Component Analysis
  68. 11.4: Multivariate Linear Regression
  69. 11.5: Using R for a Cluster Analysis
  70. 11.6: Using R for a Principal Component Analysis
  71. 11.7: Using R for a Multivariate Linear Regression
  72. 11.8: Exercises
  73. 12: Appendices
  74. 12.1: Single-Sided Normal Distribution
  75. 12.2: Critical Values for t-Test
  76. 12.3: Critical Values for F-Test
  77. 12.4: Critical Values for Dixon's Q-Test
  78. 12.5: Critical Values for Grubb's Test
  79. 12.6: Critical Values for the Wilcoxson Signed Rank Test
  80. 12.7: Critical Values for the Wilcoxson Ranked Sum Test
  81. 13: Resources
  82. 13.1: Chemometric Resources
  83. 13.2: R Resources
  84. Back Matter
  85. Index
  86. Glossary
  87. Detailed Licensing