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Chemometrics Using R

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Chemometrics Using R

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.

Analytical ChemistryChemistry
Author
David Harvey
Publisher
DePauw University
Source
LibreTexts
License
CC BY-NC-SA
Language
EN
Category
Basic Sciences
Read online Download PDF (open licence)
Contents· 77 sections
  1. 01: R and RStudio
  2. 1.01: Installing and Accessing R and RStudio
  3. 1.02: The Basics of Working With R
  4. 1.03: Exercises
  5. 02: Types of Data
  6. 2.01: Ways to Describe Data
  7. 2.02: Using R to Organize and Manipulate Data
  8. 2.03: Exercises
  9. 03: Visualizing Data
  10. 3.01: Types of Visualizations
  11. 3.02: Using R to Visualize Data
  12. 3.03: Creating Plots From Scratch in R Using Base Graphics
  13. 3.04: Exercises
  14. 04: Summarizing Data
  15. 4.01: Ways to Summarize Data
  16. 4.02: Using R to Summarize Data
  17. 4.03: Exercises
  18. 05: The Distribution of Data
  19. 5.01: Terminology
  20. 5.02: Theoretical Models for the Distribution of Data
  21. 5.03: The Central Limit Theorem
  22. 5.04: Modeling Distributions Using R
  23. 5.05: Exercises
  24. 06: Uncertainty of Data
  25. 6.01: Properties of a Normal Distribution
  26. 6.02: Confidence Intervals
  27. 6.03: Using R to Model Properties of a Normal Distribution
  28. 6.04: Using R to Find Confidence Intervals
  29. 6.05: Exercises
  30. 07: Testing the Significance of Data
  31. 7.01: Significance Testing
  32. 7.02: Significance Tests for Normal Distributions
  33. 7.03: Analysis of Variance
  34. 7.04: Non-Parametric Significance Tests
  35. 7.05: Using R for Significance Testing and Analysis of Variance
  36. 7.06: Exercises
  37. 08: Modeling Data
  38. 8.01: Linear Regression of a Straight-Line Calibration Curve
  39. 8.02: Weighted Linear Regression with Errors in y
  40. 8.03: Weighted Linear Regression With Errors in Both x and y
  41. 8.04: Curvilinear and Multivariate Regression
  42. 8.05: Using R for a Linear Regression Analysis
  43. 8.06: Exercises
  44. 09: Gathering Data
  45. 9.01: Response Surfaces
  46. 9.02: Searching Algorithms
  47. 9.03: One-Factor-at-a-Time Optimizations
  48. 9.04: Simplex Optimization
  49. 9.05: Mathematical Models of Response Surfaces
  50. 9.06:Using R to Model a Response Surface
  51. 9.07:Exercises
  52. 10: Cleaning Up Data
  53. 10.1: Signals and Noise
  54. 10.2: Signal Averaging
  55. 10.3: Background Removal
  56. 10.4: Using R to Clean Up Data
  57. 10.5: Exercises
  58. 11: Finding Structure in Data
  59. 11.01: What Do We Mean By Structure and Order
  60. 11.02: Cluster Analysis
  61. 11.03: Principal Component Analysis
  62. 11.04: Multivariate Regression
  63. 11.05: Using R for a Cluster Analysis
  64. 11.06: Using R for a Principal Component Analysis
  65. 11.07: Using R For A Multivariate Regression
  66. 11.08: Exercises
  67. 12: Appendices
  68. 12.01: Single-Sided Normal Distribution
  69. 12.02: Critical Values for t-Test
  70. 12.03: Critical Values for F-Test
  71. 12.04: Critical Values for Dixon's Q-Test
  72. 12.05: Critical Values for Grubb's Test
  73. 12.06 Critical Values for the Wilcoxson Signed Rank Test
  74. 12.07: Critical Values for the Wilcoxson Ranked Sum Test
  75. 13: Resources
  76. 13.1: Chemometric Resources
  77. 13.2: R Resources