Mike’s Biostatistics Book

by Michael R Dohm

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The lecture notes are written from my perspective on what matters in a semester-long, first course in Biostatistics: concepts, context and practical advice along with a generous introduction to general linear models. The focus is on applied statistics with reference to other data science skill sets as needed. Concepts are illustrated by examples and multiple choice questions to “test” reader comprehension.

Description from LibreTexts.

Publisher
Chaminade University
Subjects
Statistics, Applied Statistics
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. Disclaimers and copyright
  7. Preface
  8. 1: Getting Started
  9. 1.1: A quick look at R and R Commander
  10. 1.2: Chapter 1 References and Suggested Readings
  11. 2: Introduction
  12. 2.1: Why (Bio)Statistics?
  13. 2.2: Why do we use R software?
  14. 2.3: A brief history of (bio)statistics
  15. 2.4: Experimental Design and rise of statistics in medical research
  16. 2.5: Scientific method and where statistics fits
  17. 2.6: Statistical reasoning
  18. 2.7: Chapter 2 References and Suggested Readings
  19. 3: Exploring Data
  20. 3.1: Data types
  21. 3.2: Measures of central tendency
  22. 3.3: Measures of dispersion
  23. 3.4: Estimating parameters
  24. 3.5: Statistics of error
  25. 3.6: Chapter 3 References and Suggested Reading
  26. 4: How to Report Statistics
  27. 4.1: Bar (column) charts
  28. 4.2: Histograms
  29. 4.3: Box plots
  30. 4.4: Mosaic plots
  31. 4.5: Scatter plots
  32. 4.6: Adding a second Y axis
  33. 4.7: Q-Q plot
  34. 4.8: Ternary plots
  35. 4.9: Heat maps
  36. 4.10: Graph software
  37. 4.11: Chapter 4 References
  38. 5: Experimental Design
  39. 5.1: Experiments
  40. 5.2: Experimental units and sampling units
  41. 5.3: Replication, bias, and nuisance
  42. 5.4: Clinical trials
  43. 5.5: Importance of randomization
  44. 5.6: Sampling from populations
  45. 5.7: Chapter 5 References
  46. 6: Probability and Distributions
  47. 6.1: Some preliminaries
  48. 6.2: Ratios and probabilities
  49. 6.3: Combinations and permutations
  50. 6.4: Types of probability
  51. 6.5: Discrete probability distributions
  52. 6.6: Continuous distributions
  53. 6.7: Normal distribution and the normal deviate
  54. 6.8: Moments
  55. 6.9: Chi-square distribution
  56. 6.10: t-distribution
  57. 6.11: F-distribution
  58. 6.12: Chapter 6 References and Suggested Readings
  59. 7: Probability and Risk Analysis
  60. 7.1: Epidemiology definitions
  61. 7.2: Epidemiology basics
  62. 7.3: Conditional probability and evidence-based medicine
  63. 7.4: Epidemiology relative risk and absolute risk, explained
  64. 7.5: Odds ratio
  65. 7.6: Confidence intervals
  66. 7.7: Chapter 7 References and Suggested Readings
  67. 8: Inferential Statistics
  68. 8.1: The null and alternative hypotheses
  69. 8.2: The controversy over proper hypothesis testing
  70. 8.3: Sampling distribution and hypothesis testing
  71. 8.4: Tails of a test
  72. 8.5: One sample t-test
  73. 8.6: Confidence limits for the estimate of population mean
  74. 8.7: Chapter 8 References and Suggested Readings
  75. 9: Categorical Data
  76. 9.1: Chi-square test and goodness of fit
  77. 9.2: Chi-square contingency tables
  78. 9.3: Yates continuity correction
  79. 9.4: Heterogeneity chi-square tests
  80. 9.5: Fisher exact test
  81. 9.6: McNemar's test
  82. 9.7: Chapter 9 References and Suggested Readings
  83. 10: Quantitative Two-Sample Tests
  84. 10.1: Compare two independent sample means
  85. 10.2: Digging deeper into t-test plus the Welch test
  86. 10.3: Paired t-test
  87. 10.4: Chapter 10 References and Suggested Readings
  88. 11: Power Analysis
  89. 11.1: What is statistical power?
  90. 11.2: Prospective and retrospective power
  91. 11.3: Factors influencing statistical power
  92. 11.4: Two-sample effect size
  93. 11.5: Power analysis in R
  94. 11.6: Chapter 11 References and Suggested Readings
  95. 12: One-way Analysis of Variance
  96. 12.1: The need for ANOVA
  97. 12.2: One-way ANOVA
  98. 12.3: Fixed effects, random effects, and ICC
  99. 12.4: ANOVA from "sufficient statistics"
  100. 12.5: Effect size for ANOVA
  101. 12.6: ANOVA post-hoc tests
  102. 12.7: Many tests, one model
  103. 12.8: Chapter 12 References
  104. 13: Assumptions of Parametric Tests
  105. 13.1: ANOVA assumptions
  106. 13.2: Why tests of assumption are important
  107. 13.3: Test assumption of normality
  108. 13.4: Tests for equal variances
  109. 13.5: Chapter 13 References and Suggested Readings
  110. 14: ANOVA Designs, Multiple Factors
  111. 14.1: Crossed, balanced, fully replicated designs
  112. 14.2: Sources of variation
  113. 14.3: Fixed effects, random effects
  114. 14.4: Randomized block design
  115. 14.5: Nested designs
  116. 14.6: Some other ANOVA designs
  117. 14.7: Rcmdr Multiway ANOVA
  118. 14.8: More on the linear model in rcmdr
  119. 14.9: Chapter 14 References
  120. 15: Nonparametric Tests
  121. 15.1: Kruskal-Wallis and ANOVA by ranks
  122. 15.2: Wilcoxon rank sum test
  123. 15.3: Wilcoxon signed-rank test
  124. 15.4: Chapter 15 References and Suggested Reading
  125. 16: Correlation, Similarity, and Distance
  126. 16.1: Product-moment correlation
  127. 16.2: Causation and partial correlation
  128. 16.3: Data aggregation and correlation
  129. 16.4: Spearman and other correlations
  130. 16.5: Instrument reliability and validity
  131. 16.6: Similarity and distance
  132. 16.7: References and suggested readings
  133. 17: Linear Regression
  134. 17.1: Simple linear regression
  135. 17.2: Relationship between the slope and the correlation
  136. 17.3: Estimation of linear regression coefficient
  137. 17.4: OLS, RMA, and smoothing functions
  138. 17.5: Testing regression coefficients
  139. 17.6: ANCOVA - analysis of covariance
  140. 17.7: Regression model fit
  141. 17.8: Assumptions and model diagnostics for simple linear regression
  142. 18: Multiple Linear Regression
  143. 18.1: Multiple linear regression
  144. 18.2: Nonlinear regression
  145. 18.3: Logistic regression
  146. 18.4: Generalized Linear Squares
  147. 18.5: Selecting the best model
  148. 18.6: Compare two linear models
  149. 18.7: References and suggested readings (Ch. 17 and 18)
  150. 19: Distribution-free methods
  151. 19.1: Jackknife sampling
  152. 19.2: Bootstrap sampling
  153. 19.3: Monte Carlo methods
  154. 19.4: References and suggested reading
  155. 20: Additional Topics
  156. 20.1: Area under the curve
  157. 20.2: Peak detection
  158. 20.3: Baseline correction
  159. 20.4: Conducting surveys
  160. 20.5: Time series
  161. 20.6: Dimensional analysis
  162. 20.7: Estimating population size
  163. 20.8: Diversity indexes
  164. 20.9: Survival analysis
  165. 20.10: Growth equations and dose response calculations
  166. 20.11: Plot a Newick tree
  167. 20.12: Phylogenetically independent contrasts
  168. 20.13: How to get the distances from a distance tree
  169. 20.14: Binary classification
  170. Appendix
  171. A.1: Distribution tables
  172. A.2: Table of Z of standard normal probabilities
  173. A.3: Table of Chi-square critical values
  174. A.4: Table of critical values of Student's t-distribution
  175. A.5: Table of critical values of F-distribution
  176. A.6: Install R
  177. A.7: Install R Commander
  178. A.8: Use R in the cloud
  179. A.9: Jupyter notebook
  180. A.10: R packages
  181. A.11: List of R commands
  182. A.12: Free apps for bioinformatics
  183. Back Matter
  184. Index
  185. Detailed Licensing