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Mike’s Biostatistics Book

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Mike’s Biostatistics Book

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.

Applied StatisticsStatistics
Author
Michael R Dohm
Publisher
Chaminade University
Source
LibreTexts
License
CC BY-NC-SA
Language
EN
Category
Basic Sciences
Read online Download PDF (open licence)
Contents· 175 sections
  1. 01: Getting Started
  2. 1.1: A quick look at R and R Commander
  3. 1.2: Chapter 1 References and Suggested Readings
  4. 02: Introduction
  5. 2.1: Why (Bio)Statistics
  6. 2.2: Why do we use R software
  7. 2.3: A brief history of (bio)statistics
  8. 2.4: Experimental Design and rise of statistics in medical research
  9. 2.5: Scientific method and where statistics fits
  10. 2.6: Statistical reasoning
  11. 2.7: Chapter 2 References and Suggested Readings
  12. 03: Exploring Data
  13. 3.1: Data types
  14. 3.2: Measures of central tendency
  15. 3.3: Measures of dispersion
  16. 3.4: Estimating parameters
  17. 3.5: Statistics of error
  18. 3.6: Chapter 3 References
  19. 04: How to Report Statistics
  20. 4.01: Bar (column) charts
  21. 4.02: Histograms
  22. 4.03: Box plots
  23. 4.04: Mosaic plots
  24. 4.05: Scatter plots
  25. 4.06: Adding a second Y axis
  26. 4.07: Q-Q plot
  27. 4.08: Ternary plots
  28. 4.09: Heat maps
  29. 4.10: Graph software
  30. 4.11: Chapter 4 References
  31. 05: Experimental design
  32. 5.1: Experiments
  33. 5.2: Experimental units and sampling units
  34. 5.3: Replication bias and nuisance
  35. 5.4: Clinical trials
  36. 5.5: Importance of randomization
  37. 5.6: Sampling from populations
  38. 5.7: Chapter 5 References
  39. 06: Probability and Distributions
  40. 6.01: Some preliminaries
  41. 6.02: Ratios and probabilities
  42. 6.03: Combinations and permutations
  43. 6.04: Types of probability
  44. 6.05: Discrete probability distributions
  45. 6.06: Continuous distributions
  46. 6.07: Normal distribution and the normal deviate
  47. 6.08: Moments
  48. 6.09: Chi-square distribution
  49. 6.10: t-distribution
  50. 6.11: F-distribution
  51. 6.12: Chapter 6 References
  52. 07: Probability and Risk Analysis
  53. 7.1: Epidemiology definitions
  54. 7.2: Epidemiology basics
  55. 7.3: Conditional probability and evidence-based medicine
  56. 7.4: Epidemiology relative risk and absolute risk explained
  57. 7.5: Odds ratio
  58. 7.6: Confidence intervals
  59. 7.7: Chapter 7 References
  60. 08: Inferential Statistics
  61. 8.1: The null and alternative hypotheses
  62. 8.2: The controversy over proper hypothesis testing
  63. 8.3: Sampling distribution and hypothesis testing
  64. 8.4: Tails of a test
  65. 8.5: One sample t-test
  66. 8.6: Confidence limits for the estimate of population mean
  67. 8.7: Chapter 8 References
  68. 09: Categorical Data
  69. 9.1: Chi-square test and goodness of fit
  70. 9.2: Chi-square contingency tables
  71. 9.3: Yates continuity correction
  72. 9.4: Heterogeneity chi-square tests
  73. 9.5: Fisher exact test
  74. 9.6: McNemar's test
  75. 9.7: Chapter 9 References
  76. 10: Quantitative Two-Sample Tests
  77. 10.1: Compare two independent sample means
  78. 10.2: Digging deeper into t-test plus the Welch test
  79. 10.3: Paired t-test
  80. 10.4: Chapter 10 References
  81. 11: Power Analysis
  82. 11.1: What is statistical power
  83. 11.2: Prospective and retrospective power
  84. 11.3: Factors influencing statistical power
  85. 11.4: Two-sample effect size
  86. 11.5: Power analysis in R
  87. 11.6: Chapter 11 References
  88. 12: One-way Analysis of Variance
  89. 12.1: The need for ANOVA
  90. 12.2: One-way ANOVA
  91. 12.3: Fixed effects random effects and ICC
  92. 12.4: ANOVA from sufficient statistics
  93. 12.5: Effect size for ANOVA
  94. 12.6: ANOVA post-hoc tests
  95. 12.7: Many tests one model
  96. 12.8: Chapter 12 References
  97. 13: Assumptions of Parametric Tests
  98. 13.1: ANOVA assumptions
  99. 13.2: Why tests of assumption are important
  100. 13.3: Test assumption of normality
  101. 13.4: Tests for equal variances
  102. 13.5: Chapter 13 References
  103. 14: ANOVA Designs Multiple Factors
  104. 14.1: Crossed balanced fully replicated designs
  105. 14.2: Sources of variation
  106. 14.3: Fixed effects random effects
  107. 14.4: Randomized block design
  108. 14.5: Nested designs
  109. 14.6: Some other ANOVA designs
  110. 14.7: Rcmdr Multiway ANOVA
  111. 14.8: More on the linear model in rcmdr
  112. 14.9: Chapter 14 References
  113. 15: Nonparametric Tests
  114. 15.1: Kruskal-Wallis and ANOVA by ranks
  115. 15.2: Wilcoxon rank sum test
  116. 15.3: Wilcoxon signed-rank test
  117. 15.4: Chapter 15 References and Suggested Reading
  118. 16: Correlation Similarity and Distance
  119. 16.1: Product-moment correlation
  120. 16.2: Causation and partial correlation
  121. 16.3: Data aggregation and correlation
  122. 16.4: Spearman and other correlations
  123. 16.5: Instrument reliability and validity
  124. 16.6: Similarity and distance
  125. 16.7: References
  126. 17: Linear Regression
  127. 17.1: Simple linear regression
  128. 17.2: Relationship between the slope and the correlation
  129. 17.3: Estimation of linear regression coefficient
  130. 17.4: OLS RMA and smoothing functions
  131. 17.5: Testing regression coefficients
  132. 17.6: ANCOVA - analysis of covariance
  133. 17.7: Regression model fit
  134. 17.8: Assumptions and model diagnostics for simple linear regression
  135. 18: Multiple Linear Regression
  136. 18.1: Multiple linear regression
  137. 18.2: Nonlinear regression
  138. 18.3: Logistic regression
  139. 18.4: Generalized Linear Squares
  140. 18.5: Selecting the best model
  141. 18.6: Compare two linear models
  142. 18.7: References and suggested readings
  143. 19: Distribution-free methods
  144. 19.1: Jackknife sampling
  145. 19.2: Bootstrap sampling
  146. 19.3: Monte Carlo methods
  147. 19.4: References and suggested reading
  148. 20: Additional Topics
  149. 20.01: Area under the curve
  150. 20.02: Peak detection
  151. 20.03: Baseline correction
  152. 20.04: Conducting surveys
  153. 20.05: Time series
  154. 20.06: Dimensional analysis
  155. 20.07: Estimating population size
  156. 20.08: Diversity indexes
  157. 20.09: Survival analysis
  158. 20.10: Growth equations and dose response calculations
  159. 20.11: Plot a Newick tree
  160. 20.12: Phylogenetically independent contrasts
  161. 20.13: How to get the distances from a distance tree
  162. 20.14: Binary classification
  163. Appendix
  164. A.01: Distribution tables
  165. A.02: Table of Z of standard normal probabilities
  166. A.03: Table of Chi-square critical values
  167. A.04: Table of critical values of Student's t-distribution
  168. A.05: Table of critical values of F-distribution
  169. A.06: Install R
  170. A.07: Install R Commander
  171. A.08: Use R in the cloud
  172. A.09: Jupyter notebook
  173. A.10: R packages
  174. A.11: List of R commands
  175. A.12: Free apps for bioinformatics