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Applied Biostatistics
Preface
Motivating biology and datasets
Types of Variables
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SECTION I: Intro to R
1. Getting started with R
• 1. Functions and vectors
• 1. Load packages and data
• 1. Data types in R
• 1. Orientation to RStudio
• 1. Getting started summary
2. Data in R
• 2. Adding columns w mutate
• 2. Selecting columns
• 2. Summarizing columns
• 2. Choose rows
• 2. Data in R summary
3. Introduction to ggplot
• 3. A continuous variable
• 3. Saving a ggplot
• 3. Continuous y/categorical x
• 3. Two categorical variables
• 3. Two continuous variables
• 3. Many explanatory vars
• 3. ggplot summary
4. Reproducible Science
• 4. Collecting data
• 4. Reproducible analyses
• 4. Reproducibility summary
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Section II: Summarizing data
5. Simple Summaries
• 5. Summarizing shape
• 5. Changing shape
• 5. Summarizing the center
• 5. Summarizing variability
• 5. Summarizing summary
6. Associations
• 6. Categorical + numeric
• 6. Two categorical vars
• 6. Two numeric vars
• 6. Association Summary
7. Linear Models
• 7. Mean
• 7. Categorical predictor
• 7. Linear regression
• 7. Two predictors
• 7. Linear model summary
8. Ordination
• 8. PCA quick start
• 8. PCA deeper dive
• 8. PCA – Gotchas
• 8. PCAlternatives
• 8. Ordination summary
9. Better Figures
• 9. Audience & Format
• 9. Honest plots
• 9. Transparent plots
• 9. Clear plots
• 9. Avoid Distractions
• 9. Accessible Plots
• 9. Writing about plots
• 9. Dataviz Summary
10. Better Figures in R
• 10. Tools for BetteR plots
• 10. Making cleaR plots
• 10. Plots for the medium
• 10. Better ggplots summary
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Section III: Stats Foundations
11. Intro to Sampling
• 11. Sampling
• 11. Sampling Error
• 11. Sampling Bias
• 11. Non-independence
• 11. Sampling Better
• 11. Sampling summary
12. Uncertainty
• 12. Bootstrap
• 12. Confidence Intervals
• 12. Bootstrapping w/
infer
• 12. Gotchas
• 12. Uncertainty summary
13. Null Hypothesis Significance Testing
• 13. Statistical Hypotheses
• 13. P Values
• 13. Statistical Significance
• 13. Considerations for NHST
• 13. NHST summary
14. Shuffling
• 14. The frogs
• 14. Permute
• 14. Structured Permutation
• 14. Shuffling summary
14-B. χ2
• 14B. What to expect
• 14B.
\(\chi^2\)
Two Cats
• 14B.
\(\chi^2\)
distribution
• 14B.
\(\chi^2\)
summary
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Section IV: Stats for Linear Models
15. Normal distribution
• 15. Normal Introduction
• 15. Normal Properties
• 15. Normal Simulations
• 15. Normal Math
• 15. Is It Normal?
• 15. The Normal is Common
• 15. Make It Normal
• 15. Normal Summary
16. The t distribution
• 16. t: Example Data
• 16. Data summaries for t
• 16. Assumptions of t
• 16. Uncertain-t
• 16. One sample t-test
• 16. One sample t-test in R
• 16. The “Paired” t-test
• 16. t Summary
17. A binary explanatory variable
• 17. Two-t Assumptions
• 17. Two-t Calculations
• 17. Uncertain-2t
• 17. Two-sample t-test
• 17. Two t Summary
18. F this!
• 18. F the ratio of variance
• 18. F Calculations
• 18. F and anova in R
• 18. F-inding connecTions
• 18. F (ANOVA) summary
19. >2 Categories
• 19. Multiple testing problem
• 19. ANOVA: a linear model
• 19. ANOVA assumptions
• 19. ANOVA Example
• 19. Post hoc tests
• 19. Significance groups
• 19. R ANOVA pipeline
• 19. ANOVA summary
20. Linear Regression
21. Two predictors
22. Multiple Regression
23. Interactions
24. Model Evaluation
25. Study Design
26. Cause
27. Probability and Likelihood
References
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Types of Variables
SECTION I: Intro to R