Supplements
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. R tips
• 1. Functions
• 1. Vectors and other objects
• 1. Variable assignment
• 1. R Packages
• 1. R Scripts
• 1. Getting started summary
2. Introduction to ggplot
• 2. A continuous variable
• 2. Saving a ggplot
• 2. Continuous by/categorical x
• 2. Two categorical variables
• 2. Two continuous variables
• 2. Many explanatory vars
• 2. ggplot summary
3. Reproducible Science
• 3. Collecting data
• 3. Storing data
• 3. Loading data
• 3. Check & prep data
• 3. Reproducible analyses
• 3. Reproducibility summary
4. Data in R
• 4. Checking Data Review
• 4. Modifying columns
• 4. Choose rows
• 4. Data in R: Code
• 4. Data in R summary
5. Simple Summaries
• 5. Summarizing shape
• 5. Changing shape
• 5. Summarizing the center
• 5. Summarizing variability
• 5. Summarizing Data: Code
• 5. Summarizing summary
6. Associations: Part I
• 6. Two categorical vars
• 6. Categorical + numeric
• 6. Associations I: Script
• 6. Association Summary: I
7. Associations: Part II
• 7. Revisiting two cats
• 7. Two numeric vars
• 7. Associations II: Script
• 7. Association II Summary
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Section II: Stats Foundations
8. Intro to Sampling
• 8. Sampling
• 8. Sampling Error
• 8. Sampling Bias
• 8. Non-independence
• 8. Sampling Better
• 8. Sampling summary
9. Uncertainty
• 9. Bootstrap
• 9. Confidence Intervals
• 9. Bootstrapping w/
infer
• 9. Gotchas
• 9. Bootstrap script
• 9. Uncertainty summary
10. Null Hypothesis Significance Testing
• 10. Statistical Hypotheses
• 10. P-Values
• 10. Statistical Significance
• 10. Considerations for NHST
• 10. NHST summary
11. Shuffling
• 11. The frogs
• 11. Permute
• 11. Structured Permutation
• 11. Shuffling summary
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Section III: Linear Models
12. Linear Models
• 12. The Mean as
lm(y ~ 1)
• 12. Residuals
• 12. Categorical predictor
• 12. Linear regression
• 12. Two predictors
• 12. Linear model summary
13. Normal distribution
• 13. Normal Introduction
• 13. Normal Properties
• 13. Normal Math
• 13. Is It Normal?
• 13. The Normal is Common
• 13. Make It Normal
• 13. Normal Summary
Optional: Simulating from the Normal Distribution
14. The t distribution
• 14. t: Example Data
• 14. Data summaries for t
• 14. Assumptions of t
• 14. Uncertain-t
• 14. One sample t-test
• 14. One sample t-test in R
• 14. The paired t-test
• 14. t Summary
15. Comparing two means
• 15. Visualizing two groups
• 15. Two-t Assumptions
• 15. Two-t Calculations
• 15. Uncertain-2t
• 15. Two-sample t-test
• 15. Two t Summary
16. F this!
• 16. F the ratio of variance
• 16. F Calculations
• 16. F and ANOVA in R
• 16.
F
-inding connec
t
ions
• 16. F the ratio of variance
• 16. F (ANOVA) summary
17. >2 Groups
• 17. Multiple testing problem
• 17. ANOVA is a linear model
• 17. ANOVA assumptions
• 17. Post hoc tests
• 17. Significance groups
• 17. R ANOVA pipeline
• 17. ANOVA summary
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Additional Resources
Supplements
• Supplement: Effect Sizes
References
Supplements
Look here!
Additional Resources
• Supplement: Effect Sizes